esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Four-Dimensional Cardiac CT Analysis in 3D Slicer
#### 2.2.2 Multiparametric Measurements of the Landing Zone  
Dynamic landing zone measurements, which included cross-sectional area, circumference, maximum diameter, and minimum diameter in the pre-CTs, were taken in five planes at the landing zone: plane 1--RVOT (10 mm below plane 2), plane 2--BPV plane (three hinge points at the nadir of each of the attachments of the PV), plane 3--sinus plane, plane 4--sinotubular junction (STJ) plane, and plane 5--PA plane (10 mm above plane 4). The five planes in the post-CTs were defined as follows: plane 1--RVOT (10 mm below plane 2), plane 2--bottom plane of the stent (BPS) plane, plane 3--middle plane of the stent (MPS) plane, plane 4--top plane of the stent (TPS) plane, and plane 5--PA plane (10 mm above plane 4) (Figure 1). The RV volume was obtained by cutting the right heart segmentations at the tricuspid valve level and at the level of plane 4 both in the anatomical and the straightened segments. The right ventricular ejection fraction (RVEF) can be calculated using the minimum and maximum RV volume. The volume of the landing zone in the pre- (PV volume) and post-CTs (stent volume, SV) was obtained by cutting the right heart segments from plane 1 to plane 5 both in the anatomical and the straightened segments. The rendered stent volume in the post-CTs was created by masking the right heart volume between plane 2 and 4 for the entire cardiac cycle. The ratio between the minimum and maximum diameter in each section was calculated to determine the grade of the elliptical shape (ellipticity index). The correlation between the anatomical model and straightened model was assessed using the Pearson correlation coefficient. We calculated the \(r\) value for the Pearson correlation coefficient using Formula (1),then obtained the \(t\) value using Formula (2). In the case where the degrees of freedom (d.f.) for \(r\) is \(n-2\), the \(p\) value can be obtained using the critical values of \(t\) for Pearson's \(r\).  
\[r =\frac{n\sum\ x_{i}y_{i}-\sum\ x_{i}\sum\ y_{i}}{\sqrt{n\sum\ x_{i}^{2}- \left(\sum\ x_{i}\right)^{2}}\sqrt{n\sum\ y_{i}^{2}-\left(\sum\ y_{i}\right)^ {2}}} \tag{1}\] \[t =\frac{r}{\sqrt{\frac{1-r^{2}}{n-2}}} \tag{2}\]  
Figure 1: Five selected planes from different segmentations. (**A**) anatomical model from pre-operative CT, (**B**) straightened model from pre-operative CT, (**C**) anatomical model from post-CT, (**D**) straightened model from post-CT. PA pulmonary artery, STJ sinotubular junction, BPV basal plane of pulmonary valve, RVOT right ventricular outflow tract, TPS top plane of the stent, MPS middle plane of the stent, BPS bottom plane of the stent.  
Bland-Altman analysis was conducted to further confirm the agreement between the two models.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Prediction Results of Worldwide Ocean Weather  
We proposed a learning model for predicting ocean weather using the ConvLSTM model and denoising AutoEncoder. In addition, through the case studies in Section 4.4, the accuracy of the learning models according to various look back and look forward ranges was compared and analyzed. Based on the comparison, the model used in Case 9 was applied to training the models worldwide. As described in Section 3.2, the eight parameters of data used in this study consist of data that have 6-h intervals and spatial intervals of 0.75\({}^{\circ}\) in longitude and latitude. If the 0.75\({}^{\circ}\) interval data are used for one region shown in Figure 11, an image size of 120 \(\times\) 30 pixels should be applied. This induced practical difficulty in training because of the specifications of Table 3 in terms of memory. Therefore, data were organized at intervals of 1.5\({}^{\circ}\) in longitude and latitude. Finally, training was performed using an image of \(60\times 30\) pixels per area. In addition, as described in Section 4.4, three models per area in the 12 areas were used for prediction, and 36 prediction models were used to predict the ocean weather worldwide. In the case of look back, 84 days of data (12 weeks) was used, and a model for predicting after 7 days was constructed. Table 6 shows the prediction accuracy for the eight parameters of ocean weather in the 12 areas. In Table 6, the area with the lowest error is shown in blue, and the area with the highest error is shown in red, for each parameter of ocean weather. In addition, the area with the lowest average error was marked with blue shade, and the area with the highest error was marked with a red shade. The average error for the eight parameters of data worldwide was 6.7%, and the results were quite different in each area, ranging from 4.1% to 9.7%. When comparing the error rates for each area, it can be seen that Areas 1, 3, 4, and 6 have higher average errors than the other areas. This is because it is challenging to consider spatial influences in learning because the land is included in these areas. However, in Areas 10 and 11, the error rate is significantly higher than in other areas, even though these areas contain little land. This is because, despite the fact that the area includes a large ocean area, the ocean weather varied so much that the learning model could not grasp the pattern correctly.
## 4 Materials and Methods
### Experimental Setup  
To provide participants with a three-dimensional presentation of information, the visual stimuli were demonstrated on the volumetric multi-plane display (model x1406), produced by LightSpace Technologies. The major components of the volumetric display are the high-speed projector and the multi-plane optical element [46]. The optical element is a stack of twenty air-spaced planes, containing liquid crystal diffusers which are electrically switchable between light scattering and transparent modes. The width of one plane was 1.1 mm. The distance between two sequential planes was 5.0 mm. To produce the three-dimensional image, the two-dimensional images were projected on the diffuser elements, which were consecutively switched off with a refresh rate of 60 Hz. The effect of a three-dimensional image was thus produced by demonstrating two-dimensional images in different locations within the display at a high rate. The resolution of the display was \(1024\times 768\) pixels (X, Y) per layer, providing a volumetric resolution of 15 million voxels. The length and width of each plane was \(39.5\times 29.5\) cm.  
The participants sat in front of the display at a viewing distance of 60 cm from the frontal surface of the display, with a keyboard in their hands, allowing them to submit their answers and perform image manipulations. The experiment was completed in a dimly lit room.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Ablation Experiments
#### 3.3.2 Different Attention Mechanisms  
To further demonstrate the effectiveness of the proposed global-local information attention (GLIA) mechanism, we replaced our attention module with other attention mechanisms. As presented in Table 5, the GLIA obtained the highest score (68.2%) on the UAVid test set compared to using other attention mechanisms. In addition, our attention mechanism performed better on the other three metrics. Specifically, our attention mechanism improved the speed by 49 and 46, respectively, when compared to the efficient MSA and SWA in the Transformer, which also proved the superiority of the proposed GLIA.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
###### Abstract  
In this paper, we characterized and compared signal transmission performances of traces with different specifications of fiber weave. Measurements demonstrated that the dielectric constant, impedance fluctuation, and differential skew were all affected by fiber weave style. For flattened fiber weaves, the dielectric constant fluctuation reached 0.18, the impedance fluctuation amplitude was 1.0 \(\Omega\), and the differential skew was 2 ps/in. For conventional fiber weaves, the three parameters were 0.44, 2.5 \(\Omega\), and 4 ps/inch respectively. Flattened fiber weave was more favorable for high-speed signal control. We also discussed the other methods to improve the fiber weave effect. It turned out that NE-glass (new electronic glass) fiber weave also had better performance in reducing impedance fluctuation and differential skew. Furthermore, made the signal traces and fiber weave bundles with an angle or designing the long signal line parallel to the weft direction both are simple and effective methods to solve this problem.  
fiber weave effect; high-speed; impedance fluctuation; differential skew Article
## 3 Methods and Simulation
### Calculation of the Electric Field with COMSOL  
In order to electrostatically drive the \({}^{222-220}\)Rn progeny ions on the silicon detector, a specific high voltage (\(V\)) is applied to the metallic walls of the chamber to generate an electric field (\(\overset{\rightarrow}{E}\)) between them and the detector surface, according to the following PDEs:  
\[\overset{\rightarrow}{E}=-\overset{\rightarrow}{\
abla}V \tag{1}\]  
\[\overset{\rightarrow}{\
abla}\cdot(\varepsilon_{0}\,\varepsilon_{r}\overset{ \rightarrow}{E})=\rho\ [\text{Gauss's law}] \tag{2}\]  
where \(\varepsilon_{0}\) and \(\varepsilon_{r}\) are respectively the dielectric constants in vacuum and relative to the material, and \(\rho\) is the volume charge density [25]. In our case, high voltage equipment, space charges due to ions are negligible (\(\rho=0\)) and therefore the equation to be solved for the dielectric media is:  
\[\overset{\rightarrow}{\
abla}\cdot[\,\varepsilon_{0}\,\varepsilon_{r}\ (- \overset{\rightarrow}{\
abla}V)]=0. \tag{3}\]  
The program solves (3) for voltage (potential) distribution \(V\) over user defined domain with sources and boundary condition.  
From the simulation point of view, in COMSOL it is necessary to reproduce the same geometry and materials of the measurement system defined in Geant4, so that the dimensions coincide. This is essential in order to avoid code errors of the interaction between the two simulation tools. The COMSOL panel control is well organized and particularly intuitive: with the 'Model Tree' command it is possible to edit each step of the whole simulation process, i.e., defining 3D-geometry and materials of the system, specifying the PDEs that describe the physical phenomenon, solving the PDEs and then obtaining the solutions. By using _Geometry Tool_ of 'Model Tree' command it is possible to introduce several geometrical shapes (such as blocks, cones, cylinders, spheres) for the realization of the system components. The materials of the objects previously created is performed using the _Material Library_ provided by the software. A defined high voltage is applied to the metallic walls of the chamber, and the optimal bias voltage (to ground) is applied to the detector (25 V). The involved PDEs, above described, for the generation of the electric field are selected by _Electric Potential Tool_. Then, it is possible to start the simulation by _Run_ command. The data that the user can save and plot are stored in the _Results_ section of the software. COMSOL simulates the flow rate of air sampling assembly, electric field map and particle trajectories in the 3D-geometry. Using the _3D Plot Group_, the simulated potential distribution and the electric field spatial distribution can be obtained respectively. The electric field map, to be used in Geant4, is exported to a text-file by using the command _Copy Plot Data to Clippboard_. The field map contains the coordinates and the value of the electric field in the chamber volume.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Antibiotics** & \multicolumn{2}{c}{_E. coli_ **(n = 30)**} & _Salmonella_ **sp. (n = 33)** & _Campylobacter_ **sp. (n = 16)** & _Shigella_ **sp. (n = 4)** \\ \cline{2-7}  & **\% S** & **\% R** & **\% S** & **\% R** & **\% S** & **\% R** \\ \hline Ampicillin & 96.7 & 3.3 & 60.6 & 39.4 & 81.3 & 18.8 & 75 & 25 \\ Augmentin & 96.7 & 3.3 & 69.7 & 30.3 & 62.5 & 37.5 & 25 & 75 \\ Amoxicillin & 80 & 20 & 36.4 & 63.6 & 62.5 & 37.5 & 25 & 75 \\ Nalidixic acid & 90 & 10 & 81.8 & 18.2 & 18.8 & 81.3 & 100 & 0 \\ Tetracycline & 76.7 & 23.3 & 87.9 & 12.1 & 87.5 & 12.5 & 25 & 75 \\ Gentamicin & 100 & 0 & 81.8 & 18.2 & 68.8 & 31.3 & 100 & 0 \\ Ciprofloxacinoxacin & 100 & 0 & 100 & 0 & 68.8 & 31.3 & 100 & 0 \\ Erythromycin & 6.7 & 93.3 & 0 & 100 & 81.3 & 18.8 & 0 & 100 \\ Co-trimoxazole & 96.7 & 3.3 & 81.8 & 18.2 & 12.5 & 87.5 & 100 & 0 \\ Chloramphenicol & 70 & 30 & 90.9 & 9.1 & 87.5 & 12.5 & 25 & 75 \\ \hline \end{tabular} n, number of isolates for each bacteria; S, susceptible; R, resistant.  
\end{table} 
Table 2: Antibiotic of the confirmed bacterial isolates recovered from dairy manure in the Eastern Cape Province of South Africa. \begin{table}
\begin{tabular}{c c c c} \hline
**Bacterial Isolates** & \multicolumn{2}{c}{**MAR (Frequency of Determination \%)**} & **Number of Isolates** \\ \cline{2-5}  & \(\leq\)**0.2** & **\(>\)0.2** & \\ \hline _Salmonella_ sp. & 42.42 & 57.58 & 33 \\ _E. coli_ & 76.67 & 23.33 & 30 \\ _Campylobacter_ sp. & 31.25 & 68.75 & 16 \\ _Shigella_ sp. & 25.00 & 75.00 & 4 \\ \hline \end{tabular}
\end{table} 
Table 3: Multiple antibiotic resistance (MAR) index of different bacteria isolated from balloon-type anaerobic digester samples.
## 6 Experiments and Results
### Laboratory Modeling  
Table 8 shows the five best models sorted by their averaged values of R\({}^{2}\). To allow comparison, standalone data-driven models underwent training using an identical grid search for hyperparameters. The top-performing model achieved an R\({}^{2}_{avg}\) of 0.33, revealing a weak relationship between the predicted and observed values.  
The model with hyperparameters kernel = \(rbf\), C = 1, \(\varepsilon\) = 0.05, and \(\gamma\) = \(scale\) demonstrated superior performance by achieving the highest R\({}^{2}_{avg}\) and MAPE\({}_{avg}\) metrics.  
However, the analysis indicates that the R\({}^{2}_{avg}\) values are notably low, suggesting that the model may not be effectively capturing the relationship between predictors and response variables. Table 9 provides a detailed examination of the daily performance, revealing that the R\({}^{2}\) values experienced significant declines on two specific days, thereby impacting the overall R\({}^{2}_{avg}\). Despite this, the MAPE\({}_{avg}\) values remain exceptionally low, which attests to the model's high accuracy and reliability. Figure 13a,b illustrate these days of suboptimal performance.  
Aligning these underperforming days with unusually hot weather explains the reduced model performance. Despite low R\({}^{2}\) values, the prediction profile reveals a reasonably accurate curve, with larger deviations typically around 1 \({}^{\circ}\)C. In contrast, the best-performing days with the highest R\({}^{2}\) values and lowest MAPE scores are shown in Figure 14a,b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\text{R}^{2}_{avg}\) & \(\text{MAPE}_{avg}\) (\%) & Kernel & C & \(\varepsilon\) & \(\gamma\) \\ \hline
0.54 & 1.20 & rbf & 1 & 0.05 & scale \\
0.52 & 1.37 & rbf & 10 & 0.1 & scale \\
0.52 & 1.21 & rbf & 5 & 0.05 & scale \\
0.51 & 1.41 & rbf & 5 & 0.1 & scale \\
0.49 & 1.38 & rbf & 25 & 0.1 & scale \\ \hline \hline \end{tabular}
\end{table}
Table 8: Five top-performing models sorted by R\({}^{2}\).  
Obtaining a negative R\({}^{2}\) score but a very low MAPE is unusual but can occur due to significant variance differences between training and test datasets. While the test data's high variability challenges the model, resulting in worse performance than mean-based predictions (negative R\({}^{2}\)), the low MAPE indicates reasonably accurate predictions in regions where test and training data variability align. The model captures overall trends but struggles with local variations or outliers.
## 4 Results and Discussion
### Spoken Term Detection
#### 4.1.2 Test Data  
The evaluation system results on the test data are presented in Tables 10-12 for MAVIR, RTVE and SPARL20 data, respectively. Opposite to the results on the development data, the systems obtained better results for MAVIR data than for RTVE data. This could be due to the higher OOV rate for the RTVE data compared with for the MAVIR data (21.8% vs. 13.9%). Since the systems do not allow for OOV query detection, the performance drop is much more remarkable for RTVE data.  
On the test data, the data augmentation technique does not improve the system performance neither for the DNN nor for the SGMM-based systems. This suggests some overfitting to the development data, where the DNN-based acoustic model benefits from that technique. The only exception is for the DNN acoustic model on the SPARL20 data, although the difference in performance is not statistically significant for a paired \(t\)-test. The results show that the best performance is, in general, obtained by the _Kaldi SGMM_ system.  
This best performance is statistically significant (\(p<0.001\)) compared to the systems that employ data augmentation (i.e., _Kaldi DNN + DA_ and _Kaldi SGMM + DA_) for MAVIR data and with the rest of the systems for SPARL20 data. For RTVE data, similar performance is obtained with the _Kaldi DNN_ and the _Kaldi SGMM_ systems, and the difference is not statistically significant.  
To sum up, these results on the test data show that the data augmentation strategy fails when addressing spoken term detection on unseen test data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System ID** & **MTWV** & **ATWV** & **p(FA)** & **p(Miss)** \\ \hline Kaldi DNN + DA & 0.0025 & \(-\)0.0168 & 0.00000 & 0.996 \\ \hline Kaldi SGMM + DA & 0.0505 & 0.0403 & 0.00002 & 0.933 \\ \hline Kaldi DNN & 0.4218 & 0.4230 & 0.00007 & 0.513 \\ \hline Kaldi SGMM & 0.4413 & 0.4356 & 0.00007 & 0.489 \\ \hline \hline \end{tabular}
\end{table}
Table 10: System results of the STD task for MAVIR test data.  
Figure 4: DET curves of the STD systems for RTVE development data.  
The DET curves for MAVIR, RTVE and SPARL20 data are presented in Figures 5-7, respectively. For MAVIR data, the _Kaldi SGMM_ system performs the best for a low FA ratio, and the _Kaldi DNN_ system performs the best for a low miss ratio. For RTVE data, the _Kaldi SGMM_ + DA system performs the best for a low FA ratio, and the _Kaldi DNN_ system performs the best for a low miss ratio. For SPARL20 data, the _Kaldi SGMM_ system performs the best for both low FA and miss ratios.
## 2 Overview of Candidate Technologies for Wearable Wireless Networks
### Overview of On-Body Communication Technologies and Existing Implementations  
A holistic overview of (non-cellular) existing standards and technologies for WWN applications is presented in Table 1. For on-body networks, over the past decade a legacy has been incurred from the wireless sensor network-powered Zigbee and the IEEE 802.15.4 standard. Such BAN products and protocol designs remained very dominant by covering health-care-related applications such as patient monitoring in hospital wards and intensive care units. Few other variants of this standard (for example IEEE 802.15.4a, mainly used for wireless sensor networks for ultra-wide bands, and IEEE 802.15.4j, a modified physical layer of IEEE 802.15.4) focused on the medical body area network (MBAN) with frequency band between 2360 MHz to 2400 MHz (just before the congested narrow band spectrum). However, this is limited to medical-related applications where for instance dynamic mobility, space-time variations of the wireless channel cause less impact and therefore such specific features are not proposed in the standard. The SmartBAN standard is proposed by the European Telecommunications Standards Institute (ETSI) to support health-care-related applications. However, the proposed BAN-specific features are not scalable for covering other non-medical applications [8]. The IEEE 802.15.6 standard is targeted for wide a range of applications for body area networks. It provides great flexibility, diversity in terms of features and WWN-specific provisions which are necessary to be exploited in the dynamic and emerging applications. The standard proposes BAN-specific channel models (which are very important to accurately model the system performance). In terms of maximum achievable throughput at narrow-band, the IEEE 802.15.6 standard can reach up to 680 Kb/s, while operating at maximum frequency and highest modulation order by considering all the overheads of the Media Access Control (MAC) and Physical (PHY) layers [9]. This imposes limits to the use of the IEEE 802.15.6 standard in a few emerging applications. For example in applications such as augmented reality where transmission of high-rate audio and video are necessary, the IEEE 802.15.6 standard does not meet the requirements. However, since the IEEE 802.15.6 standard provides ultra-low power consumption for both invasive and non-invasive devices while having key security features, it is expected to cover a wide range of applications with relatively low throughput requirements. Importantly, the great flexibility on the usage of multiple options at the PHY (i.e., human body communication, narrow-band, and ultra-wide-band) and at the MAC layer (scheduled access, beacon enabled/disabled, carrier sense multiple access with collision avoidance (CSMA/CA), polling and posting), make the IEEE 802.15.6 standard a viable option for on-body WWN. In this paper we use the IEEE 802.15.6 standard with applications covering the data rate up to 600 kbps. Further, we provide an in-depth overview of the standard focused on connectivity.