esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Airway Segmentation Using Key Point Prediction
## 2 Materials and Methods
### Training via Regression Neural Network and Metrics for Accuracy Comparison  
The 315 midsagittal images obtained from the patient's cone-beam computed tomography (CBCT) data were split into training and test sets at a ratio of 4:1. During clinical data set-based training, validation was not performed because the sample size was too small for validation. Instead, a five-fold cross-validation was applied. First, the image size was set to 200 \(\times\) 200 pixels, and 16 convolution layers were packed for feature extraction. To generate the regression model, the regression layer was connected to a fully connected layer. Mean-squared-error was used as a loss function. Data augmentation was then conducted, including rotation from \(-6^{\circ}\) to \(+6^{\circ}\), uniform (isotropic) scaling from 0.5\({}^{\circ}\) to 1\({}^{\circ}\), Poisson noise addition, and contrast and brightness adjustment. An NVIDIA Titan RTX GPU with CUDA (version 10.1) acceleration was used for network training. The models were trained for 243 epochs using an Adam optimizer with an initial learning rate of 1e-4 and a mini-batch size of 8.  
\begin{table}
\begin{tabular}{c c} \hline
**Definition** & **Explanation** \\ \hline Reference Points & \\ PNS & Most posterior point of palate \\ VP & Most posterior point of vomer \\ CV1 & Most anterior inferior point of anterior arch of atlas \\ CV2 & Most anterior inferior point of anterior arch of second vertebra \\ CV4 & Most anterior inferior point of anterior arch of fourth vertebra \\ Reference planes & \\ PNS-Vp plane & The plane was perpendicular to the midsagittal plane passing through the PNS and the Vp. \\ CV1 plane & The plane was parallel to the natural head position plane passing through CV1. \\ CV2 plane & The plane was parallel to the natural head position plane passing through CV2. \\ CV3 plane & The plane was parallel to the natural head position plane passing through CV3. \\ CV4 plane & The plane was parallel to the natural head position plane passing through CV4. \\ Volume & From PNS-VP plane to CV1 plane \\ Nasopharynx & From CV1 plane to CV2 plane \\ Oropharynx & From CV2 plane to CV4 plane \\ Hypopharynx & \\ \hline \end{tabular}
\end{table}
Table 1: Definition of reference points and planes for airway division. (Abbreviations: PNS, posterior nasal spine; VP, posterior point of vomer; CV1, 1 st cervical vertebra; CV2, 2 nd cervical vertebra; CV4, 4 th cervical vertebra).  
Figure 2: Airway segmentation process. (**A**) Binarization image. (**B**) Hole filled image after close operation. (**C**) Difference image between (**A,B**). (**D**) An image that erases the area outside the area where 5 reference points, and 1/4 and 3/4 of the inferior border are connected. (**E**) Segmented airway.  
The prediction accuracy of the model was calculated using (a) the volume difference between the predicted and manually determined nasopharyngealnx, orophnx, and hypopharynx, and (b) the Euclidean distance between where the predicted and manually determined points are real data.
## 3 Experiments
### Datasets  
CWRU Bearing Dataset: This study utilizes vibration data of bearings sourced from the Bearing Data Center at Case Western Reserve University. Details of the dataset used are outlined in Table 1. The dataset includes data from the drive end bearing, which is a modelSKF6205, and the fan end bearing, which is a model SKF6203. The data acquisition was performed at a sampling rate of 12 kHz. There are four motor load states in this dataset, namely 0 HP, 1 HP, 2 HP, and 3 HP. In addition, each load condition also includes three roller faults with different fault sizes, Roller Fault (RF), Inner Ring Fault (IF), Outer Ring Fault (OF), and Normal state (Normal). Therefore, there are ten different failure scenarios for each load. This article constructs four datasets based on the different loads borne by the bearings, namely \(\{C_{0},C_{1},C_{2},C_{3}\}\). Meanwhile, six transfer tasks are designed between these datasets.  
JNU Bearing Dataset: The JNU bearing dataset is also used for the transfer task. This dataset uses a Mitsubishi SB-JB induction motor to perform fault diagnosis on the centrifugal fan system. The motor model is a 3.7 KW three-phase induction motor, and the sampling frequency is 50 KHz. The dataset contains three speed conditions 600 rpm, 800 rpm, and 1000 rpm. Each speed condition includes four fault types: Normal state (Normal), Roller Fault (RF), Inner Ring Fault (IF), and Outer Ring Fault (OF), for a total of twelve fault categories. This article constructs three datasets \(\{J_{0},J_{1},J_{2}\}\) according to the different rotation speeds of the bearings as shown in Table 2, and constructs four transfer tasks among them.
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailless Flapping Wing Micro Air Vehicles
#### 3.2.3 Computational Fluid Dynamic Approach Based System Identification
They have computed the stability derivatives and, once again, the eigenvalue-based analysis was conducted, and very similar results to the longitudinal scenario (i.e., one unstable slow divergence mode, one stable slow oscillatory mode, and a stable fast subsidence mode) were identified. The aforementioned CFD strategies provide a suitable understanding about the stability analyses of FWMAVs. However, the CFD-based approach is computationally costly and time-consuming.
## 3 Results  
### pH Test on Test Plates  
For the first part of experiment, test plates coated with litmus pH sensor film with TiO\({}_{2}\) were used. After the pH sensor films were dried, each test plate was immersed in a solution with different pH value for 5 min. The color change was monitored by photographing the plates with a smartphone and processing the images using the Color Picker AR application. The processing results were presented as RGB and hue values numerically (Table 2) and graphically (Figure 4).  
The results presented in Table 2 and Figure 4 indicate that there was a color change in the film in solutions with different pH values after 1 min. Due to the photography conditions, all images exhibit a brownish tint, but a visible change from reddish to blue-purple occurs when the medium shifts from acidic to alkaline.  
The R and G values decrease overall from pH 2 to pH 10, with a more notable decrease in the R value from pH 7 to pH 10. Additionally, the B value seems to follow the same trend as the G value, from pH 7 to pH 10. Thus, a general trend of decreasing R values and increasing B values can be observed when transitioning from acidic to alkaline media. The hue values corroborate the trends observed in the RGB values and the most pronounced  
Figure 3: Experimental setup with two syringe pumps connected to a millireactor.  
color change occurring after pH 7, characterized by a significant increase in hue values up to pH 10.  
The color change in the litmus indicator typically occurs within a pH range of 4.5 to 8.3. The results indicate the most significant range from pH 7 to pH 8, which fits within the theoretical range. This appears as a typical A curve characteristic of the indicator color during neutralization. The results may be affected by the immobilization of the indicator on the film, resulting in a reduced responsiveness of the indicator. Consequently, a longer diffusion time may be required for ions from acidic and alkaline solutions to interact with the indicator molecules.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**pH** & **R** & **G** & **B** & **H** & **Color** \\ \hline
2 & 184 & 166 & 156 & 21 \\
3 & 183 & 166 & 158 & 19 \\
4 & 181 & 154 & 145 & 15 \\
5 & 182 & 162 & 151 & 21 \\
6 & 175 & 150 & 143 & 13 \\
7 & 175 & 150 & 147 & 6 \\
8 & 173 & 142 & 155 & 335 \\
9 & 159 & 137 & 147 & 333 \\
10 & 141 & 121 & 131 & 330 \\ \hline \end{tabular}
\end{table}
Table 2: RGB and hue values with the corresponding colors for the test plates coated with a pH sensor film based on litmus and TiO\({}_{2}\).  
Figure 4: Graphical representation of the dependance of RGB and hue values on the pH of the solution that the test plates were immersed in for 5 min.
## 4 Discussion
The bigger question is what the future of the Arctic system is as a whole, and to evaluate this, there are many interacting processes that must be considered, such as shifts in vegetation composition, hydrological and biogeochemical processes, some of which are included in JULES and some of which still need to be developed. For instance, most terrestrial nitrogen cycling models assume that plants can only utilise inorganic forms of nitrogen, yet it is known that Arctic vegetation can directly access organic nitrogen [79]. Furthermore, a study by [80] found that modelled organic nitrogen accounted for 36-87% of total nitrogen uptake by plants in tundra ecosystems with others estimating that nearly 60% of the nitrogen uptake by plants is from free amino acids in the arctic tundra [81]. Organic nitrogen may have different controls compared to inorganic nitrogen considered in our study and could play a very important role in plant nutrition under future warming. For example, a study by [35] that measured organic nitrogen (extractable, mineralizable and plant-available) and found consistently higher amounts in deeper layers of the soils. Therefore, future permafrost thaw and vegetation shifts under future climate change could be heavily involved with organic nitrogen; yet, this process is generally not included in land surface models. There are also vegetation processes such as root suberization and re-translocation of nutrients into storage tissues that will affect their response to additional nitrogen availability [35] and these are generally not included in models either. For example, thaw-depth development synchrony with plant nitrogen demand has the potential to be mismatched, whilst some species such as _R. chamaemorus_ as well as deep-rooting graminoid species have been found to take up nitrogen by the time a meaningful change in thaw depth has occurred [35, 82]. It is therefore important to recognise the limitations of using a model as presented in this study in presenting the complete picture of the arctic system under future climate change. However, considering the strong nitrogen limitation in the Arctic overall, we expect that the addition of mineralised nitrogen from permafrost has a role to play in the overall response of the Arctic ecosystem to climate change, and our results support this hypothesis, albeit that they illustrate only one aspect of the response. Similarly, by testing a model's ability in representing one response such as belowground fertilisation, we can get closer to a more complete representation of the Arctic system and thus closer to getting the whole ecosystem response right.
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 3 Traffic Monitoring  
With the increase in urban population and the rapid increase in the number of private vehicles, many roads have become more congested than ever. Road traffic monitoring plays a crucial role in traffic management. Currently, traffic monitoring mainly relies on static roadside units, which passively collect traffic information. Many traffic-monitoring devices can only capture traffic conditions at fixed and discrete locations. This results in many units being required to monitor a road segment [40]. Instead of deploying static roadside units, UAVs can be temporarily deployed during some particular time along with certain road segments. Undoubtedly, this is promising to reduce the installation cost. More importantly, since UAVs have a high probability of obtaining LoS with ground vehicles, they are more likely to achieve a better quality of monitoring than conventional roadside units. For traffic monitoring, two directions have been widely investigated in the research community. The first one focuses on traffic information processing, and the second one is about the control and collaboration of a UAV network. Figure 5 shows an example illustrating traffic-monitoring UAVs.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology  
This section presents an overview of the proposed hybrid early warning system (HEWS) to reduce the rock-fall risk using computer vision and machine learning. The system is composed of three sub-models: detection, prediction, and hybrid risk reduction models. The following subsections give details of the system implementation and validation.
## 4 DASH7 Communication System
### Air Interface  
#### 4.1.1 RF Channels  
As mentioned earlier, the DASH7 communication standard operates in the unlicensed sub-GHz frequency spectrum. The RF band that is available to use depends on the geographical region from where devices are operated. DASH7 supports the 433 MHz, 868 MHz, and 915 MHz bands. These bands with their specific channel indexes and frequencies are found in Table 3.
## 5 Numerical Results
### Integrated Path Selection Scheme  
In this section, we examine different path selection schemes to improve the performance of the integrated EPON-WLAN FiWi network by means of wireless mesh networking between ONU-HT APs, considering not only throughput and delay but also overhead and packet loss. In our simulations, we consider uniform unicast traffic where a given node sends a generated packet to any other node with equal probability \(1/(N-1)\) and \(N\) denotes the number of OLT, ONU-HT APs, and HT STAs. We assume that 16 TDM ONUs and 16 ONU-HT APs are connected to the CO, whereby the distance between the ONUs and CO is set to 20 km. The optical line rate is set to 1 Gb/s. Each ONU-HT AP has three 10-Mbyte buffers for the EPON, WiFi, and wireless mesh segments. The wireless channel bandwidth and data rate are set to 20 MHz and 100 Mbit/s, respectively. Two HT STAs are located at a range of 2 km of the associated ONU-HT AP, while ONU-HT APs are located 2 km from each other. In our simulations, the BER of the wireless channel is set to \(10^{-5}\). We use the aforementioned traffic model to generate voice, video, and data packets at each ONU-HT AP and HT STA, while the OLT and each ONU are assumed to generate the same amount of traffic and a packet generated at a given node is equally destined to any of the remaining nodes. We note that layer-3 (_i.e._, routing based on IP addresses) might be used in our integrated dynamic path selection scheme due to the single-hop WMN connections between the ONU-HT APs.  
Figure 14 shows the beneficial impact of using our integrated dynamic path selection scheme on the FiWi network using both advanced aggregation techniques and wireless mesh networking. In our sim  
Figure 12: Impact of advanced aggregation techniques on integrated EPON-WLAN network performance for a 54 Mb/s WMN under voice, video, and data traffic.  
Figure 13: Impact of advanced aggregation techniques on integrated EPON-WLAN network performance for various WMN data rates under triple-play traffic.  
ulations, delay denotes the time interval between packet arrival at the source node and packet reception at the destination node which includes the time required for path selection process. We observe that the dynamic path selection scheme improves throughput and packet loss, without increasing delay and overhead significantly. Note that in Fig. 14 the 95% confidence intervals exhibit an error of less than 10% from the mean values.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 6 Proposed DVB System Using Polar Codes
Figure 12: Forward Error Correction encoder and decoder for: (**a**) Original DVB systems. ; (**b**) Proposed DVB systems. Figure 13: PER versus \(E_{s}/N_{0}\) for DVB-S2 FEC (BCH+LDPC) and optimized polar codes using normal frame and different code rates.
###### Abstract  
Osteoarthritis (OA) is a degenerative joint disease that mainly occurs due to the cellular inflammatory response and the destruction of joint cartilage. Natural eggshell membrane (NEM), a byproduct of egg processing, might be a promising knee OA treatment because of its anti-inflammatory properties and resemblance to synovial membrane components. Therefore, we aimed to study the anti-inflammatory effects of NEM in OA, utilizing both in vitro experiments with primary chondrocytes and in vivo studies with a surgical rat model of knee OA. In vitro studies showed that NEM treatment improved cell viability in chondrocytes exposed to interleukin-1\(\alpha\) by upregulating chondrogenic genes and inhibiting enzymes that degrade the extracellular matrix (ECM). Furthermore, the anti-inflammatory effects of NEM were observed in chondrocytes induced by lipopolysaccharideacharide. Administering NEM orally for 56 days after OA surgery resulted in enhanced joint swelling reduction and improved mobility in animal models, as well as an increase in bone density and cartilage compressive strength in a concentration-dependent manner. It inhibited inflammatory markers (5-lipoxygenase and prostaglandin E2) and extracellular matrix (ECM)-degrading enzymes (MMP-2 and MMP-9) in both the cartilage and synovium. Simultaneously, there was an upregulation in the expression of chondrogenic genes (Sox9, aggrecan, and Col-2). The histopathological and immunohistochemical analyses demonstrated that NEM's anti-inflammatory, anti-apoptotic, and chondrogenic properties contributed to the mitigation of joint degradation and synovial inflammation. Therefore, NEM is a potential alternative or functional food agent that addresses both anti-inflammatory and chondoprotective aspects in OA.  
Keywords:osteoarthritis; natural eggshell membrane; anti-inflammation; anti-apoptosis; chondrogenesis +
Footnote †: journal: Applied
## 6 Smart Grid Technologies
### Integrated Communications  
Integrated communication is a basic need of the modern power grid, required by and essential to other key technologies [46]. It will create a dynamic and collaborative mega-infrastructure for the exchange of information and energy in real-time. Smart grid specifications such as programmable logic control [48], supervisory control and data acquisition [49], and wireless, cellular, and broadband power lines [50] combine communication technologies. Ease of implementation, data transmission capacity, standards, network coverage, latency, and a secure system are the factors that must be considered for integrated communications.
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 4 Experimental Results and Discussion
### Signal Reconstruction Results  
To provide a detailed exposition of the rational implementation of EMD and reconstruction, we selected a segment of a typical \(x\)-axis collision vibration signal. Initially, noise was added to the signal with the SNR set to \(-9\) dB. Then, the signal underwent normalization. Finally, the normalized signal underwent the EMD process, resulting in different-order IMF components, as depicted in Figure 7. It is visually evident that the noise details were more pronounced in the first- and second-order IMFs. To mitigate the noise components in a reconstructed signal, one can choose to exclude IMFs containing rich noise details. However, it is essential to note that such IMFs also encompass other valuable signal feature information. Therefore, caution must be exercised during exclusion to prevent excessive removal, which could lead to the loss of features related to the original signal during the reconstruction process.  
In light of the results shown in Figure 7, our analysis primarily focuses on the reconstruction scenarios after selectively removing the first-order IMF, the second-order IMF, or both simultaneously. In the experiment, we introduced additive Gaussian white noise under three different SNR conditions, corresponding to SNR = \(-9\) dB, SNR = \(1\) dB, and SNR = \(9\) dB. The dataset utilized for these experiments was the C1 dataset, with the effective period length set to \(290\) sample points. For clarity, we defined the scenarios of removing only the first-order IMF component, removing only the second-order IMF component and removing both the first- and second-order IMF components as Case 1, Case 2, and Case 3, respectively. Finally, we evaluated the effectiveness of different removal strategies based on the classification accuracy results. The detailed classification results are illustrated in Figure 8. It is evident that signal reconstruction by solely removing the first-order IMF  
Figure 7: The EMD results of a typical \(x\)-axis vibration signal containing noise.  
outperformed scenarios where only the second-order IMF was removed or where both the first- and second-order IMFs were removed simultaneously in terms of the classification results. While this difference was less pronounced at lower noise intensities, as the noise intensity increased, the advantage of solely removing the first-order IMF became significantly more prominent. This finding indicates that in the context of collision classification, for noise suppression, removing only the first-order IMF is more effective in minimizing the loss of valid features in the data compared to other IMF removal strategies. Therefore, it can be considered more reasonable to reconstruct a signal by selectively removing only the first-order IMF component.
###### Abstract  
The stability assessment of aged tunnel linings are mainly evaluated based on the visual inspection, and the Tunnel-lining Crack Index (TCI) is one of the most widely used tunnel lining health assessment indexes in Japan. However, the intersection and distribution of cracks, which could influence the stability of tunnel lining greatly, were not considered in TCI. A new method was proposed for the health assessment of tunnel lining, which evaluated the lining states according to the fractal dimension of cracks. Based on the machine vision-based method, the crack image could be extracted efficiently. The fractal dimension of lining cracks in one span could be obtained in a few minutes. A series of comparative tests and field tests were conducted to evaluate the validity of this new method. The comparative tests confirmed that fractal dimension was able to characterize the density, width, and distribution of cracks. The intersection of cracks, which would increase the risk of lining collapse, could also increase the fractal dimension. The fractal dimensions of tunnel lining cracks were obtained according to the digital inspection test of Hidake Tunnel in Japan for all the 65 spans. Moreover, the TCI was obtained through statistical methods. The correlation between fractal dimension and TCI of tunnel lining was studied. The significance of the new evaluation index is that it can identify the unusual spans of tunnel lining and provide a basis for further internal testing. As a complement to the conventional visual inspection method, the fractal dimension of the cracks is a promising health assessment index.  
t 1 (2017) 7 (2017) 1