esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Classification Complementarity: Combination of Clinical and RFI Data  
We evaluated the use of clinical data using the classifiers defined in Section 2.2 on the same patients dataset, and following a 5-Fold Cross Validation approach. Table 4 depicts the results for the variables age (ranging from 45 to 76 years old, mean = 65.75) and previous diagnosis of diabetic retinopathy. We also used the combination (concatenation) of both. Results show significantly better than chance accuracies in predicting the CAC \(>\) 400 label, especially when the combination of variables is used.  
We further explored the predicted labels for both the image and the clinical data classifiers, and we found that individual predictions differ considerably between modalities. This suggest that an ensemble of both informational cues could take benefit from the complementarity in the predictions. Particularly, we propose two settings that target two specific applications:  
* Clinical Diagnosis: we follow a conservative prediction protocol that combines both modalities to reduce the False Negative ratio, i.e., the likelihood of having positive patients diagnosed as negative. This application is conceived for healthcare systems where resources availability is not constrained. After analyzing the retinography, this procedure may suggest further clinical tests (heart scan) for uncertain CAC positive predictions even though the patient probability of having \(CAC<400\) is still considerable.
* Large Scale Retrieval: where we give priority to improve the precision of the ensemble. The goal in this case is to build and application that may search in large databases, and may send to screen the patients with CAC \(>\) 400. As the resources are limited, only the patients with high certainty of having CAC \(>\) 400 should be further screened. Contrary to the first application, it is designed for efficient resource management and prioritize patients that urgently need further clinical screening.
## 3 Model Parameter Acquisition
### Analog Programming  
From the production data of the gas well, it is known that the original formation pressure of the well is about 20 MPa. In the simulation process, firstly, the tubing was lowered to the inclination point at 2200 m and the formation pressure was set to drop from 20 MPa to simulate the production dynamics of the gas well when the tubing was lowered to the inclination point under different formation pressure conditions. Subsequently, the tubing depth was changed to simulate the production dynamics of gas wells under different tubing depths and formation pressures, and the critical formation pressure that can maintain gas wells under different conditions was obtained. The specific simulation scheme is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Bottom** & & **Bottom** & & **Bottom** \\
**Tubing** & **Hole** & **Tubing** & **Hole** & & 7.8 \\
**Depth (m)** & **Flowing** & **Depth (m)** & **Flowing** & **Tubing** & **Flowing** \\  & **Pressure** & **Pressure** & **Pressure** & **Pressure** \\  & **(MPa)** & & **(MPa)** & & **(MPa)** \\ \hline  & 9 & & 9 & 9 \\  & 8 & & 8 & 8 \\  & 7.8 & & 7.8 & 7.8 \\
2200 & 7.7 & & 7.7 & 7.7 \\  & 7.6 & & 7.6 & 7.6 \\  & 7.4 & & 7.4 & 7.4 \\  & 7 & & 7 & 7 \\  & 6 & & 6 & 6 \\  & 9 & & 9 & 9 \\  & 8 & & 8 & 8 \\  & 7.8 & & 7.8 & 7.8 \\  & 7.7 & & 7.7 & 7.7 \\
2800 & 7.6 & 3000 & 7.6 & 3200 & 7.6 \\  & 7.4 & & 7.4 & 7.4 \\  & 7 & & 7 & 7 \\  & 6 & & 6 & 6 \\ \hline \end{tabular}
\end{table}
Table 6: Simulation scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pressure** & **Gas Compression** & **Pressure** & **Gas Compression** \\
**(MPa)** & **Factor** & **(MPa)** & **Factor** \\ \hline
50 & 1.1921 & 24 & 0.9488 \\
48 & 1.1697 & 22 & 0.9383 \\
46 & 1.1477 & 20 & 0.9299 \\
44 & 1.1260 & 18 & 0.9241 \\
42 & 1.1047 & 16 & 0.9210 \\
40 & 1.0839 & 14 & 0.9208 \\
38 & 1.0637 & 12 & 0.9236 \\
36
## 4 Discussion  
### Gesture Recognition and User Interaction  
This study demonstrates the successful integration of gesture-based drone control, autonomous obstacle avoidance, and 3D mapping, providing a more accessible and intuitive method for drone navigation. The hand gesture control system, implemented using Google's MediaPipe Hands, accurately recognized predefined gestures and converted them into flight commands. However, lighting conditions, hand positioning, and background noise affected recognition accuracy, a limitation observed in prior research on vision-based gesture interfaces [30]. Despite this, the system provided hands-free control, reducing the need for traditional controllers and lowering the barrier to drone operation, aligning with previous studies that highlight the benefits of gesture-based interfaces for robotics and UAV applications [31].  
A key aspect of this research was the integration of gesture control with autonomous navigation, allowing users to interact with the drone while ensuring safety through automatic collision avoidance. Studies on human-robot interaction suggest that balancing user input with autonomous decision-making is critical to achieving an effective shared control system [32]. Future work could focus on refining the balance between manual input and autonomous intervention, ensuring that the system adapts dynamically to user intentions while maintaining safety protocols. One possible approach is to use reinforcement learning or adaptive AI models to adjust the system's response based on feedback [33].
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 2 Novel Discrete Lattice Element Model
### Stress Tensor Computation and Fictitious Forces  
#### 2.2.1 Stress Tensor Computation  
The procedure for correcting the model linear elastic response is based on nodal stresses, as proposed in [8]. The nodal stress state is computed from the equilibrium of each particle/node. In order to obtain the stress tensor, we will consider (1) local coordinate systems of each beam element giving rise to longitudinal and transversal forces (Figure 4a) and (2) a virtual cut of each cell (Figure 4b). The longitudinal and transversal element forces \(N\) and \(T\) of all \(M\) lattice elements related to the observed node are considered in the stress tensor computation (Figure 4a).  
The total stress resultant vector acting in the section defined by \(\alpha\) is computed by considering all \(M\) elements related to the node as  
\[\begin{array}{l}F_{n\alpha}=\sum\limits_{m=1}^{M}w\;[N_{m}\text{sin}(\beta- \alpha)+T_{m}\text{cos}(\beta-\alpha)]\\ F_{\alpha\alpha}=\sum\limits_{m=1}^{M}w\;[N_{m}\text{cos}(\beta-\alpha)-T_{m} \text{sin}(\beta-\alpha)],\end{array} \tag{5}\]  
where the contribution of the stress resultant from every lattice element is multiplied by a weight defined by the ratio \(w=A_{m}^{\prime}\) / \(A_{m}\) (Figure 4b). The stress components on the section \(A_{\alpha}\) can be computed as  
\[\begin{array}{l}\sigma_{n\alpha}=F_{n\alpha}/A_{\alpha}\\ \sigma_{t\alpha}=F_{t\alpha}/A_{\alpha},\end{array} \tag{6}\]  
The stress tensor can be defined by computing the components in two perpendicular sections obtained by positioning the section with \(\alpha=0^{\circ}\) (Figure 5a) and \(\alpha=90^{\circ}\) (Figure 5b).  
By taking the total stress resultant vector on the cut planes, we can compute the stress components \(\sigma_{XX}=F_{n\alpha}/A_{\alpha}\) and \(\tau_{XY}=F_{t\alpha}/A_{\alpha}\) for \(\alpha=90^{\circ}\); \(\tau_{YX}=F_{t\alpha}/A_{\alpha}\) and \(\sigma_{YY}=F_{n\alpha}/A_{\alpha}\) for \(\alpha=0^{\circ}\). The corresponding nodal stress tensor is symmetric since \(\tau_{XY}=\tau_{YX}\)  
\[\sigma=\begin{bmatrix}\sigma_{XX}&\tau_{XY}\\ \tau_{YX}&\sigma_{YY}\end{bmatrix} \tag{7}\]  
where \(X\) and \(Y\) refer to the global coordinate axes.  
Figure 4: (a) Longitudinal and transversal lattice element forces contributing to stress computation; (b) total stress resultant vector acting in the section defined by \(\alpha\).
## 3 Results and Discussion
### Analysis of Strain Characteristics
#### 3.4.2 Effects of Antibiotics and Heavy Metals on the Growth of Q3-6  
Studying the ability of strain Q3-6 to survive under different growth conditions is helpful to determine the type of sewage they can bioremediate, especially for water with high antibiotic and heavy metal contents. Printing and dyeing sewage also contains an amount of heavy metal ions.  
We selected some commonly used antibiotics to assess the resistance of strain Q3-6. The reference standards for the antimicrobial range of the disk method for antibiotic susceptibility testing of resistant bacteria are shown in Table S3.  
According to the Performance Standards for Antimicrobial Susceptibility Testing issued by the Clinical and Laboratory Standards Institute (CLSI) of the USA [61], Table S4 lists the resistance of strain Q3-6 to different antibiotics. Strain Q3-6 is resistant to polymycin B, Iincomycin, ceftriaxone, Cefotaxime Sodium, ampicillin, penicillin, and carbenicillin; moreover, it is intermediate to kanamycin. Several studies have shown that antibiotic contamination, especially ceftriaxone [62], penicillin [63], and Iincomycin [64], is present in several regions of the world. The resistance of strain Q3-6 to many antibiotics can ensure that it can survive in antibiotic-containing sewage and continue to play the role of sewage treatment.  
Printing and dyeing sewage contain amounts of heavy metal ions (e.g., Zn (II), Cu (II), Cr (III), Ni (II), Cd (II), etc.). The experimental data (Table 3) show that strain Q3-6 was resistant to several heavy metals, such as Pb (II), Ni (II), and Zn (II), with the highest tolerance to Pb (II), with MIC up to 1500 mg/L. The best removal of Zn (II) by _Bacillus cereus_ was also found in the study of [PERSON] et al. [65]. [PERSON] et al. [66] reported the mechanism of detoxification of various heavy metals by _Bacillus_. This result helps to investigate whether strain Q3-6 can survive and degrade organic pollutants in printing and dyeing effluents with a high content of heavy metal ions, and even adsorb and remove heavy metal ions to some extent.
###### Abstract  
The correct assessment of the vertical jump height depends on an accurate and reliable measurement tool. This study aimed to determine the concurrent validity and reliability of the My Jump 2 app used for estimating the maximum height (MH) of the counter-movement jump (CM)). Twenty-one male adults participated in this study. The MH of the CMJ was estimated based on the displacement of the jumper's center of mass (force platform), the displacement time of the reflective marker placed on the jumper's sacrum (smartphone, My Jump 2-DT) and the flight time of the jumper (smartphone, My Jump 2-FT). The assessment of the concurrent validity showed a poor agreement (ICC = 0.362; Bland-Altman bias = 12.4 cm) between the My Jump 2-FT and force platform (\(p<0.001\)), and a good agreement (ICC = 0.858; Bland-Altman bias = \(-0.2\) cm) between the My Jump 2-DT and force platform (\(p<0.001\)). The ICC values for internal consistency (\(>\)0.9) indicated the excellent reliability of all measurement tools (\(p<0.001\)). The findings revealed the high accuracy and good reliability of the My Jump 2 app for the new method of MH estimation for the CMJ, including the displacement time of the marker placed on the jumper's sacrum.  
validity; jump height; counter-movement jump; My Jump 2 app; force platform Article  
Validity and Reliability of a Smartphone App for Vertical  
Height Assessment Using the Marker Displacement Time Method
# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
The simulation-based approach presented in this article enables precise and cost-effective verification of a swarm control concept that utilizes the leader drone mechanism. The assumptions of the simulation environment were described in our earlier article [22]. In the proposed concept of swarm drone control, each drone is equipped with a set of sensors and a GPS that enables the collection of environment and location data. The drones communicate autonomously, exchanging information about their status, planned routes, and detecting obstacles. Such an approach enables them to avoid collisions, coordinate actions in group tasks, and dynamically adapt to variable conditions. To achieve maximum positioning precision, real-time kinematic (RTK) positioning is used. As a result, the drones in a swarm maintain very small distances between each other, precisely execute tasks that require an accurate location, and maintain the given flight structure of the swarm. The research considered the impact of GPS positioning error, communication delays, and two methods of maintaining drone swarm structure. The examined communication delays could also simulate the data processing time by sensors. The research results presented in the article can serve as the foundation for the development of a specialized communication system for exchanging information between autonomous drone swarms and a collision avoidance system for the swarm within its surrounding external environment. The article in [23] presented experiments very similar to ours. However, the authors of the article assumed large distances between drones. In the case of large distances between drones, the communication delays between drones or positioning accuracy do not affect drone collisions. When the distance between drones in the swarm decreases, the impact of distance and communication delays on their collisions increases. The remainder of this paper is organized as follows: Section 2 describes the methods for controlling drone swarms, as well as the mechanism based on the leader drone. Section 3 presents the simulation environment. Section 4 describes the results of the conducted experiments. Section 5 presents the conclusions drawn from the experiments and future work.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions
### Gain  
The gain comparison of the proposed antenna for both simulated and measured values for the complete working region is depicted in Figure 6. The antenna has a simulated gain of more than 2 dBi in the resonating bandwidth, but it begins to decrease in the notch band,  
Figure 4: Proposed extended UWB antenna’s (**a**) prototype and (**b**) antenna under test.  
Figure 5: Measured and simulated \(\ket{S_{11}}\) of the proposed notch band extended UWB antenna.  
reaching a low of \(-3.9\) dB at 5.5 GHz. The measured value of the gain also depicts similar results for both resonating and notch bandwidths. The antenna gain and far-field radiation characteristics are measured in an anechoic facility. A well-calibrated regular gain horn antenna was placed in the anechoic facility and utilized as a transmit antenna, whereas the prototype antenna acts as a receiver antenna. Low noise amplifiers were utilized for the provision of constant power reception. The antenna was rotated at a number of angles to measure the results at various configurations.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 3 Beam Prediction Access Scheme  
The key processing elements of the proposed BRNN-LSTM deep learning model for beam prediction are now presented. First, unidirectional RNN updates hidden layers based upon information received from the input layer as well as the activation state. However, a limitation of unidirectional RNN is learning from past only. Hence, a bidirectional approach is adopted in this work to improve RNN. In this merge, one direction learns the past state, whereas the other learns the future state, then the two outputs are combined for an enhanced estimate. Therefore, the bidirectional feature enables LSTM to train each input sequence in disjoint forwards and backward states that are subsequently connected to the same output layer. This strengthens LSTM to retrieve additional beam index contextual information as compared to the conventional LSTM method. The process at the backward and forward states are similar at each of the bidirectional units.  
Another limitation for RNN networks during the training process is the gradient vanishing problem for long data sequences. Hence LSTM networks are adopted to solve this problem by introducing memory blocks (units) that are comprised of self-connected memory cells and multiplicative gate, thus enabling the learning of long term dependencies. Therefore, the work here combines the saliences of past/future (backward and forward) state information at the BRNN with the powerful memory blocks for extended training periods and more information in LSTM, to improve the quality and accuracy of the beam prediction problem. Furthermore, four BRNN-LSTM layers are stacked (chained) to achieve higher precision. The proposed BRNN-LSTM method has three phases, i.e., input, hidden layers and output, where each hidden layer is represented by a bidirectional LSTM cell. Along these lines, the prediction scheme combines BRNN and LSTM to achieve a suitable solution for time-series prediction of variable sequences lengths, i.e., duplicating the training on the input sequences (information from dataset) by leveraging forward and backward states. The architecture for the proposed scheme is presented next.
# Resolution Enhancement of Spherical Wave-Based Holographic Stereogram with Large Depth Range +
## 6 Conclusions  
In this paper, the resolution of RPHS was analyzed in a geometrical imaging model. Three parameters, capturing depth, pixel size of EI and focal length of the lens array, mainly determine the resolution. Firstly, reducing capturing depth is always beneficial to the resolution enhancement near the CDP. To avoid decreased depth range, a proper capturing depth needs to be chosen through a numerical method. Secondly, reducing pixel size of EI is always effective for the resolution enhancement near the CDP. However, the increased hopel size will cause wider light rays, making larger defocused spots away from the CDP. A partial aperture filtering technique is proposed to enhance resolution within the total depth range. Lastly, increasing focal length of the lens array will always improve the reconstruction quality near the CDP. However, the sizes of EI and hopel also increase, resulting in a decreased depth range. The partial aperture filtering can be used to limit the light ray width, but dark gaps appear. The moving array lenslet technique is introduced to fill in the dark gaps. Compared with integral imaging 3D display, the advantage of free wavefront control is shown in the proposed method. In  
Figure 11: (**a**) Numerical simulation results with focal length \(f\) = 3 mm. (**b**) Numerical simulation results with focal length \(f\) = 6 mm. (**c**) Numerical simulation results with focal length \(f\) = 6 mm and partial aperture filtering. (**d**) Numerical simulation results with partial aperture filtering and MALT.  
Figure 12: (**a**) Optical reconstruction results with focal length \(f\) = 3 mm. (**b**) Optical reconstruction results with focal length \(f\) = 6 mm and partial aperture filtering. (**d**) Optical reconstruction results with partial aperture filtering and MALT.  
the integral imaging display, each display unit is identical and hard to control separately. The trade-off between the resolution, depth range and view angle in integral imaging can only be improved by spatial-multiplexing and time-multiplexing techniques. This trade-off can be easily improved through multiplexing-encoding hologram. Simulation and optical experiments were performed to verify the proposed methods. This paper provides resolution enhancement for RPHS from three different aspects. It is a useful solution for quality improvement in different capturing conditions.  
Conceptualization, Z.W. and M.X.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W., A.W. and H.M.; supervision, G.L.; project administration, Q.F. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 61805065 and 61805066; The Fundamental Research Funds for the Central Universities, grant number JZ2021 HGTB0077. The authors declare no conflict of interest.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction  
Due to the recent developments in the power electronics technology, Voltage-Sourced Converter (VSC)-High Voltage Direct Current (HVDC) systems have solved the problem of bidirectional Power Flow (PF) in hybrid AC/DC grids [1; 2; 3]. MT-HVDC systems are capable of controlling the active and reactive power, independently. One of the important considerations to control the HVDC systems is that the \(V_{DC}\)-control and \(P\)-control VSC-HVDC stations should be capable of operating in inverter and rectifier modes, respectively [4; 5; 6]. The main purpose of applying different control strategies in MT-HVDC systems is to achieve a precise and secure control mode for MT-HVDC systems without violating the operational constraints. For stable operation and active and reactive PF, MT-HVDC systems need to maintain the DC voltage and frequency within the operating limits [1; 2; 3; 4].
# Flexural and Shear Performance of Prestressed Composite Slabs with Inverted Multi-Ribs
###### Abstract  
Half precast concrete slabs with inverted multi-ribs (Joint Advanced Slab, JAS), which enhance composite performance between slabs by introducing shear keys at connections between the slabs and improve structural performance by placing prestressing tendons and truss-type shear reinforcements, have recently been developed and applied in many construction fields. In this study, flexural and shear tests were performed to verify the structural performance of JAS members. Towards this end, two flexural specimens and four shear specimens were fabricated, and the presence of cast-in-place concrete and the location of the critical section were set as the main test variables. In addition, the flexural and shear performance of the JAS was quantitatively evaluated using a non-linear flexural analysis model and current structural design codes. Evaluation results confirmed that the flexural behavior of the JAS was almost similar to the behavior simulated through the non-linear flexural analysis model, and the shear performance of the JAS can also be estimated appropriately by using the shear strength equations presented in the current design codes. For the JAS with cast-in-place concrete, however, the shear strength estimation results differed significantly depending on the way that the shear contributions of the precast concrete unit and cast-in-place concrete were calculated. Based on the analysis results, this study proposed a design method that can reasonably estimate the shear strength of the composite JAS.  
inverted multi tee; prestressed concrete; precast concrete; structural performance; flexural analysis; shear strength +
Footnote †: journal: Applied Sciences
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 2 Theoretical Content
### RSA Algorithm
#### 2.2.2 Security of RSA Algorithm  
It is known, from the above steps, that a total of six numbers are involved, namely \(p\), \(q\), \(n\), \(\varphi(n)\), \(e\), and \(d\). The public key uses two (\(n\),\(e\)). The remaining four numbers are not public and the most critical is \(d\).  
Thus, consider whether to derive \(d\), given the known public key (\(n\),\(e\))?  
**First step:**  
\((de)mod\varphi(n)=1\). \(e\) and \(\varphi(n)\) need to be known.  
**Second step:**  
\(\varphi(n)=(p-1)(q-1)\). \(p\) and \(q\) need to be known.  
**Third step:**  
\(n=p\cdot q\). \(n\) needs to be factored.  
Therefore, it can be concluded from the above analysis that if \(n\) can be factorized, \(d\) can be calculated, which means that the private key is cracked. The security of RSA depends on the factorization of large numbers, but whether it is equivalent to the factorization of large numbers has not been proven. The factorization of large numbers is a very difficult thing, and no other effective methods have been, found except for brute force.