esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Reliable Monitoring Scenario
#### 4.3.2 Reliable Monitoring: Results
The average latency starts to diverge when the traffic load becomes too high. For \(T_{s}=20\) s and 12 TIM groups, the average latency rapidly grows already from 1920 stations as illustrated in Figure 11. In addition, TCP should be tuned, i.e., the TCP timeout should be increased, to properly handle the longer delays caused by increasing the number of TIM groups. TIM and RAW configurations also have a big impact on the sleeping time of the radio. We evaluated the energy consumption by measuring the total time the radio is turned off at the physical layer (Figure 12). Our measurements do not take into account the interference of other stations (collisions) since in our model a station is awake for the whole duration of its corresponding RAW slot if it does not go to sleep immediately after the beacon. A station goes to sleep immediately after the DTIM beacon if it neither has data to transmit nor is paged. Else, it also wakes up to receive the TIM beacon. A station goes to sleep immediately after the TIM beacon if it is not paged and has no data to  
Figure 9: Extensive RAW slotting limits the maximum attainable density in a network with 16 TIM groups. transmit. Generally, the higher the contention within a RAW slot, the more active each radio has to be. Worst case, the radio has to be active for a RAW slot duration each DTIM cycle, for each DTIM beacon and for each TIM group beacon the station belongs to. The sleep time decreases more rapidly as soon as RAW slots become fully saturated and transmissions need to be rescheduled to slots in the next DTIM cycle as is evident from Figure 12 for 1 and 2 TIM groups. In addition, 97% of time station spend sleeping in a configuration with 16 TIM groups, five RAW slots and sampling interval of 10 s.  
Figure 11: Increasing the number of TIM groups considerably increases the average latency in a network with a fixed number of RAW slots (\(N_{RAW}=2\)) and sampling interval (\(T_{s}=20\) s). The average latency diverges when the traffic load becomes too high. Figure 10: Grouping \(n\) measurements and sending them \(n\) times less frequently benefits the maximum attainable density in a network with 16 TIM groups. Finally, the use of our immediate reply mechanism in bidirectional communication greatly reduces RTT and is scalable as shown in Figure 13. The average number of immediate replies per station grows until saturation of the network is reached, after which it decreases with the same rate. Up to 71% of the replies can be scheduled in the same RAW slot as the request which at least halves the RTT, having in mind that the reply would otherwise be scheduled in the next DTIM cycle. Figure 12: Increasing the number of TIM groups enables stations to sleep longer. Stations sleep over 90% of the time already from four TIM groups and above, with five RAW slots and both (**a**) 20 s and (**b**) 60 s sampling interval. (**a**) \(T_{s}=20\) s; (**b**) \(T_{s}=60\) s.
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## Materials and Methods
### Water Detection  
Once the preprocessing procedure was completed by GEE, the histogram of VV band was generated, and the Otsu method was used to search over the thresholds that are represented by the bins in the histogram. The optimal threshold was computed to classify the data, where the partition whose values are smaller than the threshold are labeled as water while the partition whose values are larger than the threshold are labeled as nonwater. In order to reduce the effect of double bounce scattering issues, we defined the label of water as purely open water area, while the label of nonwater included the submerged and emergent aquatic vegetation and land features. The specific implementation of the Otsu Method can be found through the link: source code for the Otsu Method (by [PERSON]). The postprocessing procedure that removes noise and improves the quality of the classified output involving mask extraction, majority filtering and boundary clean was carried out in ArcGIS to remove the water bodies not geographically related to Poyang lake, small islands of pixels and odd edge of clusters.
# Development of a New Eye Movement Measurement Device Using Eye-Tracking Analysis Technology
## 5 Conclusions  
In this study, we newly developed a device, EyeScore, for generating stimulus that induces eye movements and recording those movements over time.  
EyeScore comprises a visual simulation generator, stimulus-presenting monitor, and two eye-tracking systems (for both eyes) within a single body. Using EyeScore, it was possible to measure eye movements without the need for a polygraph system, electrodes, or amplifier. [PERSON] et al. (2016) reported limited success with EOG for patients with low vision since corneo-retinal potential was typically attenuated in most patients due to ocular diseases. Using EyeScore, it is possible to measure eye movements in ophthalmological examinations. This approach may enable routine measurement of eye movements in patients with amblyopia and low vision, which were previously only measured in the laboratory.  
The faster the target speed, the lower the rho max, and the more frequently saccades appeared. The saccades were frequently observed at 5.5-9.5 frames (0.17-0.29 s) after the onset of target motion. The participants who could properly follow the slowly moving targets with pursuit eye movements had high rho max values, whereas for rapidly moving targets, participants who performed saccades frequently had high rho max values. The data are similar to other eye movement studies, suggesting the measurement with EyeScore was sufficiently accurate to grasp the nature of the participant's eye movements.  
Conceptualization, S.T., H.T., F.M. and T.H.; methodology, S.T. and H.T.; software, T.H.; validation, S.T. and H.T.; formal analysis, H.T.; investigation, S.T. and F.M.; resources, S.T.; data curation, S.T. and H.T.; writing--original draft preparation, S.T.; writing--review and editing, H.T., F.M. and T.H.; visualization, S.T. and H.T.; supervision, T.H.; project administration, S.T. and T.H.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI, grant number JP22K02470.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Niigata University of Health and Welfare committee (18508-201019).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available to protect the privacy of the subjects.  
T.H. borrowed equipment free of charge from NAC Image Technology Inc.
###### Abstract  
Readmissions after stroke are not only associated with greater levels of disability and a higher risk of mortality but also increase overall medical costs. Predicting readmission risk and understanding its causes are thus essential for healthcare resource allocation and quality improvement planning. By using machine learning techniques on initial admission data, this study aimed to develop prediction models for readmission or mortality after stroke. During model development, resampling methods were implemented to balance the class distribution. Two-layer nested cross-validation was used to build and evaluate the prediction models. A total of 3422 patients were included for analysis. The 90-day rate of readmission or mortality was 17.6%. This study identified several important predictive factors, including age, prior emergency department visits, pre-stroke functional status, stroke severity, body mass index, consciousness level, and use of a nasogastric tube. The Naive Bayes model with class weighting to compensate for class imbalance achieved the highest discriminatory capacity in terms of the area under the receiver operating characteristic curve (0.661). Despite having room for improvement, the prediction models could be used for early risk assessment of patients with stroke. Identification of patients at high risk for readmission or mortality immediately after admission has the potential of enabling early discharge planning and transitional care interventions.  
machine learning; prediction models; readmission; risk assessment; stroke +
Footnote †: journal: Medical Medical Research Center at National Cheng Kung University, Tainan 701, Taiwan 5
In Figure 1, the key enablers of the NTN-based IIoT landscape are synopsized with the intelligence introduced by the AI-based methods, providing real-time insights and allowing IIoT core elements to reach their full potential. These key enablers should be harmonically combined and coordinated to satisfy the individual demands of each application scenario and stand for the cornerstone for future IIoT applications. This ambiguous landscape regarding the NTNs, IIoT, and AI has motivated the present review paper, whose contribution is twofold. Firstly, this paper aims to shed light on the NTN-based IIoT and provide an overview of emerging IIoT applications. Secondly, this paper underlines the unique challenges that arise when implementing IIoT scenarios and investigates the adoption of AI techniques. To the best of the authors' knowledge, previous relevant surveys and tutorials considered only airborneplatforms and AI methods, without emphasizing the IIoT applications and principle. More specifically, these works studied the communication aspects [20], the design of radio access networks (RANs) [21], the interference management [22], the object detection and image recognition [23, 24], the trajectory and placement [25], and the planning, motion control, and situational awareness [26]. On the contrary, this paper focuses on the deployment of a wide range of AI techniques on both spaceborne and airborne platforms exclusively for the IIoT, describes the benefits of these techniques in specific IIoT scenarios, and identifies fertile research areas. The rest of the paper is organized as follows. Section 2 focuses on the indispensability of the wireless communication technologies within factory halls. Section 3 categorizes the types of aerospace communication technologies, emphasizes their advantages, and presents the state of art in advanced wireless techniques for NTNs. Section 4 outlines potential IIoT applications and services. Section 5 underlines the challenges of the NTN-based IIoT and highlights the advanced AI technologies that intend to enrich its capabilities. Section 6 surveys recent research work on AI methods for NTN-based IoT and underlines open research issues. Finally, conclusions are drawn in Section 7.
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 1 Introduction
* The simulation results show that the piecewise linear EH model performs better than the traditional linear EH model, and the EE of the multi-relay system is better than that of the single-relay system. The rest of this paper is organized as follows. In Section 2, the multi-relay cooperative DF system based on nonlinear EH is described, and the optimization problem is formulated to maximize the EE. In Section 3, the joint optimization algorithm of the relay transmit power and PS ratio is proposed. In Section 4, the algorithm is simulated, and its performance is analyzed. Finally, this paper is summarized in Section 5.
# Open-Bite Correction Using a Mid-Palatal TAD with an Intrusion TPA: Anteroposterior Position of TAD
## 4 Discussion
### Total Intrusion of Maximillary Dentition  
The intrusion of total maxillary dentition was reported in maxillary vertical excess cases without the open bite by [PERSON] [22]. Sometimes, open-bite cases need this kind of total  
Figure 12: Two models were constructed according to the position of the mid-palatal TAD. The red dot marks the location of the TAD. (**A**) Model of “posterior intrusion”. The TAD position is at the interproximal level between the first and second molars. (**B**) Model of “total intrusion”. The TAD is at the interproximal level between the maxillary second premolar and first molar. (**C**) Superimposition of the original (white mesh) and deformed model (color) from model of “posterior intrusion”. The red arrow shows that the second molar moved backward and upward. (**D**) Superimposition of the original (white mesh) and deformed model (color) from model of “total intrusion”. The red arrow shows that the incisor moved upward and forward. (**E,F**) Both models showed lingual tipping of molars, which could be prevented by expanding the TPA (0.5 mm each side) and applying crown buccal torque (5\({}^{\circ}\)). (**G,H**) Stress on the buccal view. (**I,J**) Stress on the palatal view. Modified from the figures from Clin J Korean Assoc Orthod 2019;9(2):59–71).  
intrusion even after obtaining the incisal overlap to further decrease the anterior facial height. _Case 4_ shows an open-bite example in which the total intrusion of the maxillary dentition was required (Figures 13 and 14).  
Figure 14: _Case 4_. Total intrusion. (**A**) Cephalogram showing the position of the TAD (red arrow). The mid-palatal TAD (arrow) was placed anterior to the first molar. After leveling and incisor retraction, the upper and lower incisors touched each other, preventing further counterclockwise mandible rotation. (**B**) Maximillary superimposition after leveling and total intrusion. Total intrusion made the counterclockwise rotation of the mandible possible even after the upper and lower incisors touched after leveling. (**C**) Superimposition of initial and final tracings. Initial FMA was 43.0\({}^{\circ}\) (normal average: 25.2\({}^{\circ}\)), showing a steep mandibular plane and long lower anterior facial height. Mandibular counterclockwise rotation decreased the lower facial height.  
Figure 13: _Case 4_. Total intrusion. (**A**) Initial photo showing a gummy smile. (**B**) After treatment, the gummy smile was resolved. (**C**) Initial intraoral photo showing an open bite and compensating curve. (**D**) Intraoral photo taken after debonding. Maximillary first and mandibular second premolars were extracted.  
If the intruding force is designed to pass through the center of resistance [23, 24], total maxillary dentition will be intruded. Thus, the TAD was placed mesial to the maxillary first molar, as shown in Figure 7D of _Case 4_ and Figure 14A of _Case 4_. Of course, the effect may be somewhat different depending on the characteristics of each patient.
## 4 Experiments
### Ablation Studies
#### 4.3.2 Ablation study on MNFM  
To investigate the improvement of the MNFM on the detection accuracy, we conducted ablation experiments on the VisDrone and DOTA datasets. The experimental results, as shown in Table 4, provide insights into the improvement achieved by our method.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **mAP** & **AP50** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline YOLOv8 (Baseline) & 35.24 & 60.92 & 28.33 & 44.52 & 63.12 \\ YOLOv8 + MNFM & 36.58 & 62.83 & 31.02 & 46.83 & 65.55 \\ YOLOv8 + MNFM + AFEM & 38.43 & 64.16 & 32.85 & 46.81 & 65.46 \\ YOLOv8 + MNFM + AFEM + RFEM (ours) & **39.75** & **66.37** & **33.14** & **51.27** & **69.35** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation comparison of model performance improvement on the VisDrone dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **mAP** & **AP50** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline YOLOv8 (Baseline) & 63.85 & 85.47 & 50.12 & 77.23 & 91.31 \\ YOLOv8 + MNFM & 64.68 & 86.29 & 53.45 & 83.25 & 93.22 \\ YOLOv8 + MNFM + AFEM & 66.15 & 87.83 & 56.18 & 83.28 & 93.15 \\ YOLOv8 + MNFM + AFEM + RFEM (ours) & **67.29** & **88.38** & **56.32** & **84.41** & **96.15** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation comparison of model performance improvement on the DOTA dataset.  
The aforementioned quantitative ablation experiments demonstrated that the MNFM improves the mAP of the baseline model. In adaptively adjusting the scale of the convolution kernels, the MNFM is able to extract more useful features from small, medium, and large objects, thereby enhancing the overall performance of the model. To visually observe and compare the effectiveness of feature extraction with and without the MNFM, we generated visualizations of the feature maps. The qualitative comparison result is shown in Figure 11.  
Obviously, based on the visualization results, it is evident that the backbone network is capable of extracting more valid and salient features of the objects after integrating the MNFM. Therefore, it can be concluded that the MNFM plays a critical role in the feature extraction stage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **MNFM** & **mAP** & **AP50** & **AP5** & **APM** & **APL** \\ \hline \multirow{2}{*}{**VisDrone**} & **w/o** & 35.24 & 60.92 & 28.33 & 44.52 & 63.12 \\  & **w/** & **36.58** & **62.83** & **31.02** & **46.83** & **65.55** \\ \hline \multirow{2}{*}{**DOTA**} & **w/o** & 63.85 & 85.47 & 50.12 & 77.23 & 91.31 \\  & **w/** & **64.68** & **86.29** & **53.45** & **83.25** & **93.22** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation comparison with and without the MNFM on the VisDrone and DOTA datasets.  
Figure 11: (**a**) represents the input image (from the VisDrone and DOTA datasets), (**b**) represents the feature map without the MNFM, and (**c**) represents the feature map with the MNFM.
## 4 Extra-Intestinal Diseases  
### Late Onset Sepsis  
In the past decades, the incidence of late-onset sepsis (LOS, onset >72 h after birth) in premature infants has increased towards 20%, despite advanced technology in specialized neonatal intensive care units [61; 62]. LOS is characterized by a high morbidity and mortality. The origin of LOS has traditionally been linked to colonization of intravascular catheters, however, recent studies demonstrated genetic incongruity between organisms isolated from blood culture and bacteria cultured from intravascular catheter tip, suggesting another origin [63; 64; 65]. Studies shown genetic similarity between cultured LOS pathogens and isolates from the gastrointestinal tract, indicating LOS pathogens might originate from the gut [66; 67; 68; 69; 70; 71; 72]. Therefore, [PERSON] et al. studied fecal VOC profiles in 36 preterm infants with confirmed LOS and compared these to 40 non-LOS controls. Fecal samples were analyzed up to five days prior to sepsis onset by means of an eNose (Cyanose 320\({}^{\circled{0}}\)) [15]. Fecal VOC profiles of sepsis subjects could significantly be discriminated up to three days prior to clinical onset from healthy controls (Table 4). The same research group conducted a similar study consisting of 127 LOS cases and 127 matched preterm born infants, in which fecal samples up to three days prior to clinical onset where analyzed by means of FAIMS. Discrimination between LOS cases and healthy controls was possible one day prior to clinical onset irrespective of the cultured pathogen. In the sub analysis, it was demonstrated that fecal VOC profiles of LOS caused by _Staphylococcus aureus_, _Staphylococcus epidermidis_ and _Escherichia coli_ differed significantly from matched healthy controls up to three days prior to clinical onset (Table 4) [73].  
Both studies underline the potential of fecal VOC analysis as a non-invasive diagnostic tool which allows for preclinical detection of LOS in preterm infants. Notably, each sepsis-pathogen seems to be reflected by different fecal VOC patterns. Further studies are needed to address these pathogen-VOCs by means of GC-MS in order to develop tailor-made eNose sensors to be used in clinical use. Since identification of pathogen specific VOCs can lead to early detection of specific bacterial pathogens, appropriate antimicrobial therapy can be administrated in an early stage. In addition, proof of absence of specific bacterial pathogens by VOC analysis can be achieved, therefore preventing administration of unnecessary antimicrobial therapy.
# Detecting the Extremely Small Angle of an Axicon by Phase-Shifting Digital Holography
## 2 Theoretical Analysis  
The electric field of the light after passing through an axicon can be expressed as follows:  
\[E_{object}=A_{o}\exp[-i(\varphi_{i}+\varphi_{A})], \tag{1}\]  
where \(A_{o}\) stands for amplitude, \(\varphi_{i}\) is the phase distribution of the background, and \(\varphi_{A}\) is the phase imposed by an axicon. The electric field of the reference light is expressed as follows:  
\[E_{reference}=A_{R}\exp(-i\varphi_{R}), \tag{2}\]  
where \(A_{R}\) stands for amplitude, and \(\varphi_{R}\) is the phase distribution of the reference light. To obtain the phase change \(\varphi_{A}\) caused by the axicon, we adopt the four-step phase-shifting technique [27]. Phases 0, \(\pi\)/2, \(\pi\), and \(3\pi\)/2 are respectively imposed onto the reference beam. Then, the light intensity of the interference hologram, collected by charge coupled device (CCD), can be expressed by the following equation:  
\[I_{1n}={A_{R}}^{2}+{A_{o}}^{2}+2A_{R}A_{o}\cos[\varphi_{R}+(n-1)\frac{\pi}{2} -(\varphi_{i}+\varphi_{A})]\ (n=1,2,3,4) \tag{3}\]  
We obtain the following equations by removing the axicon and respectively imposing phases 0, \(\pi\)/2, \(\pi\), and \(3\pi\)/2 to the reference beam again, as follows:  
\[I_{2n}={A_{R}}^{2}+{A_{o}}^{2}+2A_{R}A_{o}\cos[\varphi_{R}+(n-1)\frac{\pi}{2} -\varphi_{i}]\ (n=1,2,3,4). \tag{4}\]  
According to Equation (3), four different holograms recording the interference of Bessel beam and reference light with phase shift of 0, \(\pi\)/2, \(\pi\), and \(3\pi\)/2 are obtained. These holograms contain the information of the axicon. In Equation (4), we have another four different holograms without the axicon. Compared with the expression of Equation (3), these four holograms do not contain the information of an axicon. Based on these interference holograms, we can deduce the phase of the axicon by the mathematical transformation, and the details are shown in the following section. As representative examples, three different methods using four, five, or eight phase-shifted holograms were introduced. Four holograms in Equation (3) are enough to derive the object phase with the known reference phase [27]. The most common reference is a plane wave with uniform amplitude and phase, which needs precise control of the reference light. The phase can be solved using four phase-shifting holograms with the presence of an axicon, that is, \(I_{11}\), \(I_{12}\), \(I_{13}\), and \(I_{14}\) in Equation (3). The phase term can be expressed as follows:  
\[\tan(\varphi_{R}-\varphi_{i}-\varphi_{A})=\frac{I_{14}-I_{12}}{I_{11}-I_{13}}. \tag{5}\]  
Assuming the intensity of background light and reference light remains constant, that is, \(\varphi_{R}-\varphi_{i}\) = 0, we obtain the following equation:  
\[\varphi_{A}=-\arctan(\frac{I_{14}-I_{12}}{I_{11}-I_{13}}). \tag{6}\]We also solve the phase by using five phase-shifting holograms, that is, \(I_{11}\), \(I_{12}\), \(I_{13}\), and \(I_{14}\) in Equation (3) and \(I_{21}\) in Equation (4).