esa-sceva/satcom-chunk-collection huggingface dataset:
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 3 Multi-Feature Fusion Neural Networks  
Numerous scholars have utilized CNNs to extract fetal ECG signals from pregnant women [33; 34; 35; 35]. However, these algorithms exhibit certain limitations. Firstly, although CNNs can autonomously learn effective features from raw ECG data, these features often need more interpretability. Moreover, some essential features that can be easily extracted manually are challenging to learn through CNNs. Secondly, scholars tend to employ multiple layers of complex neural networks to enhance the network's recognition accuracy, resulting in slower computational speeds and higher demands on hardware resources, thereby limiting the practical application of these advancements.  
Therefore, developing multi-feature fusion neural networks that are both highly accurate and cost-efficient in computation holds significant importance. Such networks are better equipped to adapt to data distributions and task requirements. Common multimodal fusion strategies are primarily categorized into early fusion, late fusion, and multi-stage fusion. Early fusion refers to integrating data from different modalities into a single feature representation during the initial feature extraction phase. Late fusion involves the integration of results after features from different modalities have been extracted and processed independently. Multi-stage fusion strategies integrate information from multiplemodalities at various stages. This paper designs three distinct model fusion networks and compares the accuracy and merits of three fetal ECG classification algorithms that fuse features across multiple domains.
# Deep Anomaly Detection Based on Variational Deviation Network
## 2 Related Work
### Deep Anomaly Detection  
At present, after the extensive application of deep learning, people have also begun to study the application of deep learning to various abnormal tasks, and have achieved great success such as autoencoders, which train encoders and decoders on normal data. During anomaly detection, if the vector from the decoder is very different from the original vector, it is considered abnormal data. There is also a GAN network, which is mainly composed of a generation network and a discrimination network. It is also a learning reconstruction error. If it is very different from the original vector, it is judged as abnormal data. This type of method solves, to a certain extent, the challenge that traditional methods (random projection [20]) cannot learn high latitude data at high latitudes. This type of algorithm can learn the complex relationships between each feature at high latitudes as well as important features. However, the accuracy of subsequent individual anomaly detection work is not good [5; 6], so in recent research, in order to solve this problem, the REPEN [5] algorithm (algorithm for learning representations of ultrahigh-dimensional data for random distance-based outlier) and the Deep-SVDD [6] algorithm (algorithm for deep support vector data description) and DevNet [16] algorithm (algorithm for deep anomaly detection with deviation networks) have been proposed successively. The REPEN algorithm combines feature learning representation with distance-based methods, while Deep-SVDD mainly learns the single-class feature representation of SVDD (support vector data description) [21]; this type of algorithm mainly learns the feature representation of high-dimensional data where sometimes it cannot fully represent the characteristics of high-dimensional data and is unexplainable. The DevNet algorithm implements an end-to-end abnormal scoring network, eliminating the need for intermediate feature learning, but the reference score of normal data in this method is randomly generated for a special dataset, so a valid reference score cannot be obtained, which affects the accuracy. The variational deviation network proposed in this paper mainly learns the normal distribution of each normal data, and calculates the normal distribution of the overall normal data. The deviation loss function is then used to guide the optimization of the abnormal score network.  
Although the current mainstream deep anomaly detection solves the feature training under high-latitude data, their limitations lie in their uninterpretability and the fact that high-latitude feature learning ignores its detailed features to reduce accuracy; although the DevNet algorithm solves the uninterpretability of the model to a certain extent, its limitation lies in the fact that the parameters are randomly generated and cannot fit the reference scores of the normal data of the data itself.
## 4 System Model and Description of the Proposed Protocols  
This section includes the system model of the proposed protocols in detail. In this work, two proactive routing protocols, namely: EP-VIR-Three and BF-SPR are proposed to remove the problem of void hole in IoT-UWSN. The protocol EP-VIR-Three discovers the complete route by selecting the next potential forwarder by checking the 3-hop neighbor's availability of the next forwarder node. EP-VIR-Three selects those forwarder nodes which have minimum neighbor nodes. The protocol BF-SPR-Three minimizes the void hole probability using proactive and efficient path selection (using 'Bellman-Ford' (BF) algorithm) with 3-hop neighbor's information. The detailed discussion is given below.
## 3 Results and Discussions
### DSC Cooling and Heating Behavior of the Composites  
The DSC cooling and subsequent melting curves at the cooling and heating rates of 10 \({}^{\circ}\)C/min are shown in Figure 3, and the corresponding onset, endset and peak temperatures of crystallization (\(T_{onset}\), \(T_{condset}\) and \(T_{c}\)); the peak, onset, and endset temperatures of melting (\(T_{m}\), \(T_{monset}\) and \(T_{mendset}\)); and the relative degree of crystallinity \(X_{\rm c}\) are shown in Table 2.  
As can be seen from Figure 3 and Table 2, compared with PP, the \(T_{c}\), \(T_{onset}\), and \(T_{condset}\) on the crystallization curve; the \(T_{m}\), \(T_{monset}\), and \(T_{mendset}\) on the melting curve; and the relative degree of crystallinity \(X_{\rm c}\) of GPP increase evidently, indicating an enhancement of nucleation rate and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Cooling Scan**} & \multicolumn{4}{c}{**Subsequent Heating Scan**} \\ \cline{2-9}
**Sample** & \(T_{c}\) & \(T_{onset}\) & \(T_{condset}\) & Width & \(T_{m}\) & \(T_{onset}\) & \(T_{mendset}\) & \(X_{c}\) \\  & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\%)** \\ \hline PP & 111.7 & 118.5 & 106.5 & 12.0 & 161.5 & 153.1 & 166.0 & 48.6 \\ GPP & 115.4 & 121.0 & 110.7 & 10.3 & 163.6 & 153.9 & 167.2 & 49.5 \\ GHPP & 118.5 & 122.6 & 114.4 & 8.2 & 164.8 & 155.2 & 168.7 & 50.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crystallization and melting parameters of polypropylene (PP), iPP/GO (GPP), and iPP/GO-H202 (GHPP).  
Figure 3: **(a)** Differential scanning calorimetry (DSC) cooling curves and (b) subsequent heating curves of the samples. The cooling and subsequent heating rates were 10 \({}^{\circ}\)C/min.  
Figure 2: TEM images of **(a)** GO and **(b)** GO-H202.  
crystallizability of the composites after the addition of GO. Moreover, the lamellar thickness is enhanced as well. Interestingly, after the addition of GO-H202 (GHPP), these parameters further increase compared with GO, revealing the higher crystallization acceleration effect of GO-H202.
## 1 Introduction  
Sunflower (_Helianthus annuus_ L.) is an economically important oil-bearing plant [1; 2]. Sunflower seeds are mainly used for the production of vegetable oil. The process includes the generation of sunflower meal as a by-product in quantities that approximate 36% of the weight of the processed seeds [3]. Currently, sunflower meal is used as a protein-rich ingredient in feed formulation. The application of this byproduct in animal nutrition, however, is limited by the presence of some anti-nutrients [4] and poorly digestible fibers [5]. Alternatively, sunflower meal can be used for human consumption by either direct inclusion as an ingredient of food products [6] or after transformation into value-added compounds such as proteins, antioxidants, phytochemicals, and dietary fiber [7]. The efficient utilization of byproducts or food processing organic waste is emerging as a rapidly developing economically and socially important sector of human life in response to the worldwide need for efficient utilization of natural resources and gentle care to the environment [4].  
In recent years, there has been an increasing interest in sunflower meal protein isolates as an alternative to animal proteins currently used in the food industry [6]. They have high nutritive quality and attractive functional characteristics. A major challenge to the preparation of these products is the presence of phenols that may remain inbound in the final product and negatively affect their color, taste, and functionality. According to [PERSON] et al. [8], pre-treatment of the meal aiming at reduction of anti-nutrients such as phenols may improve the quality of the oil-bearing meal-derived protein isolates. Being focused on the biorefinery of rapeseed and sunflower meals, [PERSON] et al. [9] demonstrated that for extracting phenols, mixtures of water and ethanol could be efficiently used instead of methanol. [PERSON] et al. [10] achieved a 4-fold reduction of phenols in industrial rapeseed meal after pre-treatment of the byproduct with 75% aqueous ethanol solution before using it as a source for preparation of protein isolates with enhanced solubility [11]. To increase the profitability and usability of the rapeseed meal, the ethanol-wash liquids were collected, vacuum-concentrated, and lyophilized. The resulting powder exhibited bioactive capacity, including antioxidant and antifungal activity, which outlined it as a prospective agent for food biopreservation or plant bioprotection [12].  
Following a similar approach, a powdery sunflower meal ethanol-wash solute (SEWS), remaining as a waste of the main process of protein isolation from sunflower meal, was prepared in our laboratory [13]. The SEWS appeared as a surfactant with good solubility in water. The current research is a continuation of the previous one aiming at the further evaluation of SEWS' applicability potential. The phytochemical profile, antioxidant and antimicrobial capacity of the SEWS were investigated to evaluate its suitability in agriculture, food, or nutraceutical industries.
# Virtualized MME Design for IoT Support in 5G Systems
## 3 System Model
### The User Equipment  
UEs are the terminals that allow each human user to connect to the network via the eNodeB base stations. The UEs run the users' applications, which generate or consume network traffic (see the traffic model in Section 4). If a UE is in the idle state and it has data to transmit, it carries out a Service Request (SR) procedure to establish a bearer, which will convey the data packets. We assume that UEs move following a fluid-flow mobility model. When a UE crosses the border between two cells, it triggers an X2-based Handover (HO) procedure.
# Aerodynamic Optimization and Characterization of a Ducted Tail for a Box-Launched Aircraft
## 4 Aerodynamic Optimization  
### General Optimization Framework  
The general framework of ducted tail aerodynamic optimization is shown in Figure 4, which starts with parametric modeling and computational meshing of the ducted tail and fuselage combination of the aircraft, followed by design of experiments using optimized Latin-square sampling and using the acquired sample model lift-drag characteristics as the training parameter set. The SSA-BP approximate model is utilized to predict the lift-drag characteristics and generate the full sample space. Then, a multi-objective genetic algorithm is utilized to find the Pareto frontier solution in the full sample space and select the relative optimal solution from it. Finally, the optimal solutions in the previous step are refined and the optimization results are verified.  
Especially for step 5 in Figure 4, since the traditional CFD calculation method consumes a large amount of computation time, an approximate modeling approach is needed to quickly obtain the aerodynamic characteristics of the aircraft under different geometric parameters. The approximate model proposed in this paper is based on the BP neural network and incorporates the SSA parameter optimization module to generate an SSA-BP high-precision approximate model, which solves the overfitting and local distortion problems of the BP neural network approximate model. The specific principles of the SSA-BP approximate model are detailed in Section 4.3.  
Figure 3: Meshes—independent and computational accuracy validation. (**a**) Meshes—independent validation; (**b**) calculation accuracy validation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Number of** & **Number of** & **Number of** \\  & **Coarse Meshes** & **Medium Meshes** & **Refined Meshes** \\ \hline Lift coefficient of the & & & \\ ducted tail & 0.1885 & 0.1958 & 0.1975 \\ Drag coefficient of the & & & \\ ducted tail & 0.0109 & 0.0113 & 0.0148 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulation results using different computational meshes.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
###### Abstract  
Wind-power generation is one of the fundamental sources of renewable energy. However, due to the increasing size of wind turbines, they cause unwanted interference with radar systems for civic protection, especially for on-shore locations. This paper presents parameter studies performed on different wind-turbine models, with a focus on differences of the aerodynamical shape of the rotor blades. Numerical simulation is employed to estimate the influence of different wind-turbine design parameters, with the aim of deriving strategies to minimize wind-turbine influence on radar systems for civic protection. Due to the complex nature of the aerodynamic shape of the blade, a general model cannot be derived from the studies. However, further steps to eventually achieve this goal are outlined.  
radar cross section; wind turbines; numerical simulation +
Footnote †: journal: Applied Sciences
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### The Influence of the Elastic Modulus of Cement Sheath on Casing
#### 3.3.2 The Influence Law of Elastic Modulus of Cement Sheath on Casing Eccentricity  
Based on the specific values provided, it can be observed that with a fixed elastic modulus of the cement sheath at 25 GPa, there is a tendency for higher casing stress values as the eccentricity increases. Among them, radial stress and shear stress exhibit the most significant changes, and they are highly sensitive to eccentricity. As the eccentricity increases, the radial stress and shear stress exhibit the largest increments and the highest sensitivity to eccentricity. Additionally, the elastic modulus of the cement sheath also has a certain influence on the casing stress distribution at different eccentricities. When the eccentricity is fixed, a lower elastic modulus of the cement sheath will result in lower casing stress values, while an increase in the elastic modulus of the cement sheath leads to an increase in casing stress values.
# Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection
## 5 Results and Discussion
### Performance Comparison with and without Optimization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{3}{c}{**Conventional Method**} & \multicolumn{3}{c}{**Target Guidance Method**} \\ \cline{2-7}  & **CAV** & **Tram** & **Weighted Total** & **CAV** & **Tram** & **Weighted Total** \\ \hline Transit time of passengers (s) & 13,225.1 & 3849.6 & 7599.8 & 7335.6 & 5614.0 & 6302.6 \\ Energy consumption (kJ) & 13,950.12 & 7414.54 & 21,364.66 & 11,902.01 & 2052.28 & 13,954.29 \\ Discomfort value & 0.29 & 0.32 & 0.31 & 0.27 & 0.18 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of indicators from two different strategies. Figure 6: Comparison of connected vehicle trajectories. (**a**) The second phase. (**b**) The third phase. (**c**) The first phase of the next cycle. Figure 7: Trajectories of tram and connected vehicles. (**a**) Conventional method. (**b**) Target guidance method. obtained through optimization of the model presented a zigzag pattern of first falling, then rising and finally falling. The reason for this is that, when the number of tram passengers is small and the number of social vehicle passengers is large, the priority given to the tram by the conventional method will have a greater negative impact on many social vehicle passengers. With the increase in the proportion of tram passengers, an advantage in priority gradually appears. Through the optimization of the target guidance method designed in this paper, when the ratio does not reach 1, the optimized TSP type is the phase insertion scheme only at the early stage of the third phase. In this case, the energy consumption and comfort indices of the tram and CAVs are low, and the passing time of the tram passengers is longer. When the ratio is greater than 1 and less than 6, the phase insertion scheme that only occurs in the third phase cannot reduce the transit time of tram passengers; therefore, the TSP changes into the phase insertion scheme that occupies the second phase and the third phase at the same time. With an increase in the ratio, occupation of the second phase increases. When the ratio is 6 to 8, a large increase is mainly caused by the change in the TSP from the phase insertion to green extension scheme. When the ratio is above 8, the TSP has little influence on social vehicles and, thus, the optimization results are in accordance with the scheme obtained by the conventional method, in which a part of the energy consumption and comfort index are sacrificed to reduce the transit time of many tram passengers. The optimized scheme obtained by the target guidance method proposed in this paper can adapt to scenarios with different passenger proportions and has good applicability.
## 3 Characteristic Analysis  
### Plane Strain Deformation  
Multiplying the first equation in (7) by \(\cos 2\psi\), the second by \(\sin 2\psi\) and summing gives  
\[\bigg{(}1-\frac{\cos 2\psi}{\sin\phi}\bigg{)}\frac{\partial q}{\partial x_{1}}- \frac{\sin 2\psi}{\sin\phi}\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial q}{\partial x_{2}}+2q\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial\psi}{\partial x_{2}}=0. \tag{19}\]  
Multiplying the first equation in (7) by \(\sin 2\psi\), the second by \(-\cos 2\psi\) and summing gives  
\[\frac{\sin 2\psi}{\sin\phi}\frac{\partial q}{\partial x_{1}}-\bigg{(}1+\frac{ \cos 2\psi}{\sin\phi}\bigg{)}\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial q}{\partial x_{2}}+2q\frac{\partial\psi}{\partial x_{1}}=\frac{2q}{R( x_{1})}. \tag{20}\]  
Figure 4: Mohr–Coulomb yield locus under axially symmetric conditions.  
Assume that S - curve in Figure 1 is a characteristic curve. Then, \(\psi=\pm(\pi/4+\phi/2)\) on this curve [14]. In particular, \(\partial\psi/\partial x_{1}=0\). Equation (20) supplies the characteristic relation in the form  
\[\pm\cot\phi\frac{\partial q}{\partial x_{1}}-\frac{2q}{R(x_{1})}=0. \tag{21}\]  
In the proceeding, it is assumed that S - curve in Figure 1 is an envelope of characteristics. Therefore, Equation (21) is not satisfied even though \(\psi=\pm(\pi/4+\phi/2)\) on this curve. It is possible only if  
\[\left|\frac{\partial q}{\partial x_{2}}\right|\to\infty \tag{22}\]  
as \(x_{2}\to 0\). In this case, the second term in (20) reduces to the expression \(0\cdot\infty\) at \(x_{2}=0\).
# A Lightweight Barcode Detection Algorithm Based on Deep Learning
## 3 Method
### Lightweight Detection Head  
In the field of object detection, there are mainly two types of detection heads: anchor-free and anchor-based. The anchor-based detection head can generate dense anchor boxes, enabling the network to directly classify objects and perform bounding box regression,  
Figure 5: C2f-Ghost module (**left**), ghost module (**middle**), and ADown module (**right**).  
thereby enhancing the model's recall rate. However, this method also leads to the generation of numerous redundant anchor boxes and requires careful setting of several hyperparameters during model training, which increases the model's computational load and memory consumption. Based on this, this paper opts for an anchor-free detection head. To further reduce the number of parameters and computational complexity of the decoupled head structure, modifications are made to the detection head: two grouped convolutions are utilized at the head position to achieve parameter sharing, and then the target location and category information are separately extracted to form the decoupled head. The improved detection head boasts fewer parameters, lower computational complexity, and higher parameter utilization efficiency. The specific structure is illustrated in Figure 6.  
To mitigate the adverse effects of sample imbalance during the model training phase and improve training quality by treating easy and hard samples differently, this paper selects Focaler-CIoU [24] as the loss function for the model. Given \([u,d]\in[0,1]\), the value of \(IoU^{Focaler}\) can be adjusted through modifying \(u\) and \(d\) to focus on different samples. Since the true distribution of target barcodes typically does not deviate significantly from their annotated positions, the model should prioritize values near the annotated positions during training. Therefore, Distribution Focal Loss (DFL) [25] is chosen to be paired with the Focaler-CIoU loss function, which together act on the bounding box regression of the barcode detection model presented in this paper. The calculations for the Focaler-CIoU and DFL loss functions are provided in Equations (4), (5), and (6), respectively.  
\[L_{Focaler-CIoU}=L_{CIoU}+IoU-IoU^{Focaler}=1+\frac{\rho^{2}(b,bst ^{t})}{c^{2}}+\alpha v-IoU^{Focaler}, \tag{4}\] \[IoU=\frac{\alpha\cap B}{\alpha\cup B},\alpha=\frac{v}{(1-IoU)+v},v=\frac{4}{\pi^{2}}\Big{(}\arctan\frac{\underline{w}^{gt}}{hs^{gt}}-\arctan \frac{\underline{w}}{h}\Big{)}^{2},\] (5) \[IoU^{Focaler}=\left\{\begin{array}{ll}0,&IoU<d\\ \frac{IoU-d}{u-d},&d\ll IoU\ll u,\\ 1,&IoU>u\end{array}\right.\]  
where, \(A\) and \(B\) are the area occupied by the prediction box and the real box, respectively, \(b\) and \(bst^{t}\) are the coordinates of the center point of the prediction box and the real box, respectively, \(\rho(\bullet)\) is the Euclidean distance, \(c\)