esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Virtual Cell Transmission in Ultra Dense Networks
## 2 System Model  
We consider a circular area \(\mathcal{A}\) where \(B\) RBAs and \(K\) single antenna users (multi-antenna users can be easily obtained from the model extended from the single-antenna user, where the single-antenna users can be seen as multiple streams of multi-antenna users) are uniformly and randomly distributed. Without loss of generality, we normalize the radius of \(\mathcal{A}\) to one. Denote the set of RBAs and the set of users in the UDN as \(\mathcal{B}\) and \(\mathcal{K}\), with \(|\mathcal{B}|=B\) and \(|\mathcal{K}|=K\), respectively. Assume that each of the RBAs is equipped with a single antenna, and all of the RBAs are connected to a central processor where the next generation fronthaul interface (NGFI) [19] is used to support necessary CSI and control signaling exchange. For any user \(k\) (\(k\in\mathcal{K}\)) in the UDN, he or she preliminarily selects \(N_{0}\) closest RBAs to form his or her virtual cell, which is the initial serving RBA set of user \(k\), denoted as \(\mathcal{V}_{k}\). If there is no virtual cell merging, MRT is adopted in each of the \(K\) virtual cells according to [11], regardless of ICL. The details of virtual cell merging are discussed in Section 2. Assume that after virtual cell merging, there exist \(M\) merged virtual cell clusters. Denote the user set and RBA set of the \(m\)-th (\(1\leq m\leq M\)) merged virtual cell cluster as \(\mathcal{K}_{m}\) and \(\mathcal{B}_{m}\), with \(|\mathcal{K}_{m}|=K_{m}\) and \(|\mathcal{B}_{m}|=N_{m}\), respectively. All of the users in \(\mathcal{K}_{m}\) are jointly served by all of the RBAs in \(\mathcal{B}_{m}\), as depicted in Figure 1. We focus on a specific user \(k\in\mathcal{K}_{m}\). Then, the channel vector from merged virtual cell cluster \(m\) to user \(k\) can be modeled as:  
\[\mathbf{h}_{k,\mathcal{B}_{m}}=\lambda_{k,\mathcal{B}_{m}}\odot\mathbf{g}_{k, \mathcal{B}_{m}} \tag{1}\]  
where \(\lambda_{k,\mathcal{B}_{m}}\in\mathbb{C}^{1\times N_{m}}\) is the large scale fadings from the merged virtual cell cluster \(m\) to user \(k\). \(\lambda_{k,\mathcal{B}_{m}}\) includes path loss and shadowing, which change in a relatively slow manner and can be estimated accurately via long-term statistics. \(\mathbf{g}_{k,\mathcal{B}_{m}}\in\mathbb{C}^{1\times N_{m}}\) represents the small-scale fadings from all of the RBAs in \(\mathcal{B}_{m}\) to user \(k\) and are assumed to be independently and identically drawn from a unit zero-mean circularly-symmetric complex Gaussian distribution.
## 2 Sensors
### Respiration Sensors  
Different from the sensors introduced above, respiration sensors in BSNs are generally composed of several sensors, such as pressure sensor, accelerometer or gyroscope. They get respiration parameters indirectly by detecting the expansion and contraction of the chest or abdomen based on those sensors. Respiration sensors are always used in the treatment of respiratory diseases and for continuously monitoring human symptoms. A breathing feedback system with wearable textile-based sensors is introduced in Reference [49]. The sensor gets the depth and respiratory rate by recognizing expansion and contraction of the chest based on a kind of piezoresistive material called a carbon-loaded elastomer. It can be implanted into fibers or textiles, helping users to practice correct breathing and to treat respiratory diseases. In addition, Reference [50] proposed a new method to solve the problem of continuous respiration monitoring. This method is based on reconstruction of the angular motion caused by breathing, which makes use of accelerometer data. It tracks the axis of rotation and obtains the angular velocity of the sensor nodes placed on the chest, implementing accurate respiration detection despite the interference caused by body movements. However, most respiration sensors monitor the parameters of breath simply, with rare consideration of flow rate and tidal volume of respiration, which is the future research direction of respiration sensors.
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 2 Methodology
### Datasets  
The dataset was obtained from [1]. The data include photographs of local medical devices, images compiled from the internet, particularly from manufacturers catalogs, and additional data sourced from a public dataset [4]. The medical devices represented in the dataset include oximeters, glucometers, and blood pressure monitors. A detailed breakdown of the number of images per device type and source is presented in Table 1, illustrating the composition and diversity of the dataset. From the full set of 1614 annotated images, 75% were used for training, 12.5% for validation, and 12.5% for testing. This split was applied to ensure balanced representation across device types and label classes.  
Figure 2: High-level SSD MobileNet V2 FPNLite architecture.  
For each image, manual labeling was performed using 15 classes, comprising the ten digits (0 to 9) and five acronyms representing medical terms: \"sys\" for systolic blood pressure, \"dia\" for diastolic blood pressure, \"m\" for glycemic level, \"p\" for pulse, and \"spO2\" for oxygen saturation.  
A second dataset was created from the original. This dataset consisted of cropped images of the bounding boxes annotated in the original images, along with their corresponding labels. The purpose of this dataset was to train the CNN to perform the classification task specifically, to compare the performance of standalone models with the combined approach of bounding box regression and CNN classification.  
To illustrate some of the variability present in the dataset, Figure 4 presents a selection of sample images. These samples reflect a range of characteristics, including inconsistent lighting, glare, reflections, and varying fonts.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Device Type**} & \multicolumn{2}{c}{**Catalogs**} & \multicolumn{2}{c}{**Local Photos**} & \multicolumn{2}{c}{**Public Dataset**} & \multirow{2}{*}{**Total Images**} \\ \cline{2-3} \cline{5-8}  & **Devices** & **Images** & **Devices** & **Images** & **Devices** & **Images** & \\ \hline \hline \multirow{2}{*}{Oximeter Glucometer} & \(\sim\)23 & 253 & 1 & 68 & 0 & 0 & 321 \\ \cline{2-3
# Application of Gas Foil Bearings in China
## 3 Challenges and Prospects
### The Failure Protective Measures and Applications in Extreme Condition  
During the operation of the high-speed turbo machinery supported by gas foil bearings, high temperature failure caused by the accumulation of heat and the transient load in the complex environment will cause a sudden stuck of the rotor-bearing system. If the scratches are severe, it will be necessary to replace the rotor with a new one, which would cause a certain amount of economic loss and wasted time. Therefore, failure mechanisms, failure protection, and preventive measures for gas foil bearings need to be considered, which are of great significance to a stable operation. During the operation of turbo machinery, more attention should be paid to the foil temperature and rotor vibration. The temperature of a foil surface can be monitored using temperature sensors in the prevention of bearing failure due to excessively high temperatures. In addition, rotor vibration is an important parameter reflecting the operating stability of high-speed turbomachinery. The vibration displacement, velocity, or acceleration signals can be obtained by data acquisition system. When the rotor amplitude is too large, turbo machinery should be slowed down in time to prevent sudden instability and shutdown. It is a trend to equip foil bearings in aeronautical and astronautical devices. The bearing capacity of foil bearings are greatly affected by working environment, for instance in rarefied air and extreme temperature situations. At the same time, the reliability of foil bearings is required to be higher for aeronautical equipment. The challenge is how to improve the reliability, service life, and load capacity of the foil bearing. Compared with ball bearings and rolling bearings, the load capacity of foil bearings is relatively small. Due to the influence of manufacturing materials, coating technology, and turbine mechanical thermal management, the reliability and service life of foil bearings could be greatly affected.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
###### Abstract  
Multi-stage or multi-generator generative adversarial networks (GANs) have recently been demonstrated to be effective for speech enhancement. The existing multi-generator GANs for speech enhancement only use convolutional layers for synthesising clean speech signals. This reliance on convolution operation may result in masking the temporal dependencies within the signal sequence. This study explores self-attention to address the temporal dependency issue in multi-generator speech enhancement GANs to improve their enhancement performance. We empirically study the effect of integrating a self-attention mechanism into the convolutional layers of the multiple generators in multi-stage or multi-generator speech enhancement GANs, specifically, the ISEGAN and the DSEGAN networks. The experimental results show that introducing a self-attention mechanism into ISEGAN and DSEGAN leads to improvements in their speech enhancement quality and intelligibility across the objective evaluation metrics. Furthermore, we observe that adding self-attention to the ISEGAN's generators does not only improves its enhancement performance but also bridges the performance gap between the ISEGAN and the DSEGAN with a smaller model footprint. Overall, our findings highlight the potential of self-attention in improving the enhancement performance of multi-generator speech enhancement GANs.  
speech enhancement; generative adversarial network (GAN); multi-stage GAN; multi-generator GAN; self-attention mechanism +
Footnote †: journal: _Applied_  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 6 Summary and Next Steps  
This paper presents an innovative framework for the continuous detection of PoIs based on mobile phone trajectories, and analyses ML-based algorithms, specifically, DBSCAN and LOF, to be applied for the continuous detection of outliers. The detection of outliers corresponds to one of the relevant functional blocks in the proposed PoI detection framework. To the best of our knowledge and as corroborated in Section 2, where we have analysed related work, the framework for continuous detection of PoIs is novel and presents the basis for a much-desired aspect in urban planning in Smart Cities. This is the possibility to improve services via existing data, via a consented, non-intrusive, and pervasive data-collecting approach. In addition to the architectural design of such a framework, the paper focuses on the detection of outliers. After checking different algorithms as explained in Section 4, DBSCAN and LOF have been selected as they are representative algorithms for both density-based outlier detection and distance-based outlier detection. The aim was to understand which algorithm could best suit a continuous PoI detection framework in the context of outlier detection (one of the proposed blocks).  
DBSCAN and LOF have been applied to two datasets with very different characteristics in terms of universe, size, and location features. The paper explains how to best set DBSCAN and LOF, and performs an analysis for accuracy, precision, and recall. Overall, LOF is the algorithm that seems to be better suited to be used as the basis for the continuous detection of outliers.  
As the next steps, we will continue with the development of the proposed framework. Once outliers are removed (by applying LOF), we shall work on the detection of PoIs by integrating learning and correction approaches.  
Conceptualization, F.M.P. and R.C.S.; methodology, F.M.P.; software, F.M.P.; validation, F.M.P. and R.C.S.; formal analysis, F.M.P. and R.C.S.; investigation, F.M.P.; resources, F.M.P.; data curation, F.M.P.; writing--original draft preparation, F.M.P.; writing--review and editing, F.M.P. and R.C.S.; visualization, F.M.P.; supervision, R.C.S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the research unit COPELABS, University Lusofona, Lisbon, FCT strategic project COPELABS UID/MULTI/04111/2019.  
The datasets used in this work can be found here: [[https://github.com/fjmper/PHD-Outliers](https://github.com/fjmper/PHD-Outliers)]([https://github.com/fjmper/PHD-Outliers](https://github.com/fjmper/PHD-Outliers)), accessed on 12 August 2022.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ DOAJ & Directory of Open Access Journals \\ ML & Machine Learning \\ MCS & Mobile Crowd Sensing \\ LOF & Local Outlier Factor \\ DBSCAN & Density-Based Spatial Clustering of Applications with Noise \\ \end{tabular}
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 1 Introduction
Then, we introduce a dynamic embedding model with the graph similarity to cluster the graphs for traffic incident detection in vehicular networks. * We conducted the experiments on multiple traffic time series data simulated by the Simulation of Urban Mobility (SUMO) simulator. The experimental findings show that the proposed model outperforms the baselines and performs better on high traffic volume scenarios. The rest of this paper has the following structure. In Section 2, background and related work are offered. Section 3 discusses the dynamic graph embedding model for detecting traffic incidents in road networks. In Section 4, experimental settings and results are explained. In Section 5, the conclusion is finally given.
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 5 Discussion  
### Performance of the SDMD  
The results depicted in Figure 9 demonstrate slight performance variations between the proposed SDMD and Circutor(r) in terms of voltage and current measurement deviations. However, these deviations are within the permissible limits set by international standards IEC 62053-21 [39] and IEC 62052-11 [52] for Class 1 accuracy meters. Specifically, the SDMD's maximum deviation of 0.7527% in voltage and 0.7364% in current, corresponding to absolute offsets of 1.6950 V and 0.0181 A, respectively, comfortably meets these standards.  
Figure 11: Voltage and current waveforms at (**a**) node 1, (**b**) node 2, (**c**) node 3, and (**d**) node 4. Voltages and currents are represented by continuous and dashed lines, respectively. The term ’V1’ refers to the voltage in phase 1, and similarly for the other phases and the currents. The term ’VN’ denotes the voltage in the neutral line.  
This adherence to Class 1 accuracy standards indicates that the SDMD, despite exhibiting slightly higher deviations compared to Circutor(r), particularly in current measurements, still maintains a high level of accuracy crucial for reliable voltage and current measurements. Further improvements to reduce these deviations could enhance the SDMD's value as a measurement device.  
In summary, the experimental setup evaluated the SDMD through two methods, both providing acceptable results as discussed above: by comparing its measurements against those from a well-calibrated grid emulator and by contrasting them with those from a commercial measurement device, Circutor(r). This confirms the suitability of the SDMD as part of the DiagElect system.
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 3 Numerical Results and Discussion
### Effect of Vehicle Speed on Ground Vibration  
When the train is running at 65 km/h, the ground vibration does not meet the requirements of the ground vibration limit. In the case of only changing the train speed, the ground vibration results of different speeds are investigated through simulation calculations. The simulated speeds are 80 km/h, 65 km/h, 50 km/h, 40 km/h, 35 km/h, and 30 km/h. The time domain results of ground vibration speeds for different speed scenarios are given in Figure 9.  
With the vehicle running speed from 30 km/h to 80 km/h, the trend of the time domain results is consistent. As the speed decreases, the ground vibration velocity shows a decreasing trend, and the peak velocity of ground vibration also decreases with the reduction in train running speed. When the speed is reduced to 40 km/h, the vibration speed attenuation is more obvious, and as the speed continues to reduce, the vibration attenuation is slower. Figure 10 gives the results of ground vibration speed at different speeds. It can be seen from the figure that for the train running speed from 50 km/h down to 40 km/h, the ground vibration attenuation is most obvious.  
Table 4 gives the ground vibration speed results from 30 km/h to 80 km/h. When the train runs 30 km/h, the ground vibration result is 0.045 mm/s, and at this time, the ground vibration speed result meets the limit value requirements.  
Figure 8: Ground vibration results: (**a**) time domain result; (**b**) frequency domain result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Speed (km/h)** & **Distance (m)** & **Vibration Velocity (mm/s)** & **Vibration Velocity Limits (mm/s)** \\ \hline
1 & 80 & 18.5 & 0.092 & 0.05 \\
2 & 65 & 18.5 & 0.08 & 0.05 \\
3 & 50 & 18.5 & 0.074 & 0.05 \\
4 & 40 & 18.5 & 0.057 & 0.05 \\
5 & 35 & 18.5 & 0.052 & 0.05 \\
6 & 30 & 18.5 & 0.045 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ground vibration velocity at different vehicle speeds.  
Figure 9: Ground vibration velocity time domain: (**a**) speed 80 km/h; (**b**) speed 65 km/h; (**c**) speed 50 km/h; (**d**) speed 40 km/h; (**e**) speed 35 km/h; (**f**) speed 30 km/h.  
Figure 11: Ground vibration velocity frequency domain: **(a)** speed 80 km/h; **(b)** speed 65 km/h; **(c)** speed 50 km/h; **(d)** speed 40 km/h; **(e)** speed 35 km/h; **(f)** speed 30 km/h.  
Figure 10: Ground vibration velocity trends with speed.
## 2 Materials and Methods
### Variables Description and Data Sources
#### 2.2.1 Variable Description
The optimization of industrial structure (OIS) in this paper includes the change of industrial structure proportional relationship (that is, the \"quantity\", represented by OIS1) and the improvement of industrial structure degree (that is, the \"quality\", represented by OIS2). The former refers to the shift of industrial focus, in turn, the transfer of various factors in turn, and the latter refers to the high added value, high technology and high intensification of industry. In this paper, the \"quantity\" of industrial structure optimization OIS1 is calculated by the industrial structure hierarchy coefficient, and the \"quality\" OIS2 is measured by the weighted value of the product of the output ratio of each industry and labor productivity. To control the impact of other factors at the provincial level on _GTFP_, referring to scholars [8; 20; 22], this paper chooses the following controlling variables: urbanization level (UL), measured by the built-up area of each region, representing the impact of urbanization; industrialization level (IS), measured by the ratio of the output value of the secondary industry in each province to the regional GDP; R & D investment level (RD), measured by the ratio of R&D inputs to GDP in each province; population size (POP), measured by the resident population of each province at the end of the year; economic development level (EG), measured by the total industrial output value of each province; FDI level (FDI), measured by the actual use of FDI; cost-based environmental regulation (ER), measured by the total income of pollutant discharge in each province to measure. The descriptive statistics of the whole sample variables are shown in Table 2. Descriptive statistics of the subsample areas are shown in Tables A1-A3 in Appendix B. In order to eliminate the heteroscedasticity of the explained variable, the natural logarithm of the explained variable _GTFP_ is taken.
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 4 Discussion
### Influence of Different SAM Weights and Ranks  
In existing studies on SAM fine-tuning with LoRA, there is limited comprehensive research on the impact of different SAM weights and rank values. Since the selection of SAM weights and rank values is particularly important for the subsequent deployment and inference of the model, we conduct extensive experiments. We train the model by using the weights sam_vit_b, sam_vit_l, and sam_vit_h, with LoRA rank values set to 2, 4, 6, 8, 16, 32, 64, 128, 256, 512, 1024, and 2048. The Dice coefficients obtained on the test set are shown in Table 2. It is evident that the SAM models fine-tuned with different LoRA rank values show significant improvements compared to the SAM model without LoRA fine-tuning (the \"no LoRA\" row).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LoRA** & **Sam\_vit\_b** & **Sam\_vit\_l** & **Sam\_vit\_h** \\ \hline None & 44.32 & 42.49 & 41.14 \\
2 & 83.55 & 84.2 & 84.78 \\
4 & 83.34 & 84.47 & 84.78 \\
6 & 82.23 & 84.81 & 84.92 \\
8 & 81.88 & 84.53 & 85.12 \\
16 & 82.82 & 84.34 & 85.01 \\
32 & 82.62 & 84.39 & 84.75 \\
64 & 83.47 & 83.98 & 85.11 \\
128 & 84.44 & 85.11 & 85.47 \\
256 & 84.98 & 85.1 & 86.17 \\
512 & 85.28 & 85.53 & 85.78 \\
1024 & 84.94 & 85.78 & 86.26 \\
2048 & 85.28 & 85.99 & 86.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impact of different SAM checkpoints and ranks on the model.  
Figure 5: The model segmented and annotated the fire detection line on the lower right side, which was missed by personnel.  
Figure 6: The model segments and annotates the fire detection line in the lower-left side that was missed by the personnel.  
For the SAM models fine-tuned with LoRA, we find that the experimental results are relatively close. To provide a more intuitive analysis, the data are converted into a line chart, as shown in Figure 7.