esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.9 Characterizations  
PLA electrospun fibers were characterized by tensile tests using a universal testing machine (CT3-1000 Texture Analyzer, Brookfield, Middleboro, MA, USA). Wettability of PLA fibers was characterized using a contact angle meter (Holmarc HO-IAD-CAM-01, Kalamassery, Koshi, India), depositing 10 \(\upmu\)L of water on the surface of the material. The drop was analyzed using the Image J software possessing the contact angle plugin (NIH, USA). Thermomechanical analysis of 3D-printed scaffolds and composites was done in a Dynamic Mechanical Analyzer (DMA8000, Perkin Elmer, Waltham, MA, USA), under dual cantilever geometry. Storage and loss modulus, and tan \(\delta\) were measured in a temperature scan program from 25 to 120 \({}^{\circ}\)C, heating at 2 \({}^{\circ}\)C/min, applying a static force of 2.0 N, 1 Hz of frequency, and 0.05 mm of tension. Glass transition of PLA-based materials was assessed by tan \(\delta\) maximum position in the temperature scan program. Data were analyzed by OriginPro software. The surface of the obtained materials was studied by scanning electron microscopy (FIB-SEM Auriga Compact, Zeiss, Aalen, Germany), by sticking a small material sample in a SEM stub using a conductive carbon adhesive tape ([PERSON], Redding, USA). Samples were covered with a thin layer (1 nm) of gold by sputtering (Leica EM ACE 600) and were analyzed by SEM at 3.00 kV. Samples for SEM-EDS analysis were coated with a 10 nm layer of carbon (Leica EM ACE 600) and were analyzed at 20.0 kV. Changes in molecular weight distribution of PLA composites were studied by gel permeation chromatography (GPC, Jasco, Japan), equipped with a refractive index detector (RI-4030, Jasco) and a divinylbenzene-based column (DVB column, [PERSON]) enclosed in a column oven at 40 \({}^{\circ}\)C (CO-4060, Jasco). Around 100 mg of the composite was dissolved in 10 mL of chloroform for liquid chromatography (Merck), keeping the samples in an orbital shaker at 37 \({}^{\circ}\)C for 24 h. After that, samples were filtered with 0.22 \(\upmu\)m PVDF filters and diluted with chloroform for liquid chromatography at a 5 mg/mL final concentration. Samples were injected into the GPC running in chloroform as a mobile phase at 1 mL/min. Mass average molecular weight (M\({}_{\mathrm{w}}\)), number average molecular weight (M\({}_{\mathrm{n}}\)), and polydispersity index (PDI), were calculated using ChromNAV-GPC software (Jasco), applying a molecular weight calibration curve constructed using different narrow polymethylmethacrylate (PMMA) as standard (ReadyCal Kit, Polymer Standard Service GmbH).
# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 2 Accuracy Assessment Based on GRA  
The principle of accuracy assessment based on grey relational analysis involves constructing a grey relational degree-mean square deviation distance model and an accuracy-mean square deviation distance model. The simulation test data and physical test data are substituted into the grey relational degree-mean square deviation distance model. The resulting mean square deviation distances are then converted into accuracy values through the mean square deviation distance-accuracy model.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Stability Comparison
#### 3.1.1 HSNM Comparison  
The Hold Static Noise Margin (HSNM) is obtained by plotting the Voltage Transfer Characteristics (VTCs) of the cross-coupled inverters while the cell is in the hold state. This creates a \"butterfly curve\", and the HSNM is the side length of the largest square that fits  
Figure 4: Waveforms of SEU and SEDNU recovery of the proposed RHLP-18T cell.  
inside the curve's two lobes. As Figure 5 shows, the RHLP-18T has an HSNM of 173 mV. This is somewhat lower than cells like QCCS12T (278 mV) and RHWC12T (281 mV), though better than SIMR18T (137 mV). This moderate HSNM might be due to the cell's longer internal feedback paths and the weaker nodes created by transistors P9 and P10 compared to a standard 6T cell--representing a likely trade-off to improve other characteristics.
## 5 Experimental Results
### Results
#### 5.4.3 LMVSegRNN Outperforms Learning-Based Geometric Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline
**Seg. Method** & \multicolumn{2}{c}{**Basic Cases**} & \multicolumn{2}{c}{**Complex Cases**} \\  & \(\mathbf{W\text{-}IoU\uparrow}\) & \(\mathbf{d_{H95}\downarrow}\) & \(\mathbf{W\text{-}IoU\uparrow}\) & \(\mathbf{d_{H95}\downarrow}\) \\ \hline PointNet (_w/o n_) & \(0.822\pm 0.065\) & \(1.959\pm 1.611\) & \(0.785\pm 0.090\) & \(2.437\pm 1.231\) \\ PointNet (_w. n_) & \(0.928\pm 0.021\) & \(1.120\pm 1.773\) & \(0.921\pm 0.039\) & \(1.309\pm 1.262\) \\ PointNet++ (_w/o n_) & \(0.914\pm 0.043\) & \(0.983\pm 1.522\) & \(0.907\pm 0.058\) & \(1.396\pm 1.363\) \\ PointNet++ (_w. n_) & \(0.943\pm 0.053\) & \(1.033\pm 1.039\) & \(0.935\pm 0.051\) & \(1.022\pm 1.129\) \\ SpMeshCNN (_w/o ab_) & \(0.923\pm 0.095\) & \(0.784\pm 1.225\) & \(0.920\pm 0.092\) & \(0.893\pm 1.103\) \\ SpMeshCNN (_w. ab_) & \(0.948\pm 0.063\) & \(0.623\pm 0.794\) & \(0.918\pm 0.057\) & \(0.858\pm 1.074\) \\ Graph Cut & \(0.961\pm 0.184\) & \(1.013\pm 2.816\) & \(0.945\pm 0.109\) & \(0.985\pm 1.794\) \\ LMVSegRNN & \(\mathbf{0.976\pm 0.084}\) & \(\mathbf{0.458\pm 0.993}\) & \(\mathbf{0.971\pm 0.038}\) & \(\mathbf{0.490\pm 0.571}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of semantic tooth segmentation between LMVSegRNN and existing methods using _Poseidon3D_ dataset. By \(n\), we refer to normal vectors as a part of input features. By _ab_ (attention blocks), we refer to incorporating edge self-attention blocks in the two bottom stages of SparseMeshCNN. Results in bold are for the best-performing method.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 9 Experimental Results
### Relative Pose Measurement Experiment  
In these experiments, we investigated the effect of CL when relative pose measurements are only available for a very short period. For the first case (no CL), each vehicle propagates its own states for 40 s. Starting at 40 s, vehicle 3 measures its own pose for 1 s, after which there are no other measurements. In the second case (CL), the same scenario is repeated except that between 40 s and 41 s, vehicle 3 simultaneously measures the relative pose of vehicle 2 and 1 in addition to its own pose.  
Figure 11 compares the time history of the position error norms of the two cases in this experiment for all three vehicles. As expected the performances for all three vehicles are identical for the first 40 s and vehicle 3's localization is similar throughout. However, despite only 1 s of relative measurements (i.e., five samples), vehicle 1's position estimation is dramatically improved. Vehicle 2, on the other hand, does not initially benefit from CL. However, as time passes, the localization errors start decreasing for CL while increasing for the \"no CL\" case.  
Figure 10: Overview of a mobile robot used for experiment.
###### Abstract  
As urban underground spaces continue to develop, new engineering projects are increasingly closer to existing operating tunnels. This study focuses on the impact of excavation work, such as a new tunnel or deep excavation, on an existing shield tunnel. However, few studies have examined the simultaneous effects of excavation and over-crossing tunneling on operational tunnels. This paper presents a case study of Section 2 of Hangzhou Metro Line 7, employing MIDAS GTS to simulate the settlement of the existing tunnel and analyze the data collected through automatic measurements. Both methods demonstrated that excavating a foundation pit near the existing tunnel results in the tunnel's settlement while over-crossing tunneling of the new tunnels leads to the uplift of the existing tunnel. The excavation prompted the existing tunnel to move horizontally toward the foundation pit, whereas over-crossing tunneling pushed it horizontally away. The numerical simulation revealed minor vertical displacement but significant horizontal displacement, aligning closely with the measured data. The impacts of foundation pit excavation and over-crossing tunneling on the vertical and horizontal displacement of existing tunnels are contrary, suggesting that the two construction stages will offset each other.  
Keywords:existing shield tunnel; measured data; numerical simulation; multiple disturbances +
Footnote †: journal: Applied sciences
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Imaging Setup  
The imaging setup consists of the proposed breast phantom surrounded by transmitting/receiving antenna elements. The number of antennas used for imaging affects the accuracy of the reconstructed image. In this paper, the modified Vivaldi antenna is used as a radiating/receiving element. Nine antenna elements are used as radiating/receiving elements. The placement of the antenna around the breast phantom is critical in this design since the modified antenna has a directional radiation pattern. The nine antennas are placed at an equal distance from each other and 10 mm from the phantom, with the radiating side of the antenna facing the phantom, as shown in Figure 19. The distance from the center of the phantom to the center of each antenna element is \(r_{3}\) = 67.5 mm, and the distance between adjacent antennas in the array is 46.2 mm (center to center). The setup is illustrated in Figure 19 with antennas numbered Antenna1-Antenna9. The fidelity of the modified antenna in the imaging setup was investigated. A signal centered at 4.3 GHz is transmitted from Antenna1 and received by all other antennas. The procedure is repeated nine times with Antenna2, Antenna3,... and Antenna9 as transmitting elements and all others as receiving elements. Backscattered signals are collected in the frequency range of 3.65 to 5.2 GHz. Table 4 shows the minimum reflected signal magnitude of S12, S13,..., S21, S23,..., 597, S98 with and without the presence of the tumor inside the breast phantom. The result shows that the presence of the tumor increases the reflected signal magnitude due to the presence of high-dielectric material. Moreover, a weaker signal magnitude is received when the tumor is not present in the breast phantom. The presence of tumor is very noticeable over the frequency range of 4.1 to 4.7 GHz. The imaging is performed in air; a coupling medium between the array and the phantom is not used.  
Figure 19: Imaging Setup using Modified Antenna.
# The Potential of Indole Alkaloids in Bone Health and Osteoporosis Management
## 3 Diagnosis  
The BMD (bone mineral density) test is used to determine the quantity of minerals in one square centimeter of bone, or bone mineral density. The findings are contrasted with the bone mineral density of a reference value that has been established, namely the bone mineral density of a young adult in good health, between the ages of 25 and 30, who has attained a bone mass, to which a T-score is awarded. A 1994 technical study from the World Health Organization (WHO) determined the T-score values of BMD within which an individual can be classified as normal, with reduced bone mass (osteopenia), or with osteoporosis. The WHO established the subsequent diagnostic standards based on comparisons with the average peak bone mass in young adults:  
* Normal bone mineral density: BMD within 1 standard deviation (SD) of the average peak bone mass in young adults.
* Osteopenia: BMD between \(-1\) and \(-2.5\) SD below the average peak bone mass in young adults.
* Osteoporosis: BMD more than 2.5 SD below the average peak bone mass in young adults.
* Severe osteoporosis: BMD more than 2.5 SD below the average peak bone mass in young adults, along with one or more fragility fractures.  
These standards, which pertain to postmenopausal women, were established by utilizing dual-energy X-ray absorptiometry (DXA) data from bone density assessments. It is estimated that at least 30% of postmenopausal Caucasian women have osteoporosis at risk of fracture using a cut-off of \(-2.5\) SD of bone mass [3; 7; 30; 31; 32; 33; 34].
## 3 Experiments and Results
### Audio-Guided Space-Time Memory Network  
In this section, based on the network architecture and process outlined in the preceding section, the construction of the audio-guided space-time memory network (AG-STMNet) is described. Following the training of the network, it was rigorously tested and validated. Subsequently, the mask generator and AG-STMNet were integrated into a complete target tracking framework to evaluate the overall performance of the proposed method.
## 5 Results and Discussion  
### Model Comparison  
Firstly, we eliminate the multicollinearity effect of data. We use the variance inflation factor (VIF) to judge whether there is multicollinearity between independent variables. The larger the VIF, the more serious the multicollinearity problem is. If the VIF exceeds 10, the multicollinearity of the data needs to be processed. As shown in Table 2, the average VIF value in this paper is 2.74, of which the maximum VIF value is 6.5, less than 10, so there is no need to worry about the multicollinearity between independent variables. The specific VIF test value is in Table 2.  
To avoid over-fitting and reduce generalization error, we use a five-fold cross-validation procedure to train the model, in order to get the best parameter setting and result. In this paper, the parameters are listed as follows: the tree complexity is 10, the shrinkage parameter is 0.01, and the number of iterations and maximum trees is 10,000. After running the XGBoost model, we obtained the final results, with the minimum RMSE and R\({}^{2}\) of approximately 0.821.  
To examine the advantage of XGBoost over traditional linear and nonlinear models, we conducted a comparison analysis between the traditional-linear model, random forest, LightGBM, and XGBoost, based on mean absolute error (MAE) and mean square error (MSE). The result shown in Table 3 indicates that the R\({}^{2}\) of XGBoost model is 0.821, the mean absolute error (MAE) is 0.098, and the mean square error (MSE) is 0.017. The MAE and MSE of XGBoost are much smaller than other linear or nonlinear models, suggesting that XGBoost has a better fitting degree. The XGBoost model has the maximum R\({}^{2}\) value. Thus, this paper applies XGBoost to reveal the non-linear impact of built-environment variables on bus-metro-transfer ridership. The model can identify the rank of relative importance for the influential factors and improve the interpretability of the decision-making framework.
## 3 Results and Discussion
### Phase 8: Confusion Matrices (2-by-2 Tables)
In this case, its Overall Accuracy is quite low (56.90%) though. When considering the separate analysis of the Oil-Slick Information Descriptors (i.e., Size and SAR), Set.3 and Set.4 (see Figure 4: without PCA and with PCA, respectively) present the same result--these are shown in Table 7. The foremost outcome revealed in Table 7 is that the sole use of SAR Information Descriptors (AVG with SKW) is not as effective as using only Size Information Descriptors (Area with Perimeter and PtoA with Compact). Table 7 also discloses that these two pairs of Size Information Descriptors have the same results in the Logarithm function (NLT.2), and in fact, these results present superior discrimination power than in the other two analyzed NLTs, i.e., NLT.0 (No Transformation) and NLT.6 (Cube Root). Slightly better Overall Accuracies are achieved when using Area with Perimeter than PtoA with Compact; however, one should note that the False Negatives of the former pair are much higher than those of using the second pair: 67.7% against 21.0% (NLT.0), and 43.4% against 28.9% (NLT.6). We can also evaluate the results of using several variables (Tables 5 and 6) against the use of individual pairs of attributes, i.e. the separate analysis of Size and SAR Information Descriptors (Table 7). If one compares the outcomes of NLT.2 (Log\({}_{10}\)) in Tables 5-7, it is possible to notice that the sole use of the two Size Information Descriptor pairs (Table 7) has equivalent results as the ones from the other two attribute selection strategies, i.e., no UPGMA variable selection with PCA (Set.2: Table 5) versus UPGMA selected variables without PCA (Set.3: Table 6).
# An IoT System for Remote Monitoring of Patients at Home
## 7 Experiments
### Experiment on the MQL Scheduling Algorithm  
In the experiments to test the MQL scheduling algorithm, four types of PHDs were used to generate pulse oximetry data, ECG data, blood sugar data, and weight data. ECG data, the heaviest data of all, were obtained by gathering 300 data samples per second for 5 ms. Each result in the following graphs was obtained by averaging the results of ten experiments. The characteristics of four types of biomedical data used in these experiments are as follows:  
* Pulse oximetry data: Message class (Urgent), Message priority (5.0), Messages size (64 bytes)
* ECG data: Message class (Urgent), Message priority (3.0), Messages size (1024 bytes)
* Blood sugar data: Message class (Non-urgent), Message priority (3.0), Messages size (64 bytes)
* Weight data: Message class (Non-urgent), Message priority (1.0), Messages size (64 bytes)  
To compare the proposed MQL scheduling algorithm with other multiclass message scheduling algorithms, the MBDP algorithm was implemented. For the MBDP algorithm, the following maximum delay times were utilized: 0 second for urgent data, 50,000 seconds for blood sugar data, and infinity for weight data. Figure 14 shows the throughputs (the number of messages scheduled for transmission) generated by the MQL and the MBDP algorithms.  
As seen in Figure 14, the MQL scheduling algorithm proposed in this study performs better than the MBDP scheduling algorithm. In addition, we found that the throughputs of the MQL algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the MBDP algorithm increase with decreases in the increasing ratio. This is partially because the MQL algorithm  
Figure 14: Throughputs of the MQL (Multiclass Q-learning) and the MBDP (Multiclass Based Dynamic Priority) scheduling algorithms.  
performs significantly better than the MBDP algorithm, especially when the number of messages waiting to be scheduled becomes quite large. As measurement time increases, the number of messages waiting to be scheduled also increases. Compared to the MQL algorithm, the MBDP algorithm incurs greater overhead when recalculating the RTTS of every message in the waiting queues, and when moving messages between queues upon recalculation.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
###### Abstract  
A quasi-Yagi slotted array antenna with fan-beam characteristics is proposed for 28 GHz 5G mobile terminals. The antenna is composed of a \(1\times 8\) slot antenna array with directors to enhance the half-power beamwidth (HPBW). The proposed antenna has a fan-beam radiation pattern with a simulated HPBW of 256.72\({}^{\circ}\) and a peak gain of 11.16 dBi. In addition, the proposed antenna covers \(\pm\)48\({}^{\circ}\) using a beam steering mechanism. Mutual coupling reduction is achieved by inserting slits between the adjacent slot radiators on the ground plane. The simulated \(-\)10 dB reflection coefficient bandwidth of the proposed antenna is 1.79 GHz (27.03-28.82 GHz), and the mutual coupling between each of the slot radiators is lower than \(-\)25.02 dB over the 28 GHz target band (27.5-28.35 GHz). To investigate the effect of a human body in a practical environment, the power density was considered to estimate the electromagnetic exposure with a simplified skin model. The measured results were in good agreement with the simulated ones and demonstrated that the proposed antenna could be used for 5G mobile terminals.  
Keywords:fifth generation (5G); millimeter-wave; phased array; power density; fan-beam +
Footnote †: journal: Applied Sciences
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Synthetic Data
#### 3.1.2 Evaluating the Performance of Pose Estimation  
Performance of position determination was evaluated by comparing the calculated reference-camera-to-target transformations (\(T_{Cl_{i}}^{W}\)) to the ground truth. We calculate the translation error (\(e_{t}\)) between the estimated and ground truth rigid body transformations as the Euclidean distance in each of the two axes that are parallel to the target plane. Since it is not relevant for the alignment task, we disregard the depth axis.  
The rotation error (\(e_{R}\)) is calculated as the absolute difference between the magnitude of the angle around the rotation axis (as defined in the axis-angle representation). At this stage, we use the ground truth feature point coordinate values. We evaluate the performance of monocular and stereoscopic estimates for baseline values of 100, 150, 200, 250, and 300 mm. We observed that using ground truth feature points, errors are in the range of \(10^{-5}\) mm in translation and \(10^{-5}\) degrees in rotation for all approaches, which is considered to be insignificant. Therefore, in the following experiments, random noise with a uniform distribution and several range values was added to the feature point coordinate values, and the results were averaged over 20 tests.  
In Figure 11, we present position estimation errors \(e_{t}\) and \(e_{R}\), averaged over the 20 positions of the synthetic dataset for added noise in the range of \(-0.5\) to \(0.5\) pixels. We can see that stereoscopic estimation produces the best results and performs better as the baseline increases. We can also see that standard deviation follows the same trend, showing that more consistent results are found with stereo, particularly for higher baseline values.  
In Figure 12, we compare the performance of monocular and stereoscopic approaches for the smallest and largest considered baselines as a function of the added noise level. We see that stereoscopic estimation performs best and is the most robust to added noise. We observe that the error for approaches using stereo vision becomes lower as the cameras' baseline increases for all considered noise levels.  
We can consider in the application a baseline range of 100-300 mm and a maximum expected corner noise level of two pixels. In these conditions, the difference in translation and rotation error between monocular and stereoscopic estimates is less than 0.1 mm and 0.1 degrees.  
Figure 10: Error in the X and Y axes corresponding to the detection of marker centroids (x) and checkerboard corners (*) in synthetic data. Each data point represents an image feature point.