esa-sceva/satcom-chunk-collection huggingface dataset:
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Complete Pipeline of the Proposed Methodology
#### 2.1.2 Preprocessing  
This dataset is very challenging because of its high diversity, as it contains images of multiple modalities. Also, this dataset has a considerable problem of class imbalance. Initially, when this dataset was proposed in 2017, it had 111,155 CUIs associated with it. Later on, these CUIs were reduced to 5528 in the 2019 challenge, and for the 2020 challenge, it had 3047 CUIs. Reducing the number of concepts, the F1-score increases every year from 0.1583 in ImageCLEF 2017, 0.1108 in ImageCLEF 2018, and 0.2823 in ImageCLEF 2019 to 0.3940 in the year 2020. By keeping this fact in mind, we attempted to address this problem by reducing the number of concepts from 3047 to 20. We selected these concepts by maximizing the most frequent concepts technique. Figure 3 illustrates the most recurrent concepts in the dataset and provides an explanation of the most frequent ones.  
New CSV files were generated for the training and validation phases, based on the 20 most frequent concepts identified in the dataset. During this process, it was observed that some images were not associated with any of the selected concepts. To address this  
\begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Description** & **CUIs** & **UMLS Description** \\ \hline DRAN & Angiography & C002978 & Angiogram \\ DRCO & Combined Modalities & C0040398 & X-Ray Computed \\ DRCT & Computerized Tomography & C0040405 & Tomography \\ DRMR & Magnetic Resonance & C0024485 & Magnetic Resonance Imaging \\ DRPE & Positron Emission Tomography & C0032743 & Positron Emission Tomography \\ DRUS & Ultrasound & C0041618 & Ultrasonography \\ DRXR & X-Ray & C0043299 & Diagnostic Radiologic Examination \\ \hline \end{tabular}
\end{table}
Table 10: Modalities information.  
Figure 3: Distribution and description of the 20 most frequent concepts.  
issue and ensure compatibility with the CNN training pipeline, a new class, labeled \"N\", was introduced to represent images without any assigned concepts. Although it would have been possible to exclude such images from the dataset, doing so would have significantly increased the complexity and time required for data management. Therefore, the introduction of the \"N\" class provided an efficient solution to maintain dataset consistency and facilitate model training.  
While the reduction from 3047 to 20 concepts allowed us to address severe class imbalance and improve model performance, we acknowledge that this simplification may have led to the omission of important information, potentially limiting the model's ability to generalize across all medical concepts. In this study, our primary objective was to demonstrate the feasibility of automatic concept detection on a manageable and meaningful subset of frequent concepts. Future work will aim to expand the set of detectable concepts and to assess the model's generalization capabilities across more diverse and less frequent classes.
## 5 Verification Case Study and Discussions
### Pareto-Optimal Designs of Planar Miniaturized Multiband Antenna
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Designs** & \(\mathbf{x}^{(1)}\) & \(\mathbf{x}^{(2)}\) & \(\mathbf{x}^{(3)}\) & \(\mathbf{x}^{(4)}\) & \(\mathbf{x}^{(5)}\) \\ \hline \(F_{1}[dB]\) & \(-16.02\) & \(-15.67\) & \(-15.45\) & \(-15.03\) & \(-14.71\) \\ \(F_{2}[mm^{2}]\) & 634.92 & 628.00 & 617.97 & 602.76 & 577.17 \\ \hline \(d\) & 8.58 & 8.61 & 8.76 & 8.69 & 8.27 \\ \(l\) & 31.20 & 31.40 & 29.26 & 29.26 & 28.90 \\ \(l_{1}\) & 12.70 & 12.50 & 12.00 & 11.95 & 11.09 \\ \(l_{2}\) & 8.80 & 8.80 & 9.04 & 9.04 & 8.79 \\ \(l_{3}\) & 6.92 & 6.90 & 7.28 & 7.21 & 7.01 \\ \(l_{4}\) & 11.43 & 11.43 & 11.73 & 11.73 & 11.37 \\ \(w\) & 20.35 & 20.00 & 21.12 & 20.60 & 19.97 \\ \(w_{1}\) & 3.23 & 3.23 & 3.34 & 3.31 & 3.13 \\ \(w_{2}\) & 3.10 & 3.10 & 3.27 & 3.27 & 3.27 \\ \(w_{3}\) & 1.01 & 1.00 & 1.19 & 1.17 & 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Miniaturized multiband planar antenna: selected Pareto-optimal designs. Figure 12: Simulation and prediction \(S_{11}\) for the Pareto-optimal designs in Table 3.
# Leader-Follower Role Allocation for Physical Collaboration in Human Dyads
## 3 Results
The forces applied by the partner with a lower individual performance (\(\mathbf{F_{i}}\)) and partner with a higher individual performance (\(\mathbf{F_{s}}\)), the proportional force (\(\mathbf{F}\)) as well as the difference between the partners' forces (\(\Delta F\)) during a collaborative task, from which the leader was calculated, are also presented for each pair and target separately in Figures 11-19. Results presented in Figure 4 suggest that the subject displaying higher performance in the individual trials will be the leader in the collaborative trials as the average leading value for all targets are positive. We can also see that the influence of each partner on the task is not constant but rather changes based on the target. Analysis of variance showed that the difference in partner influence on the task performance was based on the target distance (\(F_{(2,7)}=11.347\), \(p=0.001\)) and the target size (\(F_{(2,7)}=6.659\), \(p=0.009\)) with the \(F\) and \(p\) value of the interaction between distance and size being (\(F_{(2,7)}=1.983\), \(p=0.125\)). Here, the influence of the higher-performing individual on the task increased with the increase in target size and target distance. _Determining task performance:_ Figure 5a shows the mean and standard error (SEM) for the overall task performance in different experiment types with a higher value representing better overall task performance. Analysis of variance showed that the experiment type has a significant influence on the overall task performance (\(F_{(2,7)}=14.847\), \(p<0.001\)). A post hoc t-test showed that the task performance is statistically different between the higher-performing and the lower-performing individual (\(t_{(7)}=-4.450\), \(p<0.001\)) as well as between the subject with a lower individual performance and subjects in collaboration (\(t_{(7)}=-4.949\), \(p<0.001\)). On the other hand, the task performance between the subject with a higher individual performance and subjects in collaboration is not statistically different. Figure 4: Leading subject for each target, where the values represent mean leader \(L[N]\), calculated using Equation (3), between the 8 pairs \(\pm\) SEM. Analysis of variance showed as well that the target distance and target size have a significant influence on the task performance for lower-performing individuals (size: \(F_{(2,7)}=17.044\), \(p<0.001\); distance: \(F_{(2,7)}=94.783\), \(p<0.001\); size \(\times\) distance: \(F_{(2,7)}=5.498\), \(p=0.002\)), higher-performing individuals (size: \(F_{(2,7)}=12.452\), \(p<0.001\); distance: \(F_{(2,7)}=194.926\), \(p<0.001\); size \(\times\) distance: \(F_{(2,7)}=19.90\), \(p=0.123\)), and subjects in collaboration (size: \(F_{(2,7)}=17.685\), \(p<0.001\); distance: \(F_{(2,7)}=187.351\), \(p<0.001\); size \(\times\) distance: \(F_{(2,7)}=5.772\), \(p=0.005\)). This result is visualised in Figure 5b, where the mean and standard errors (SEM) of task performance are shown for each target size and distance separately.
# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 3 Approach
### Multi-Scale Convolutional Model Based on Multiple Attention  
The residual block with feature importance ordering processed by the Channels-Sort module is divided into \(n\) small blocks by equal grouping operation, where \(X=[X_{1},X_{2},...,X_{k},...,X_{n}]\). From front to back, the importance of each group to the classification results is gradually reduced. Therefore, it enables more important features to expresses in more scales. Next, we introduce a spatial attention block before the Channels-Sort module to capture more distinguished local features, forming a multi-scale convolutional block with dual attention (DAMS-block). This process can be obtained in the X to Y stages of Figure 2. For a more fine-grained image classification, we further constructed the multi-scale convolutional block with multiple attention (MAMS-block). On the basis of DAMS-block, we pay attention to each residual small block after grouping. The channel attention analysis is performed on the small blocks in the same residual block in order to better realize the feature fusion between different small blocks. A network structure in which a plurality of MAMS-blocks is superimposed is called MAMS-CNN. The DAMS-block and the MAMS-block are given in Figure 3.  
As shown in Figure 3, channel attention analysis is performed in each residual small block, and more detailed features can be observed in the receptive fields of different scales. For the operation between the small blocks, we chose to use the channel fusion of \"1 \(\times\) 1\" convolution instead of adding them by element. The main reason is that different small groups focus on different features. Simple addition will blur the attention information, but channel fusion will get more attention features. Specific operations are as shown in Equations (8) and (9).  
\[F_{se}:V=GlobleAvgPool(X_{k}) \tag{8}\] \[F_{se}:V^{{}^{\prime}}=Sigmoid(Dense(Dense(V,c/r),c))\] \[F_{scale}:X=Add(Multiply(V^{{}^{\prime}},X_{k}),X_{k})\] \[Y_{k}=\left\{\begin{array}{cc}F_{se}(X_{1})&k=1\\ F_{se}(Conv(Concat(Y_{k-1},X_{k}),\{1\times 1\}))&1<k<n\\ Conv(Concat(Y_{n-1},X_{n}),\{1\times 1\})&k=n\end{array}\right.\]  
The \"SE-block\" in Figure 3 performs the \(F_{se}(*)\) operation. Where \(F_{se}(*)\) means to perform a Squeeze-and-Excitation operation, that is, channel attention. \(F_{se}(*)\) is divided into three phases: Squeeze operation, excitation operation, and reweight operation. See reference [22] for details.  
Figure 3: The structure schematic of DAMS-block and MAMS-block.  
The parameter n represents the number of groups per residual block (e.g., n = 4 in Figure 3). \(Conv(*,\{1\times 1\})\) fuses channel information and adjusts the channel number using \"1 \(\times\) 1\" convolution. \(Concat(*)\) means that two convolutional blocks are connected in series along the direction of the feature channel.
## 3 Cylindrical Cavity Expansion in the SCC Soil  
### Problem Description  
The undrained expansion of a cylindrical cavity with an initial radius \(a_{0}\) in an infinite structured soil is schematically shown in Figure 3, where \(\sigma_{h}\) and \(\sigma_{v}\) denote the initial horizontal and vertical total stress, respectively, and \(u_{0}\) is the initial pore pressure. As the internal pressure \(\sigma_{a}\) increases gradually from its initial value \(\sigma_{h}\), the cavity expands from \(a_{0}\) to a larger cavity radius \(a\). Meanwhile, a typical soil element moves outward along the radial direction from the initial position \(r_{x0}\) to the current position \(r_{x}\). During this process, the soil element first undergoes elastic deformation until the initial yielding occurs, and then experiences plastic hardening or softening with structure degradation. The soil structure begins to be crushed if the soil element with an incompletely destroyed structure may attain a critical state before reaching the current position, \(r_{x}\). In Figure 3, \(r_{xp}\) and \(r_{xc}\) denote the positions of the soil element at the precise moment when it enters the plastic state and when its structure begins to be crushed.  
Cylindrical coordinates (\(r,\theta,z\)) are considered because of the symmetry of the cylindrical cavity expansion problem. Considering a soil element located at the radial position \(r\) with unit height in \(z\) direction, the radial and tangential total stresses, \(\sigma_{r}\) and \(\sigma_{\theta}\), satisfy the force equilibrium equation as follows:  
\[\frac{d\sigma_{r}}{d\tau}+\frac{\sigma_{r}-\sigma_{\theta}}{r}=0 \tag{9}\]  
Figure 3: Mechanical model for undrained cylindrical cavity expansion in the SCC soil.  
According to the Terzaghi effective stress principle, Equation (9) can be written as  
\[\frac{d\sigma_{r}^{\prime}}{dr}+\frac{du}{dr}+\frac{\sigma_{r}^{\prime}-\sigma_{ \theta}^{\prime}}{r}=0 \tag{10}\]  
where \(\sigma^{\prime}_{r}\) and \(\sigma^{\prime}_{\theta}\) are the effective components corresponding to \(\sigma_{r}\) and \(\sigma_{\theta}\), respectively, and u is the pore pressure at position \(r\).
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 6 Conclusions  
In this paper, an ANFIS-PID controller is proposed and applied to a four-degrees-of-freedom hydraulic manipulator to improve the trajectory tracking accuracy of the manipulator. Firstly, the mechanical structure and the hydraulic drive system of the hydraulic manipulator are designed; secondly, the forward and inverse kinematics of the manipula  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Joint \(i\)**} & \multicolumn{3}{c}{**Mean Absolute Error (rad)**} & \multicolumn{3}{c}{**Root Mean Square Error (rad)**} \\ \cline{2-7}  & **PID** & **FPID** & **ANFIS-PID** & **PID** & **FPID** & **ANFIS-PID** \\ \hline
1 & 0.082699 & 0.016138 & 0.000294 & 0.096532 & 0.019189 & 0.000539 \\
2 & 0.031882 & 0.009513 & 0.000282 & 0.040795 & 0.012259 & 0.000366 \\
3 & 0.038149 & 0.012045 & 0.000240 & 0.041039 & 0.013060 & 0.000282 \\
4 & 0.027114 & 0.013934 & 0.000233 & 0.029157 & 0.015178 & 0.000320 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean absolute error and root mean square error of trajectory tracking for each joint.  
Figure 11: (**a**) Angular displacement error for joint 1; (**b**) angular displacement error for joint 2; (**c**) angular displacement error for joint 3; (**d**) angular displacement error for joint 4.  
tor are analyzed, the trajectory curves are obtained by the fifth polynomial interpolation function and the connection between the hydraulic system and the manipulator system is derived according to the principle of virtual work, so as to establish the coupled dynamics equations of the hydraulic manipulator system. Finally, for the uncertainty and nonlinearity of the hydraulic manipulator system, the ANFIS-PID controller is designed, the adaptive regulation of the PID parameters is accomplished using the autonomous learning and fuzzy reasoning ability of ANFIS and the simulation is analyzed in Matlab/Simulink. In addition, the PID and fuzzy PID controllers are applied to the manipulator in a simulation for comparative performance analysis. The simulation results show that the ANFIS-PID controller has a smaller tracking error, higher control accuracy and better control effect in trajectory tracking compared with the PID and fuzzy PID controllers; this is in accordance with expectations.  
J.H.: Conceptualization, Supervision, Methodology. F.W.: Software, Writing, review and editing, Validation, Data curation. C.S.: Formal analysis, Software, Writing original draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the foundation of Science and Technology Program of Jiangsu Province, funding number: SZ-YC202165.  
Not applicable.  
Data Availability Statement: Data is unavailable.  
The authors would like to thank [PERSON] at Jiangsu University for their valuable suggestions and help. The work was supported by foundation of Science and Technology Program of Jiangsu Province (No: SZ-YC202165).  
The authors declare no conflict of interest.
# Correlation Analysis of Macro-Micro Parameters of Sandstone Based on PFC3D
## 5 Conclusions  
This study addressed the issue of the low compressive-tensile strength ratio in the traditional parallel bond model (PBM) when simulating rock materials. The PBM model was improved by introducing a strong-weak contact group parameter configuration strategy to enhance its adaptability to the mechanical behavior of sandstone. Based on this improvement, orthogonal experimental design and regression analysis methods were used to systematically study the effects of 12 mesoscopic parameters on seven macroscopic mechanical properties. A quantitative relationship model between the macro and micro parameters was established, and the model's applicability and accuracy were verified through laboratory experiments.  
The main contributions and value of this research are reflected in the following aspects: (1) The introduction of strong-weak contact groups optimized the particle contact parameters, thereby improving the compressive-tensile strength ratio and providing a reference for improving the PBM model; (2) Through orthogonal design and correlation analysis, we identified the mesoscopic parameters that dominantly influence macroscopic responses, thereby establishing a foundation for model parameter selection and optimization; (3) a multiple regression model was constructed to enable the inverse prediction of macroscopic performance and mesoscopic parameter calibration, improving modeling efficiency and reliability to a certain extent, with practical engineering application value.  
The verification results showed that the model's predictions for key parameters, such as the elastic modulus, compressive strength, and Poisson's ratio, exhibited errors controlled within 5% compared to the laboratory test results. The simulated compressive-tensile strength ratio was close to the measured value, and the failure mode was also consistent. The research findings provide a reference for future parameter inversion, discrete element modeling, and numerical simulation studies of similar rock materials.  
Conceptualization, G.Z. and Q.L.; Methodology, G.Z.; Software, K.J.; Validation, G.Z. and T.Q.; Formal analysis, Q.L. and Y.L.; Investigation, Z.L.; Resources, T.Q.; Data curation, Y.L.; Writing--original draft, G.Z. and Q.L.; Writing--review & editing, Q.L. and T.Q.; Visualization, Z.L.; Supervision, Z.L. and K.J.; Project administration, Y.L. and K.J.; Funding acquisition, G.Z., Q.L. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51774122), the Graduate Innovation Project of Heilongjiang University of Science and Technology (No. YJS2025026), and the Graduate Innovation Project of Heilongjiang University of Science and Technology (No. YJS2025131).  
Not applicable.  
Figure 8: Comparison of uniaxial compressive and tensile stress–strain curves.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 3 Results and Discussions
### Biolayer Formation  
The LA-MoS2 nano-sheets deposited on the SPCE contains carboxylic acid groups provided by LA that can be covalently attached to the aptamer. In a second step, the aptamer was tethered to the surface through an amide bond established between the terminal acid of the self-assembled lipoic acid on the surface of MoS2 and the amino group of the aptamer using NHS/EDC chemistry as depicted in Scheme 1 step b. The SPCE modifications steps were monitored by cyclic voltammetry (CV) and EIS using [Fe(CN)6]3/4- as a redox probe (Figure 3). The CV of LA-MoS2 showed a decrease in the peak current indicating covalent attachment of negatively charged aptamer on the surface of the electrode, which repelled the negatively charged redox probe (Figure 3a, curve b). On the other hand, electrochemical impedance spectroscopy was used to characterize the modified surface. The variation of semicircle diameter of the Nyquist plot (Figure 3b) is related to the charge transfer resistance (Rct) and reflects the status of the electrode surface. Modifying the SPCE/LA-MoS2 with aptamer, led to an increase of the Rct (Figure 3b, curve b). This can be explained by the negatively charged aptamer forming a blocking barrier to the diffusion of the redox probe ions thus confirming the CV results. This is in a good agreement with previous reports [40].  
Figure 2: SEM images of: (**a**) bare screen-printed carbon electrode surface (SPCE); (**b**) SPCE modified LA–MoS2 Nano-sheets; (**c**) EDX analysis of the modified SPCE with MoS2 nano-sheets.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 4 Optimization Problem Formulation
### Function and Problem Formulation  
Defining \(L_{i}\) as the lifetime of the \(i\)th sensor node, which stands for how many superframes the \(i\)th node can function well before it runs out, it can be mathematically represented as:  
\[L_{i}=\frac{E_{i}}{C_{i}}, \tag{3}\]  
where \(C_{i}\) is defined as the \(i\)th sensor node's energy consumption rate, which stands for the amount of energy consumed by the \(i\)th sensor node in one superframe. Using Equations (1) and (2), \(C_{i}\) can be further expressed as:  
\[C_{i}=E_{tx}(k,d_{i},n)\cdot(1+R_{i})+E_{rx}(k)\cdot R_{i}, \tag{4}\]  
where  
\[d_{i}=\sum_{j=1}^{l}d_{i,j}\cdot x_{i,j}, \tag{5}\]  
\[R_{i}=\sum_{\begin{subarray}{c}j=1\\ j\
eq i\end{subarray}}^{l}x_{j,i}. \tag{6}\]  
As emphasized in Section 3.1, the lifetime of WBANs is decided by the minimum lifetime of sensor nodes in the network. Therefore, the lifetime of WBANs can be expressed as:  
\[Lifetime=\min_{i\in 1,\cdots,l}\qquad L_{i}. \tag{7}\]  
According to Equations (3)-(7), it can be derived that the lifetime of WBANs is the function of matrix \(\mathbf{X}\), which stands for relay selection results:  
\[Lifetime\left(\mathbf{X}\right)=\min_{i\in 1,\cdots,l}\qquad L_{i}(\mathbf{X}). \tag{8}\]As a consequence, aiming to maximize a WBAN lifetime, the optimization problem can be mathematically formulated as:  
\[\underset{\mathbf{X}}{\text{maximum}} Lifetime\left(\mathbf{X}\right)\text{,}\] subject to : \[\text{Constraints (a)}: Equations\left(1\right)-\left(6\right)\text{ and }\left(8\right)\text{,}\] Constraints (b) : \[\sum_{j=1}^{l}x_{i,j}=1,\forall i=1,2,\cdots,l\text{,}\] Constraints (c) : \[x_{i,j}\in\{0,1\},\forall i,j=1,2,\cdots,l\text{.}\]  
In the optimization problem, constraint (b) stands for the IEEE 802.15.6 topology restriction, while constraint (c) states that each optimization variable is binary. The optimization problem formulated above is an NP-hard binary integer problem, which makes traditional methods difficult to get the optimization results. Therefore, we design a heuristic iterative solution to rapidly address the optimization problem. The solution is introduced in the next section.