esa-sceva/satcom-chunk-collection huggingface dataset:
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 7 Numerical Evaluation
### Results
In other words, the objective function that the two algorithms were aiming for is not the same as the SINR measures plotted in the figure--only a reasonable proxy for it with reduced channel information. As can be seen in Figure 5a, in terms of the objective function value obtained, the optimization algorithm always outperformed the approximation algorithm, as expected. Across all the cases tested, the approximation algorithm achieved an average of 81% of the optimal objective function value. Figure 5b shows the time required to solve MMAD to optimality versus the number of nodes. As is typical for this kind of combinatorial optimization problem, the solution time grows exponentially with the problem instance size (note that the solution times are shown on a log scale). For small instances of 15 nodes, the optimization takes less than ten seconds to run, which is feasible in practice for IoT scenarios where nodes transmit on the order of minutes or higher. However, for larger instances, it would not be feasible to use the optimization algorithm; the largest solution time observed was 1,232,518.65 s--more than 14 days--and realistic IoT scenarios can have a far greater number of nodes than we tested here. As a comparison, our Python implementation of the approximation algorithm ran in less than 0.1 ms for all cases tested. The other comparison algorithms had similarly fast solution times as well. For practical deployments, the approximation algorithm is thus a more appropriate solution. Based on the SINR characteristics for the grouped nodes, further communication-oriented performance parameters such as outage probability and achievable rate can be derived for state-of-the-art transmission schemes [3].
###### Abstract  
MR-Linac is a recent device combining a linear accelerator with an MRI scanner. The improved soft tissue contrast of MR images is used for optimum delineation of tumors or organs at risk (OARs) and precise treatment delivery. Automatic segmentation of OARs can contribute to alleviating the time-consuming process for radiation oncologists and improving the accuracy of radiation delivery by providing faster, more consistent, and more accurate delineation of target structures and organs at risk. It can also help reduce inter-observer variability and improve the consistency of contouring while reducing the time required for treatment planning. In this work, state-of-the-art deep learning techniques were evaluated based on 2D and 2.5D training strategies to develop a comprehensive tool for the accurate segmentation of pelvic OARs dedicated to 0.35 T MR-Linac. In total, 103 cases with 0.35 T MR images of the pelvic region were investigated. Experts considered and contoured the bladder, rectum, and femoral heads as OARs and the prostate as the target volume. For the training of the neural network, 85 patients were randomly selected, and 18 were used for testing. Multiple U-Net-based architectures were considered, and the best model was compared using both 2D and 2.5D training strategies. The evaluation of the models was performed based on two metrics: the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). In the 2D training strategy, Residual Attention U-Net (ResAttU-Net) had the highest scores among the other deep neural networks. Due to the additional contextual information, the configured 2.5D ResAttU-Net performed better. The overall DSC were 0.88 \(\pm\) 0.09 and 0.86 \(\pm\) 0.10, and the overall HD was 1.78 \(\pm\) 3.02 mm and 5.90 \(\pm\) 7.58 mm for 2.5D and 2D ResAttU-Net, respectively. The 2.5D ResAttU-Net provides accurate segmentation of OARs without affecting the computational cost. The developed end-to-end pipeline will be merged with the treatment planning system for in-time automatic segmentation.  
automatic segmentation; organ at risk; 2.5D; convolutional neural networks; MRI +
Footnote †: journal:  
0023316521[[https://doi.org/10.3390/a16110521](https://doi.org/10.3390/a16110521)]([https://doi.org/10.3390/a16110521](https://doi.org/10.3390/a16110521))
## 4 Experimental Results and Discussion
### Self-Centering Capacities  
In this paper, the self-centering factor \(\eta\) of the first cycle for each drift level is adopted to evaluate the self-centering capacity of the system, which is calculated as follows:  
\[\eta=\frac{\Delta_{\mathrm{r}}}{\Delta} \tag{2}\]  
where \(\Delta_{\mathrm{r}}\) is the residual displacement of each drift level after unloading, and \(\Delta\) is the maximum displacement of the corresponding drift level. \(\Delta\) takes the average of the residual deformation in the positive and negative directions at each loading drift level. The relationships of \(\eta\) versus loading drift of the three tests are shown in Figure 14. During the loading process, the values of \(\eta\) in the three tests remained relatively low, indicating the satisfactory self-centering capacity of the proposed system mainly relying on the steel strands. As the plastic deformation of the steel energy dissipaters required restoring force when the loaded specimen returned to its original position, the overall \(\eta\) of Tests RW1-2 and RW1-3 was slightly higher than that of Test RW1-1 because more energy dissipaters were installed in Tests RW1-2 and RW1-3. The above showed that additional dissipaters have a significant positive effect on the strength and energy dissipation capacity of the structure; however, it is undeniable that this leads to a weakening of the self-centering capacity. Therefore, the relationship between the performance demands should be weighed for the design of PRW-TSD systems. Furthermore, the crushes of the wall panels actually reduced the plastic deformation of the energy dissipaters in Test RW1-3, especially the SEDPs, leading to a relatively higher self-centering capacity of Test RW1-3 than that of Test RW1-2.
## 4 Numerical Simulations
### Parametric Studies  
Equation (7) shows that the stability of the support structure is mainly influenced by the following four aspects: the thickness of the steel corrugated plate and mold bag concrete, and the excavation depth and excavation radius of the circular shaft. Based on the above factors, the ABAQUS 2D model was used to simulate the forces at the base of the support structure under different working conditions.  
From both the theoretical calculations and the numerical simulations, it has been found that the thinner the material thickness, the greater the value of the two Von Mises stresses; correspondingly, the stability of the support structure decreases, and the law of linear change (shown in Figure 12a,b). At the same time, the radial deformation of the supporting structure decreases slightly with the increase in thickness of the two materials. For every three rings of height excavated downward (2.1 m), the maximum Von Mises stress increases by approximately 1.46 MPa for steel corrugated plate and 0.27 MPa for mold bag concrete (shown in Figure 12c). When the excavation radius increases by 1 m, the maximum Von Mises stress increases by approximately 0.1 MPa for steel corrugated plate and 19 kPa for mold bag concrete (shown in Figure 12d).  
Figure 12: Numerical simulation results: (**a**) thickness of steel corrugated plate, (**b**) thickness of mold bag concrete, (**c**) excavation depth, and (**d**) excavation radius.  
In summary, as the thickness of the steel corrugated plate and mold bag concrete decreases and the depth and radius of the circular shaft excavation increase, all cause an increase in the radial deformation of the support structure, and the maximum Von Mises stress in the structure shows a linear increase in the law of change. The numerical simulation results are in good agreement with the theoretical analysis.
## 2 Algorithm for Optimal Deployment
### Optimal Deployment Algorithm for the Sensor Nodes
\right\} \tag{2}\]  
\[v_{i,\ d}(n+1) =v_{i,\ d}(n)+\ c_{1}r_{1}r_{1}\left[p_{i,\ d}(n)-\ x_{i,\ d}(n) \right]+c_{2}r_{2}\left[p_{g,\ d}(n)-\ x_{i,\ d}(n)\right]+\overset{ \rightarrow}{F_{i}} \tag{3}\] \[\ x_{i,\ d}(n+1) =\ x_{i,\ d}(n)+\ v_{i,\ d}(n+1), \tag{4}\]  
where \(\overset{\rightarrow}{F_{i,\ j}}\) is the resultant force of the attractive force and repulsive force between the \(i\)-th and \(j\)-th sensor nodes. \(\overset{\rightarrow}{F_{i}}\) is the total sum of the forces between the \(i\)-th sensor node and others. \(d_{i,\ j}\) is the Euclidean distance between the \(i\)-th and \(j\)-th sensor nodes, and \(d_{th}\) is the communication threshold, which was defined as the smaller value of the communication radii for the \(i\)-th and \(j\)-th sensor nodesas mentioned earlier. \(\alpha_{i,\ j}\) indicates the direction from the \(i\)-th to \(j\)-th sensor nodes. \(w_{A}\) and \(w_{R}\) are the weighting values for the attractive and repulsive forces, respectively. \(x_{i,\ d}(n)\) and \(v_{i,\ d}(n)\) represent the position and velocity of the \(i\)-th sensor node in the \(d\)-th dimension at iteration \(n\), respectively. \(p_{i,d}(n)\) is the optimal position of the \(i\)-th sensor node until iteration \(n\), and \(p_{g,\ d}(n)\) is the optimal position obtained from the experiences of all sensor nodes until iteration \(n\), which is called the global optimal position. The constants \(c_{1}\) and \(c_{2}\) are acceleration weight constants, and \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1. In this study, the effective communication coverage area was used as an objection function. The pseudocode for the VFPSO used to find the optimal positions for the sensor nodes is illustrated in Figure 8 and its flow chart is shown in Figure 9. The loop finishes when the communication coverage rate sufficiently converges and its standard deviation (s.d.) over 10 consecutive iterations becomes less than 0.5%. Figure 10 illustrates an example showing the convergence progress of the sensor nodes. Through iteration of the VFPSO simulation, the sensor nodes randomly distributed in the targeted area at the initial stage moved toward the optimal positions where network connectivity was well maintained. Figure 8: Pseudocode of the virtual force-particle swarm optimization (VFPSO) algorithm for the search of the optimal deployment positions for the sensor nodes.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 2 The Proposed Method
### Phase I: Generating the QUS Image  
Detecting the ROI in the ultrasound image that includes the needle can be quite challenging, especially when the needle is poorly visible. Additionally, areas with bright linear structures may be incorrectly recognized as the ROI containing the needle. Therefore, it is crucial to develop a robust algorithm to identify the ROI. In this study, a machine learning approach is used to generate a QUS image that facilitates the task of identifying the ROI. The machine learning approach operates by extracting three groups of features from the ultrasound images. The features are subsequently evaluated with a selection algorithm to determine the most relevant ones. Finally, a machine learning classifier, namely the Linear Discriminant Analysis (LDA), is employed to analyze the selected features and generate the QUS image.
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 2 Background
### Core Dynamic Multi-Objective Optimization Algorithm  
The core DMOA used in this study is presented in Algorithm 2. The algorithm starts with a set of randomly generated solutions, after initializing its run-time parameters, such as the population size, maximum archive size, maximum number of iterations, etc. The non-dominated solutions are added to the archive. A loop is performed until the number of iterations exceeds the maximum number of iterations. The non-dominated solutions, which are found at the end of the loop's execution, constitute the final solutions to the associated optimization problem. The algorithm uses sentry solutions to check whether a change in the environment has occurred.  
```
1:procedureDMOA(freq, severity, maxiteration, dMOP)
2:Set population size, N
3:Set archive max size, SizeArchive
4:Initialize the iteration counter, iteration \(\leftarrow\) 0
5:Initialize time, t \(\leftarrow\) 0
6:Initialize(P\({}_{t}\),freq, severity, dMOP, t)\(\triangleright\) initialize population of solutions, \(P_{t}\)
7:AssignNonDominatedToArchive(P\({}_{t}\), dMOP, t)\(\triangleright\) initialize archive
8:whileiteration\(\leq\) maxiterationdo\(\triangleright\) check if stopping condition has been reached
9:t \(\leftarrow\) 1/severity - \(|\) iteration/freq\(|\)\(\triangleright\) calculate the current time
10:Optimizer(P\({}_{t}\), dMOP, t)\(\triangleright\) perform the search optimization
11:Pick sentry solutions \(\triangleright\) select sentry solutions to check for change
12:ifENV changes(P\({}_{t}\), dMOP, t)then\(\triangleright\) check for change in environment
13:ProcessChange(P\({}_{t}\),freq, severity, dMOP, t)\(\triangleright\) respond to change
14:iteration \(\leftarrow\) iteration\(+\)1\(\triangleright\) increase iteration count
```  
**Algorithm 2** Dynamic Multi-objective Optimization Algorithm
# Medical Robotic Bed to Prevent Pressure Sores
## 1 Introduction
An air mattress integrated with a pneumatic pump was developed to estimate body pressure by measuring mattress deformation [15]. [PERSON] et al. proposed an anti-bedsore mattress consisting of multiple air cells and a capacitive pressure sensor. The sensors measure body pressure, and the tension of the air cells is adjusted based on the measured values by using a pneumatic pump [16]. In this study, we propose a novel robotic bed to prevent the formation of pressure sores. This bed consists of multiple independently controlled segments. Each segment is driven using a four-bar mechanism and a brushless direct current (BLDC) motor. The use of an electric motor reduces noise and can eliminate the discomfort caused to patients by pneumatic pump noise in robotic beds that use pneumatic actuators. In addition, in the case of closed-loop control, electric motors offer faster and more precise response characteristics than pneumatic actuators. In the case of open-loop control, excessive pressure may be applied to the body depending on the body type even after controlling. To solve this problem, we implement a closed-loop controller that uses sensors to collect body pressure information. Body pressure is measured using a pressure sensor array composed of force-sensing resistors (FSRs), and the pressure applied to the body is automatically controlled to be lower than the threshold value through fuzzy control. For this purpose, we developed a flexible belt-type pressure sensor. Through actual experiments, the validity of the proposed robot bed is verified. The remainder of this paper is organized as follows: Section 2 presents the details of the proposed bed system, Section 3 presents our experimental results, and Section 4 provides the concluding remarks.
# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 4 Experiments  
### Datasets  
Currently, there are no datasets that provide long videos of MEs and MaEs for ALS patients as the subjects in the datasets. However, there are publicly available long video datasets that can be used to evaluate our proposed method, such as the CAS(ME)\({}^{2}\)[20] and SAMM Long Videos [21], as shown in Table 1, to provide an overview of each of the datasets. Instead of ALS patients, these datasets include healthy individuals who suppressed their true emotions. Long videos exist to simulate real-life scenarios that occur independently and overlap among MEs and MaEs. Thus, these datasets were used for the ME detection task due to the challenging data provided.  
CAS(ME)\({}^{2}\)[20] was the first dataset made accessible to the public that included ME expressions from lengthy videos, and is mainly used for ME detection. Nine emotional videos out of twenty were chosen as the emotion-eliciting materials, and the participants had a mean age of 22.59 years (standard deviation: 2.2 years). It is assumed that each emotion video will primarily evoke a single emotion. Participants in the simulation process sat in front of a 640 \(\times\) 480 [pixels] visible-light camera operating at 30 frames per second in a room lit by two LED lights. The participants were instructed to watch nine videos randomly while maintaining a neutral expression. Rather than classifying expressions into the seven basic emotions, this dataset was limited to four broad categories (positive, negative, surprise, and others), with the \"others\" class lacking a clear definition. Participants with comparable ages were chosen, and footage was recorded at a low frame rate of 30 frames per second.  
SAMM Long Videos [21] is a collection of long videos explicitly created for ME detection in the ME dataset. The lack of existing datasets for MaE detection and spontaneous ME recognition was the driving force behind the creation of this dataset. It is an expansion of SAMM [25], using the information gathered from these trials. The collection includes 159 MEs and 343 MaEs across 147 lengthy videos. It was recorded at 200 frames per second. Every video has comprehensive AUs and is FACS-coded. Each expression's offset, apex, and onset frames are also annotated.
###### Abstract  
As one of the most important applications in peer-to-peer (P2P) networks, the video-on-demand (VoD) system freely supports video cassette recorder (VCR) operation for users. However, the users may experience significant playback delay after frequent VCR operations in the VoD system, which will affect the quality of experience (QoE) of the users. Hence, selecting an appropriate data-prefetching strategy to support better VCR operation is an important approach to improve the QoE. This paper proposes a data-prefetching strategy (DSA) to determine the most suitable anchor interval by considering the playback delay and positioning satisfaction. According to the DSA, we use the multiple-attribute decision-making (MADM) theory to model the selection of intervals of prefetching data blocks (i.e., anchor interval) and the technique for ordering preference by similarity to an ideal solution (TOPSIS) algorithm to solve the MADM. The simulation results show that the DSA strategy obtains higher positioning satisfaction than the existing schemes, which is approximately 60% higher than the anchor points, popular parts of video, and user interests (API)-based method. Moreover, with the increase in network bandwidth, the DSA strategy can minimize the playback delay after VCR operation using relative few extra bandwidths.  
video-on-demand (VoD); video cassette recorder (VCR); multiple-attribute decision-making (MADM); playback delay; positioning satisfaction +
Footnote †: journal: Sensors
## 4 Experimental Set Up, Test and Results
### Layer 1-2 Tests
First of all, we tested the synchronization recovery procedure both changing in the sensor cards the time to send data and changing in the hub the watchdog delay. In both cases, the system synchronizes itself again. Then we simulated a sensor card broken, switching one of them off. Even in this case the system correctly works sending an error message to the server. Other tests aimed to verify the rightness of the data coming from the sensors, this is the reason why, a PCE-FWS 20 [57, 58] Meteorological Station (MS) has been used. Within the uncertainty range of the sensors and of the MS, the measurements realized with our systems were similar to the MS ones.
## 4 Experiment
### Control Experiment  
Initially, the PZT actuator is supplied with a voltage, from 0 to 450 VDC, according to the supplier's instructions, to calibrate its unload displacement capability. The noncontact displacement sensor LK-G30 measures the output displacement of the PZT. Figure 16 shows the measurement results. This measurement result inherently indicates that the character of the piezoelectric actuator is rate-independent hysteresis. This is the major limitation of all piezoelectric actuators, which needs to be appropriately compensated for by the control system to ensure good positioning accuracy.  
To validate the operability of the mechanism, an experimental open-loop control model, as shown in Figure 14, is operated. Two noncontact laser sensors, (1) and (2), record the output displacement and parasitic motion. The LK-3001P controller, connected to a computer via the USB port, transmits the measurement results. The software LK-Navigator 2 (Keyence, Osaka, Japan) stores and displays the measurement results on the computer. Figure 17 illustrates the open-loop displacement control results, which show that the  
Figure 16: Unload PZT displacement measurement: (**a**) setup and (**b**) measurement data.  
experimental displacement measurement does not exhibit a proportional relation as does the theoretical one. Thus, a more precise control method is necessary.  
As shown in Figure 15, the full closed-loop control model uses an additional NI MyRio 1900 card to provide feedback on the output displacement signal. This study uses the GA-PID algorithm in MATLAB and LabVIEW to implement the closed-loop control. The single-objective GA algorithm makes finding the optimal set of PID parameters easy. The values of those parameters are \(K_{P}=13.0166\), \(K_{I}=0.00753268\), and \(K_{D}=0.0019387\). Figure 18 shows the relation between the input and output displacement of the closed-loop control by the GA-PID algorithm, which is more linear than in Figure 17. Figure 19 shows the set value, actual value, and impulse response when different input waveforms (sine, triangle, and square wave) are applied to the PZT actuator. The responses have an error of less than 0.025 \(\upmu\)m. This result shows that the control system has high accuracy and responsiveness. In comprehensive research, a more detailed analysis of the current PID control algorithm and procedure, or application of other modern control methods to evaluate the performance and effectiveness of the mechanism, should be presented. For simplicity and generality, these issues will be discussed elsewhere, as they are beyond the scope of the current paper.  
Figure 17: Open-loop displacement control results.  
Figure 18: Experimental results of input/output displacement in closed-loop control.
## 4 Effects of \(C_{y}\) and \(T\) on \(S_{d}\) Spectrum
### Analysis of the \(S_{d}\) mean spectrum  
The \(S_{d}\) mean spectrum under different \(C_{y}\) conditions is calculated based on two different hysteretic models. The damping ratio is 0.05. The post-yielding stiffness is 0. The calculated results are shown in Figure 3. The following conclusions can be drawn from the results.  
(1) The variation law of the \(S_{d}\) mean spectrum under different \(C_{y}\) conditions is the same with the same \(T\) based on the two hysteretic models. The larger the displacement response mean spectrum is.  
(2) Although the variation law of the \(S_{d}\) mean spectrum increases with the increase in \(T\), the change speed of the \(S_{d}\) mean spectrum is obviously different under different \(C_{y}\) conditions. The smaller the \(C_{y}\) is, the greater the nonlinear degree of the structure is, and the larger the \(S_{d}\) mean spectrum is in the short- and medium-period ranges, but the smaller the \(S_{d}\) mean spectrum is in the long-period range.  
(3) When \(C_{y}\) is equal to 0.1, the change law of the \(S_{d}\) mean spectrum is quite different from that under other \(C_{y}\) conditions. The main reason is that when \(C_{y}\) is equal to 0.1,the yield strength of the structure is only 10% of that of full elasticity, and the character of the SDOF system is too flexible. In addition, it should be analyzed separately.