esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
In addition, the model accounted for whether the patient had had a diagnosed iAE prior to/at the time of response evaluation. Treatment responses according to RECIST 1.1 were divided into binary categories. The output of the prediction model is a continuous value [0,1] depicting the probability for the positive event, i.e., objective response (CR or PR) versus no objective response (SD + PD) (Figure 1). With a classification threshold of 0.5, the continuous probabilities were converted into binary outcomes, i.e., when the predicted probability for the positive event is greater than 0.5, prediction is labeled positive (CR or PR as treatment response), and if less than 0.5, then negative (SD or PD as a treatment response). Thus, the modeling methodology used in this study follows a general framework of binary classification in ML. Prediction performance of the model for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), which trained and tested 63 models, each time iteratively leaving one sample (related to one of the clinician-assessed treatment responses) out as a test set. Multiple response assessments across the same patients were used to create a timeline of best overall responses (BORs); however, in every time point analyzed, the parameters differ, comprising a new sample. Furthermore, the used gradient boosting trees-based algorithm (XGBoost) can handle intercorrelated observations or features, and, thus, correlated input parameters do not cause problems for the modeling. The prediction performance of the model was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew's correlation coefficient). Accuracy describes how many predictions were correct as a percentage, and 100% indicates a perfect classification. AUC is a commonly used performance metric for binary classification ranging from 0 to 1, where 0.5 is random guessing and 1 is perfect classification. F1 score is the weighted average of precision (i.e., how many of the cases predicted as positive are positive) and recall (how many of the positive cases are detected), which attains values between 0 and 1, 1 indicating perfect precision and recall. MCC summarizes all possible cases for binary predictions: true and false positives, and true and false negatives. MCC can be considered as a correlation coefficient between the observed and the predicted classifications, and it attains values between \(-1\) and \(1\), where 1 is perfect classification, 0 is random guessing, and \(-1\) indicates completely contradictory classification.
## 2 State of the Art
### Model-Based System Engineering and Safety Assessment
#### 2.1.2 Model-Based Safety Assessment (MBSA)  
Similarily to MBSE, the field of safety analysis is faced with the challenge of complexity. The traditional approaches, such as Petri nets, reliability block diagrams, event trees, and fault trees, are models that are very close to mathematical equations and lack structure or lack expressivity power. They also are very abstract compared to the system specification. The model-based safety assessment (MBSA) approaches try to answer these limitations.  
The MBSA models have a high expressivity, as they represent systems with a point of view closer to their architecture. The gain on expressive power is obtained by going from Boolean formalisms to state or event formalisms. There are three different kinds of model-based risk and safety assessment formalisms [12], including specialized profiles of MBSE tools such as [13], approaches that extend fault trees or reliability block diagrams such as dynamic fault trees [14] and model-based safety and reliability assessment modeling languages. In this work, we are interested in MBSA modeling languages, such as SAML [15], Figaro [16], and especially AltaRica 3.0 [17].  
AltaRica 3.0 is the third version of the AltaRica language; it is based on the mathematical framework of guarded transition systems (GTS) [18], which are state automata.
## 5 Comparison of Simulation Results and discussion
### Fundamental and Second Harmonic Amplitudes
The enhanced second harmonic generation also validates the determination of the nonlinear parameter, as further discussed below. The improved SHG in the dual-element transducer (Case 8, \(R=-1\)) is much weaker compared to the phase shift effect of the four-element transducer. These results indicate that the phase shift can be applied more effectively, using the four-element transducer to produce improved SHG in solid samples with stress-free boundaries. The focusing of the incident beam at the reflecting surface (Case 9, \(R=-1\)) provides only slightly improved SHG, compared to the maximum possible SHG obtained by the phase-shift of the four-element transducer (Case 5, \(R=-1\)). This can be attributed to the insufficient number of transmission elements that cannot achieve very tight focusing. A 64-element linear array probe or a spherically focused probe provides tight focusing to achieve the maximum possible SHG, with the phase difference between the two second harmonic components reaching about \(\pi/2\)[26; 27].
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 2 Materials and Methods
### Simulation and Phantom Experiment Setups
#### 2.6.1 Simulated Study  
The simulated data were acquired with the commonly used ultrasound simulation tool Field II (Technical University of Denmark, Lyngby, Denmark) [25]. The experimental setup for the simulation is shown in Table 1.  
We simulated a set of point targets in a homogeneous medium and an 8 mm radius anechoic cyst in a speckle pattern. An attenuation coefficient of 0.5 dB/(MHz\(\cdot\)cm) has been simulated in order to mimic the properties of the phantom used for experiments.  
In all simulations, plane waves with different steering angles were emitted. We selected an \(F\)-number of 1.75 as in [7]. The corresponding angle span [\(-\alpha_{\text{max}}\), \(\alpha_{\text{max}}\)] covered by the plane wave sequence is then given as:  
\[\alpha_{\text{max}}\approx\frac{1}{2}F=\frac{1}{2}\cdot 1.75\approx 16^{\circ}. \tag{8}\]  
The maximum number of plane waves \(N\) in the sequence is computed as:  
\[N=\frac{L}{F\lambda}=\frac{38.4}{1.75\times 0.296}\approx 74, \tag{9}\]  
where \(L\) and \(\lambda\) are the aperture size and the wavelength respectively. In order to have an odd number of plane waves, we decided to transmit 75 steered plane waves regularly spaced between \(-\alpha_{\text{max}}\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Simulation** & **Phantom Experiment** \\ \hline Pitch & 0.30 mm & 0.30 mm \\ Element width & 0.27 mm & 0.27 mm \\ Element height & 5 mm & 5 mm \\ Elevation focus & 20 mm & 20 mm \\ Number of elements & 128 & 128 \\ Aperture & 38.4 mm & 38.4 mm \\ Transmit frequency & 5.208 MHz & 5.208 MHz \\ Sampling frequency & 20.832 MHz & 20.832 MHz \\ Pulse bandwidth & 67\% & 67\% \\ Excitation & 2.5 cycles & 2.5 cycles \\ Transmit voltage & - & 30 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: The settings for the computer simulation and phantom experiment.  
and \(\alpha_{\max}\) for the acquisition of each phantom, which corresponds to an angle increment of 0.43\({}^{\circ}\). No apodization is adopted for transmission. In our simulation, we picked out 11 angles uniformly from the 75 total angles for obtaining a good sidelobe suppression result in real-time.
# Directional Active Noise Control with a Local Minimax Error Criterion
###### Abstract  
The traditional mean squared error (MSE) criterion can be formulated as a quadratic function of a vector of control filter coefficients, and it is easy to obtain optimal control filter coefficients. Although the MSE criterion can lead to noise reductions in the control area, an unpredictable directional residual sound field is generated. In this paper, we propose a method for multi-channel active harmonic noise control with a local minimax error criterion based on the Nelder-Mead algorithm, which leads to reductions at all error positions and greater reductions at controllable positions. Directional noise reduction experiments of two areas are presented for two different error criteria at discrete locations in an anechoic chamber. Compared with a system employing the traditional MSE criterion, the results show that an active noise control system with the proposed criterion can achieve extra reductions at specified locations and overall noise reductions at the same time. The present research offers some important insights into directional control.  
active noise control; harmonic noise; mean squared error; minimax error; directivity; multi-channel +
Footnote †: journal: Applied Sciences
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 5 Conclusions  
We propose the MSA-YOLO algorithm to enhance obstacle detection performance in rail transit under foggy conditions. The input image is processed through a series of filters: defogging, white balance, Gamma, contrast, tone, and sharpening. The MSA effectively removes dense fog, while preserving and enhancing critical obstacle information, facilitating YOLO in obstacle detection. Experimental results demonstrate that the proposed MSA-YOLO algorithm achieves higher detection accuracy in foggy scenarios.  
While the MSA-YOLO algorithm has shown promising results, several areas for future work remain. First, the algorithm could be further optimized for real-time performance in practical rail transit applications. Enhancing computational efficiency without compromising detection accuracy will be critical for deployment in real-world scenarios. Second, testing and refining the algorithm on more diverse and extensive datasets, including real-world foggy weather images, will help to improve its generalizability and robustness. Additionally, exploring the integration of the MSA-YOLO algorithm with other advanced defogging techniques and adaptive learning mechanisms could further enhance its performance.  
Study conception, design, and original draft writing: J.C.; data collection: D.L.; analysis, interpretation of results and writing review/editing: J.C. and W.Q.; draft manuscript preparation and editing: Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Grants 2023 AY31004, the Research Project of Jiaxing Civil Technology Innovation Research, and Innovative Education Special Project for Intelligent Navigation Applications in 2023 under Grant 2023 BD022.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors [PERSON] and [PERSON] was employed by the company Zhejiang Stream Rail Intelligent Control Technology Co., Ltd. and Shanghai Stream Rail Transportation Equipment Co., Ltd. Author [PERSON] was also employed by the company Zhejiang Stream Rail Intelligent Control Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 4: Detection results of IA-YOLO and MSA-YOLO.
# Retrofitting Masonry Walls against Out-Of-Plane Loading with Timber Based Panels
## 4 Numerical Study
### Numerical Simulation of Small-Scale Test on a Masonry Prism (MP) Specim
#### 4.2.1 Development of the Masonry Prism Model
For the anchor connection, the nodes on the surface of the brick around the connection holes were connected to the surface of the anchors (surface-to-surface contact) using the default contact enforcement in ABAQUS (Figure 14). This connection ensures that there was a full adhesive bond between the anchor and the surface of the holes in MP. This kind of connection represents the retrofit system where the OSB timber panel is connected to the MP using an adhesive anchor identified as the best performing connection type in the experimental study. Moreover, proper consideration of the applied boundary conditions in the numerical simulation was taken. The models (MP00-NM and MPOSB-NM) were constrained to replicate what was done experimentally to enable a sound basis for comparison of results. In the developed models, the nodes at the middle of the back-steel plate at the top of the MP were restrained in the x- and z-direction. In addition, the plate at the bottom was restrained in all the three directions (x, y, z) at the middle nodes to replicate the support condition of the tested specimen (Figure 14d). The loads considered in this analysis are self-weight of the model and applied unit load in the out-of-plane direction at the third and seventh course of the model. This loading and support arrangement is a replica of the four-point bending test carried out in the laboratory. The out-of-plane load is applied as a unit uniform distributed load (UDL) on the steel plate tied to the front face of the model (Figure 14d). The analysis is the static RIKS load control method, similar to the test condition. The total load capacity of the model is measured as the load proportionality factor multiplied by the applied load in Newton (N).
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 5 Model Effectiveness Validation
### Simulation Result Analysis  
There is no doubt that the intersection channelization information, traffic volume information, and signal timing collectively affect the operational performance of MUT intersections. Table 6 shows the simulation results of the cellular automata model and VISSIM model over five consecutive days.  
It can be seen from Table 6 that, compared to the current MUT intersection, the modified MUT intersection reduces the average vehicle delay to varying degrees (regardless of which simulation model is used). For the simulation results of the VISSIM models, the percentage reductions in the average vehicle delay are 10.1%, 14.1%, 13.5%, 12.6%, and 10.7% from Day 1 to Day 5, respectively. For the simulation results of the cellular automata models, the percentage reductions in the average vehicle delay are 11.3%, 13.6%, 13.7%, 12.7%, and 9.8% from Day 1 to Day 5, respectively.  
It is worth noting that modeling uncrossable boundaries with a certain width in cellular automata models has still not been fully explored in relevant theoretical frameworks and the literature. Due to this limitation, we simplified the cellular automata model by not accounting for the width of the median U-turn. This simplification may have contributed to the worse simulation results of the cellular automata models compared to the VISSIM models (regardless of whether the current or modified MUT intersection is considered).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modified Parameters**} & \multirow{2}{*}{**Direction**} & \multicolumn{5}{c}{**Periods**} \\ \cline{3-6}  & & **Day 1** & **Day 2** & **Day 3** & **Day 4** & **Day 5** \\ \hline \multirow{3}{*}{Separation distance (m)} & East & 119.5 & 121 & 117.2 & 119.2 & 123.3 \\ \cline{2-6}  & West & 88.8 & 90.3 & 85.4 & 88.1 & 91.6 \\ \hline \multirow{3}{*}{Gap of median U-turn (m)} & East & & & & & \\ \cline{2-6}  & West & & & & & \\ \hline \multirow{3}{*}{Width of median U-turn (m)} & East & & & & & \\ \cline{2-6}  & West & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The modified parameters for the selected MUT intersection.  
However, the discrepancy between the average vehicle delay results of the cellular automata models and VISSIM models are relatively low, with the maximum error being around 7%.  
Based on the analysis above, the comparisons confirm that the proposed cellular automata model demonstrates relatively high accuracy when calculating the average vehicle delay for the selected MUT intersection. Furthermore, it can be shown that the proposed cellular automata model effectively reflects vehicle movements at the selected MUT intersection. Additionally, the simulation results of the modified MUT intersection indicate that the proposed calculation methods for critical parameters of MUT intersections can be beneficial for reducing average vehicle delay.
## 3 Inter-Turn Fault Diagnosis of Integrated Motor and Drive Systems
### Fault Diagnosis Algorithm  
The terminal connections are implemented based on the space voltage vector by the three-phase inverter. Based on the impedance and space voltage vector, the ITF algorithm is proposed as shown in Figure 6. The alternating voltage is applied to the motor according to the phase of the space voltage vector through the three-phase inverter. The impedances corresponding to each phase of the space voltage vector are calculated from the input voltage and measured phase currents. The fault diagnosis is performed by identifying the maximum and minimum impedances associated with each phase of the space voltage vector. For example, when an ITF occurs in phase A with the \(\Delta\) connection, the phases of the space voltage vector corresponding to the maximum and minimum impedance values are 60\({}^{\circ}\) and 150\({}^{\circ}\), respectively, as shown in Figure 6b. In the same way, when the ITF occurs in phase B or C, the fault diagnosis can be performed as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Phase of Space Voltage Vector**[\({}^{\circ}\)] & **Terminal Connection** \\ \hline
0 and 180 & _a-bc_ \\
30 and 210 & _c-a_ \\
60 and 240 & _c-ab_ \\
90 and 270 & _b-c_ \\
120 and 300 & _b-ca_ \\
150 and 330 & _a-b_ \\ \hline \end{tabular}
\end{table}
Table 4: Terminal connections for phases of space voltage vector.  
Figure 6: Inter-turn fault diagnosis algorithm: (**a**) Y connection; (**b**) \(\Delta\) connection.
## 3 Simulation Results and Analysis
### Effect of Temperature and Water Content Coupling on Methane Adsorption Performance  
#### 3.3.1 Orthogonal Test Design  
An orthogonal test was designed to simulate the effect of temperature and water content coupling on CH4 adsorption. The experimental design was carried out as a two-factor, nine-level orthogonal test, and the determined levels of each factor are shown in Table 3.  
The two experimental factors, temperature and water content, and their corresponding levels were arranged sequentially without considering the interaction of individual factors. Based on the results of polar analysis, the effects on CH4 adsorption under different factors were plotted and the experimental factors were ranked in order of significance. The orthogonal table is shown in Table 4.
## 3 Results and Discussion
### Arsenic Bioavailability in Paddy Soils Determined Using the Sequential Extraction Procedure  
The bioavailability and mobility of As are closely related to its geochemical speciation, which is indicated by the extractable fraction [70,71]. Each As fraction is diverse in mobility and bioavailability [72]. In this study, the first two fractions (F1 and F2) were mobile and unstable and thus considered the bioavailable fractions for rice [70,73].  
As shown in Table 6, As in the paddy soil existed mostly as the residual fraction (F7) in the karst area, accounting for 76.96%. F7 is considered stable in mineral crystal lattices and is unavailable for plants [73]. The proportions of other fractions (F3, F4, F5, and F6) were F4 (11.73%) > F5 (8.07%) > F6 (1.19%) > F3 (0.97%) > F1 (0.56%) > F2 (0.51%). The water-soluble and exchangeable fractions of As were the least abundant of the seven fractions. By comparing the bioavailable fractions of As with those of another enriched heavy metal, cadmium, in karst areas, it was found that the proportion of bioavailable fractions of As was lower than that of cadmium [38]. Furthermore, dendrograms were drawn using a hierarchical cluster analysis to determine the similarities between the different As fractions and rice grains (Figure 4). F1 and F2 were amalgamated as one fraction (F1, F2), owing to their low concentrations. The results indicate a statistical similarity between F1, F2 and the As content in the rice grains. Conversely, no significant similarity was observed between the As content in the rice grains and other fractions, including the total soil As. This implies that the bioavailable fraction (F1, F2) of As in paddy soils is readily absorbed by rice, while massive residual As in paddy soils is hardly absorbed by rice, which causes a low As content in rice grains and a high As content in paddy soils in karst areas.  
Figure 3: Arsenic content in the root soil under different pH values [16].
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 3 Integration of the Enabling Technologies
### Radio Access Network Platforms  
The comparison between the RAN solutions/platforms is based on eight factors such as the support for eNodeB (eNB), gNodeB (gNB), Multiple Input Multiple Output (MIMO), Software Defined Radio (SDR) user equipment, Commercial Off-the-Shelf (COTS) user equipment, type of license, main contributors, and type of community support. The comparison shown in Table 1 shows that the OAI RAN and srsLTE provide both the implementation of eNB and gNB whereas the Radisys RAN only provides the implementation of gNB. Radisys contributes to O-RAN providing an open-source implementation of the 3 GPP new radio stack for the gNB distributed unit. O-RAN is open, intelligent, and deployed on a virtualized platform with high flexibility. The concept behind the O-RAN is all about disaggregating the hardware from software: open hardware and software using the standard processors with open interfaces. All RANs, listed in the table support the MIMO technology for the uplink and downlink. Furthermore, the OAI RAN and srsLTE can be used to serve both COTS and SDR UEs, however, this information is not available for the Radisys RAN. In terms of the license, the OAI RAN is distributed under the Apache License v1.1 which enables individuals and companies who have patents to contribute to the OAI source code while maintaining their patent rights. The Radisys is distributed under the Apache v2.0 license and the srsLTE is written in C++ and released under the GNU AGPLv3 license.
# Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
## 2 System Model
Hence, the CDF of these links can be expressed as follows:  
\[F_{W_{ij}}(w)=\frac{2F_{1}(m,m;1+m;1)}{m\beta(m,1-m)}-\frac{\Gamma(1-m)}{\beta (m,1-m)}\bigg{(}\frac{w}{\tilde{\gamma}}\bigg{)}^{\frac{m-1}{2}}e^{-\frac{s}{2 \tilde{\gamma}}}W_{\frac{m-1}{2},\frac{m}{2}}(\frac{w}{\tilde{\gamma}}) \tag{6}\]  
where \(W_{ij}\in\{|h_{R_{k}D}|^{2}\}\), \({}_{2}F_{1}(.,.;.;. )\) is the Gauss hypergeometric function given by Equation (9.100) [63], \(\Gamma(x)\) is the gamma function given by Equation (8.310.1) [63], \(W_{.,.}(. )\) is the Whittaker function given by Equation (9.220) [63], and \(\beta(.,. )\) is the beta function given by Equation (8.384) [63]. Please refer to Appendix A for more details.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
## 2 Overview of Direct Shear Test  
### Material  
The polyurethane grouting material used in this study is a two-component, non-water-reactive foaming material, and its chemical reaction principle is as follows: n OCN-R(isocyanate)-NCO + n HO-R\({}^{\prime}\)(polyol)-OH\(\rightarrow\)-[O-R\({}^{\prime}\)-O-CONH(polyurethane)-R-NHCO)n-. Component I consists of isocyanate and polyisocyanate, while Component II contains polyol, a foaming agent, and additives. The two components are mixed in a 1:1 ratio under heating conditions, resulting in a polymerization reaction between isocyanate and polyol to produce a segmented copolymer with repeating urethane linkages. Duringthis reaction, the heat released transforms the foaming agent from liquid to gas, leading to the formation of a stable foam structure.  
According to the requirements of the GB/T 50081-2019 [30], concrete of design compressive strength grade C30 was prepared for the experiments. The concrete was composed of P-O 42.5 Portland cement, Zone II sand with particle sizes ranging from 0 to 5 mm, groundwater, water reducer, and coarse aggregate with a maximum particle size of 20 mm. The specific material proportions for the concrete are detailed in Table 1.