esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasound in Telemedicine: A Brief Overview
## 4 Artificial Intelligence and Tele-Ultrasound  
Excellent-quality ultrasound images serve as the foundation for expert interpretation. Artificial intelligence (AI) is emerging to provide a means for autonomously gathering images, even for the uninitiated. Obstetricians have perfected the art of capturing and evaluating fetal images for pregnancy monitoring. Startup enterprises in the United States and Europe have acquired regulatory approval to advise inexperienced clinicians to take images for clinical purposes using AI guidance. The ultrasound probe is put on the patient after entering information such as the patient's height, weight, and gender. The clinician isthen instructed on how to move the probe (clockwise, counterclockwise, up, or down), and the picture is autocaptured as soon as the best image is detected.  
There is still a scarcity of validation data for high-quality images acquired using this method. According to trials conducted by nurses with no previous echocardiography experience who used this equipment on hundreds of patients, high-quality video photographs may be obtained well over 90% of the time. That result holds true for images of organs that are not constantly moving, such as the heart, and should be more suited for AI algorithmic direction. Researchers are collecting massive amounts of ultrasound images from a range of patients to assist and drive algorithm development even further. These images will be linked to ground-truth diagnoses and verified clinical results. The implications of AI-assisted ultrasound guiding may be crucial for all practitioners in the future. For example, an emergency room doctor may snap a photograph of any region of a patient's body and then send the video loop to a radiologist for interpretation. Deep learning algorithms for exact ultrasound interpretations are being investigated in the meantime; however, they are still in the early phases of development [31]. The advantages might be extended to rural regions, as well as middle- and low-income nations, where worker capacity or skill in image capture may be limited or non-existent, democratizing imaging technology. More widespread clinical use of smartphone ultrasonography has begun in rural Africa and India, and AI guidance for image capture may be adopted soon. In the COVID-19 outbreak, new uses of AI guidance have lately been seen [32]. Researchers are using deep learning approaches to develop algorithms for automating acquisition and severity evaluations to help in real-time clinical decision-making. If fully confirmed, the easily available objective image data might assist in the diagnosis, triage, and surveillance of seriously sick patients. In the future, a patient with heart failure may transmit to their doctor a clinical grade echo of their heart, whereas a person with acute stomach discomfort might obtain targeted photographs that indicated a kidney stone.
## 5 Simulation Results and Discussions
### Comparison of Localization Ratio  
In this sub-section, the localization ratio is compared, which is defined as the percentage of successfully localized unknown nodes. As shown in Figure 9, the localization ratio decreases as the increasing number of malicious nodes, and increases with the growing number of anchor nodes. Obviously, more unknown nodes can be localized with more anchor nodes. In addition, more packet error and packet loss will be introduced by more malicious nodes. In this case, some unknown nodes cannot receive enough localization beacons to localize themselves, which greatly reduces the localization ratio. However, our proposed CSLT has higher localization ratio than the compared algorithm, this is because, in CSLT, a reliable trust model is proposed to ensure the security of sensor nodes, and a secondary localization process is also helpful to improve localization ratio.  
Figure 8: Comparison of localization accuracy. (**a**) Impact of malicious node ratio; (**b**) Impact of anchor node ratio.
## 3 Results
### Fault Diagnosis
#### 3.2.11 Comparisons  
In this part, in order to recapitulate the obtained results, a list of the 10 fault diagnosis methods previously introduced is reported, where the advantages and disadvantages of each method are described.  
* STFT. Advantages: able to detect the higher-order harmonics of the BPFI for bearing 3 and of the BPFO and BSF for bearing 4. Disadvantages: not able to detect the corresponding first-order harmonics.
* PSD. Advantages: able to detect some harmonics of the BPFI for bearing 3 and of the BPFO for bearing 4. Disadvantages: not able to detect the harmonics of the BSF for bearing 4, and presence of frequency lines related to structural resonances.
* SES. Advantages: able to detect the harmonics of the shaft rotation frequency and BPFI for bearing 3 and of the BPFO and BSF for bearing 4. Disadvantages: presence of high noise and masking components.
* TSA + SES. Advantages and disadvantages: exactly the same of SES alone.
* ALP + SES. Advantages and disadvantages: the same of SES alone, with a reduction in part of the background noise.
* Daubechies' Wavelets + SES. Advantages and disadvantages: the same of SES alone, with a little reduction in noise and masking components.
* FIR filter + SES. Advantages and disadvantages: exactly the same of SES alone.
* CPW + SES. Advantages: able to detect the harmonics of the shaft rotation frequency and BPFI for bearing 3 and of the BPFO and BSF for bearing 4 with very low noise. Disadvantages: reduced amplitude of the response peaks.
* IES. Advantages: able to detect the harmonics of the shaft rotation frequency and BPFI for bearing 3 and of the BPFO and BSF for bearing 4 with relatively low noise. Disadvantages: analysis performed on the raw (i.e., not pre-processed) signal.
* WVD. Advantages: able to detect some harmonics of the shaft rotation frequency for bearing 3 and of the BPFO for bearing 4. Disadvantages: not able to detect the harmonics of the BPFI for bearing 3 and of the BSF for bearing 4.
## 4 Performance Analysis  
Considering the processing capability onboard is weak, we apply the beam steering method for signal transmission. For convenience reasons, we provide the following assumption.  
**Assumption 1**.: _For the beam steering method, the equivalent downlink channel gain can be assumed as_  
\[g_{k,m} = \mathbf{h}_{k,m}^{\mathbf{H}}\mathbf{w}_{m} \tag{9}\] \[= \rho_{k,m}l(d_{k,m})\sqrt{E_{S}}\mathbf{a}^{H}(\phi_{k,m},\theta_ {k,m})\times\mathbf{a}(\phi_{k,m},\theta_{k,m})/||\mathbf{a}(\phi_{k,m},\theta _{k,m})||^{2}\] \[= \rho_{k,m}l(d_{k,m})\sqrt{E_{S}(\eta_{k,m})},\]  
_where \(\eta_{k,m}\) denotes the angle between UT \(k\) and beam-center compared to satellite \(m\). In detail, \(E_{S}(\eta_{k,m})=E_{S}\Big{(}\frac{l_{1}(u_{k,m})}{2u_{k,m}}+36\frac{l_{3}(u_ {k,m})}{u_{k,m}^{\prime}}\Big{)}^{2}\)[19]. Here, \(u_{k,m}=2.07123\frac{\sin(\eta_{k,m})}{\sin(\eta_{3 dB})}\), \(\eta_{3 dB}\) denotes the angle of 3 dB for the transmit beam._
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 3 First Approach: Single-Objective Evolutionary Algorithm
### Constraints  
The application of evolutionary computation allows the introduction of constraints that all the individuals in the population must fulfill to be feasible. In Section 1, it was mentioned that the distribution of labels with few examples might lead to subsets lacking examples having that label, which can difficult validation and test of multi-label classifiers. Therefore, EvoSplit introduces an optional constraint to ensure that, if possible, all subsets contain, at least, one example of each label. For instance, if a data set has to be split into three subsets and a label only appears in three examples, each of those examples will be distributed to a different subset. The constraint is not considered for those class labels which number of examples is lower than the number of subsets. Some other constraints could also be considered, if needed.  
In case of generating an individual that does not fulfill the constraint, a repairing process similar to that explained before would be applied.
###### Abstract  
Objectives: The aim of this systematic review was to analyze the effect of keratinized mucosa (KM) on different peri-implant health-related parameters and on patient-reported outcome measures (PROMs). Material and methods: Randomized controlled trials, cohort, cross-sectional and case-control human studies with a follow-up period of at least 6 months comparing two groups of patients with presence or absence of KM, or with KM \(<\) 2 mm or \(\geq\)2 mm were included. Primary outcomes were implant failures, PROMs and BoP (BoP/mBI). Additional outcomes were PPD, plaque accumulation (mPI/PI), gingival inflammation (GI/mGI), marginal bone loss (MBL), soft tissue recession (REC) and biological complications. Results: Fifteen studies were included (one RCT, two cohort prospective and twelve cross-sectional). Meta-analysis was performed for cross-sectional studies. Implant failure and complications were not presented as outcome measures, and five studies analyzed PROMs. Results from the meta-analysis reported no evidence of any statistical significant difference between groups in PPD, BoP and MBL, while a statistical significant difference in GI/BI, PI and REC was present in favor of the group with KW \(\geq\) 2 mm. More biological complications were present in the group with no KM/KM \(<\) 2 mm but few cases were present to draw any conclusions. Although a meta-analysis could not be performed, a consistent trend toward the worst pain/discomfort in KM \(<\) 2 mm was observed. Conclusions: No clear evidence was found supporting the role of KM in peri-implant health and PROMs, even if more plaque and marginal inflammation were present in the KM \(<\) 2 mm group. Clinical relevance: KM could have a role in patients with erratic maintenance and patient comfort.  
k 20 good oral hygiene are key factors for the success of the treatment. In this respect, the role of keratinized mucosa (KM) around implants is still controversial [6; 7; 8].  
The 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions stated that a lack of KM is among the principal factors associated with the recession of peri-implant mucosa [9]. Furthermore, it affirmed that the evidence is equivocal regarding the effect of KM on the long-term wellbeing of the peri-implant tissues. The results of the studies on this topic are divergent and without consensus [10; 11; 12; 13; 14; 15; 16; 17]. It seems, however, that KM may have advantages regarding patient comfort and the ease of plaque removal [9]. This is in line with the review paper of Wennstrom & Derks [6] suggesting that although the lack of an \"adequate\" width of KM per se is not harmful to peri-implant health, it may hamper the performance of proper oral hygiene.  
Other systematic reviews have tried to clarify the importance of KM, addressing its ideal width in relation to health and esthetics with controversial results [6; 7; 8; 18].  
The aim of this systematic review was to update the knowledge about the influence of KM on peri-implant health and esthetics, also addressing patient-related variables that were never considered in previous reviews.
## 5 Adaptive Scheduling Mechanism Based on Self-Similarity
### The Proposed ASMSS
According to the definitions, the corresponding scheduling mechanism of \(N_{0}\) in this period can be denoted as \(T_{N_{0}}=\textless 0101001000,300\) s\(\greater\) shown in Figure 4.  
Based on the _ASMSS_ design for the duty cycle OppNets above, we can find that node \(i\) need to update the last contact time of all the nodes ever encountered in order to calculate the expected \(EV\) for each slot in each period. However, nodes in the network will inevitably miss some contacts with other nodes due to the duty cycle operation, or the contact is found with a delay when the nodes are both in sleeping state at the beginning of their contact, as shown in Figure 1. It is difficult for the nodes in the network to obtain the exact last contact time of all the nodes ever encountered. Since the inter-contact time between nodes is much longer than the contact duration and the discovery delay between nodes is less than the contact duration, we only consider the missing contacts and ignore those delayed contacts. For the missed contacts, if the contact between node \(i\) and \(j\) in the last period is missed, then the last contact time of node \(i\) and \(j\), \(t_{ij}^{0}\) will not update in the current period, which results in inaccurate calculation of \(EV\) of nodes \(i\) and \(j\) in the current cycle. However, as time goes on, the \(EV\) of node \(i\) and \(j\) will be significantly reduced. Figure 5 shows the Complementary Cumulative Distribution Function (CCDF) of the inter-contact time in the Infocom6. As shown in the figure, we can find that when the inter-contact time is larger than 2 h, the cumulative distribution of the inter-contact time is less than 8%, which means that when the inter-contact time is greater than 3 h, the \(EV\) between the nodes \(i\) and \(j\) in the current period will be close to 0. In summary, the proposed mechanism is still valid for the network in which nodes have missed contacts. Figure 4: An example of the Adaptive Scheduling Mechanism based on Self-Similarity (_ASMSS_).
## 3 Data Sources and Treatments
### Environmental (Weather) Data  
Weather data play the major role in the devised WPP approach. Several techniques exist for obtaining weather data at specific sites and resolutions; for example, through observation, data-mining, and NWP. Observation is the most reliable technique but is not usually feasible and accessible. Data-mining is comparatively malleable but incapable of downscaling. NWP uses energy conservation principles to obtain the weather information and is able to downscale the weather data to a desired point in space. The accuracy of the weather data source highly influences the accuracy of WPPs.  
There are various types of NWP models, for instance, Consortium for Small-scale Modeling (COSMO), weather research and forecast (WRF), Regional atmospheric modeling system (RAMS), and Mesoscale Meteorological Model, version 5 (MM5) [22; 23; 24; 25]. In this paper, weather information (forecast) from the WRF model is used. The weather forecast is downscaled for the location of the farm and the hub elevation.  
The weather information used is a matrix where the rows represent the samples and the columns represent each weather variable. Six selected weather attributes (wind speed, wind direction, pressure, temperature, dew point, and humidity) recorded for three years (2014-2016) with 15-min resolution are used. The two-year (2014-2015) weather data are used for the wind power forecast model training and validation, while the remaining one-year (2016) weather data are employed to test the model. The weather variables are selected due to their high correlation with wind energy. The WRF weather data are achieved based on the UTC time, which lags 8 h from the CST.
## 4 Evaluation of Seismic Performance  
### Lateral Load Capacity  
In seismic design, shear walls should have adequate lateral load capacity to resist the horizontal seismic action. The general wall behavior of each specimen is shown in Table 2 of Section 3.2. Based on the plane section assumption, the predicted load capacity \(F_{p,t}\) is calculated according to the Chinese Code GB 50003-2011 [3]. The experimental and theoretical load capacity versus the axial compression ratio of each specimen are presented in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Wall**} & \multicolumn{3}{c}{**First Crack**} & \multicolumn{3}{c}{**Peak**} & \multirow{2}{*}{\(F_{p,t}\) (kN)} & \multirow{2}{*}{\(F_{c}\)/\(F_{p}\)} & \multirow{2}{*}{\(F_{p}\)/\(F_{p,t}\)} \\ \cline{2-2} \cline{5-8}  & \(F_{cr}\) **(kN)** & & & & & \\ \hline CMW-1 & 125 & 2.1 & 248 & 16.3 & 0.6 & 289 & 0.50 & 0.86 \\ CMW-2 & 175 & 2.0 & 330 & 8.8 & 0.3 & 387 & 0.53 & 0.85 \\ PMW-1 & 150 & 2.4 & 267 & 17.5 & 0.6 & 289 & 0.56 & 0.92 \\ PMW-2 & 250 & 3.0 & 366 & 12.7 & 0.5 & 387 & 0.68 & 0.95 \\ \hline \hline \end{tabular} Note: \(F_{p,t}\) denotes the theoretical lateral capacity calculated by Chinese Code (GB 50003-2011) [3].  
\end{table}
Table 2: Summary of test results.  
Figure 11: Experimental and theoretical wall capacity.  
As seen in Figure 11, precast RMSWs have relatively better bearing capacity than traditional RMSWs with the same reinforcement and axial compression. More specifically, the lateral load of PMW-1 (\(\sigma_{n}/f_{m}^{\prime}\approx 0.10\)) and PMW-2 (\(\sigma_{n}/f_{m}^{\prime}\approx 0.15\)) exhibit an 8% and 11% increase when compared to the peak loads of CMW-1 and CMW-2, respectively. The test results showed that the flexural capacity of precast walls increased approximately 10% on average, when compared to traditional RMSWs under the same axial compression. Therefore, it could be reasonably inferred that the cancellation of the cleaning-hole unit at the bottom layer in the precast masonry wall increased the local compressive strength of the wall toe, which eventually increased the flexural capacity of the RMSWs.  
In addition, with the increasing axial stress, the peak load of both traditional and precast walls exhibited a similar major increase of about 35%, which proves that axial compression has a remarkable influence on the load capacity of the shear wall.  
It should be noted that when compared with the experimental results, the theoretical capacity for traditional and precast test walls was overestimated on average by 14% and 7%, respectively. This could be explained by the fact that the reversed cyclic loading caused accumulated damage to the specimen, especially under high axial compression [25].
## 2 Materials and Methods
### Methods
#### 2.2.2 Feature Selection  
After having the data standardized and the three classes balanced, we performed a feature selection procedure based on the Boruta algorithm [38]. Boruta is a wrapper method that exploits the Random Forest classifier to perform a robust and efficient supervised feature selection. Given the relatively small number of features, we might not even apply a feature selection strategy, but through Boruta we reduce the noise or redundant data, thus we can improve our model by using only those features that are uncorrelated and non-redundant. In fact, a wrapper method like Boruta plays an important role not only reducing the computational time of the algorithm but decreasing the complexity of the model and improving the model performances. Briefly, Boruta ([PERSON] is a god of the forest in the Slavic mythology) exploits the same idea behind the Random Forest classifier, that is, by inserting elements of randomness into the system and calculating results from the set of randomized samples, it is possible to reduce the negative effect of random fluctuations and correlations [38]. In Boruta, Random Forest trees are independently grown on different bagging samples of the training set. Then, feature importance is computed through the loss of classification accuracy caused by random permutation of the variables.
## 2 Experiments  
The sample used in the experiment was CNT film, which was produced by the floating catalytic chemical vapor deposition method [21], provided by the Suzhou Institute of Nano-Tech and Nano-Bionics, China. The thickness of the film was 10 \(\upmu\)m, the sheet resistance was \(2\times 10^{-5}\)\(\Omega\)-cm, and the thermal conductivity of the CNT film was 700 W-m\({}^{-1}\)-K\({}^{-1}\), which could be detected by frequency-domain thermoreflectance [22]. The oxygen-containing alkanes and olefin oligomers were contained to bond the CNTs and the transmittance less than 5% at the laser \(\lambda\) = 1030 nm [23]. The sample used a small amount of water vapor to atomize the surface of the spring steel substrate and to flatten the 10 \(\upmu\)m thick CNT film on the spring steel substrate with a size of \(2\times 2\) cm. After standing for about 2 h, the sample was placed on an X-Y translation stage with a movement resolving power of 1 \(\upmu\)m.  
A femtosecond laser system, which has automated harmonics generators module mounts directly on the pharos PH1-15 laser head and integrated into the system, was used in the experiments. The process parameters are the wavelength of 1030 nm and 515 nm, pulse duration of 300 fs, and the repetition rate range from 1 kHz to 1 MHz. The maximum of pulse energy was 400 \(\upmu\)J. The energy profile of the laser beam was a Gaussian function profile, and the polarization state was linearly polarized. A schematic diagram of the experimental setup with femtosecond laser ablating is illustrated in Figure 1. The laser beam was directly focused on the surface of the CNT film by using optical lenses with a focal length of 60 mm, and the focal spot diameter was about 11 \(\upmu\)m. The pulses number irradiated on the targets were precisely controlled by an electrical mechanical shutter synchronized with the laser repetition rate. A high-resolution visualization of the laser ablated region on the target surface was allowed by an accurate imaging system with a CCD camera (DelphiLaser). The laser output power was examined with a power meter before each test. Every ablating and cutting process were repeated 3 times, and each measurement was conducted at least 5 times on different areas. After laser ablating, the characteristics of the CNT film were examined by SEM (ZEISS Merlin Compact) and Raman spectrometer (Renishaw inVia). Raman spectra were collected on the cutting position of the raw film, the excitation laser power is 0.5 mW (532 nm wavelength), and the diameter of the exciting laser beam on the film surface was about 1 \(\upmu\)m.  
Figure 1: The schematic diagram of the experimental setup of the femtosecond laser percussion ablating.