esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
Compared with existing medical image segmentation algorithms, our model achieves higher accuracy and faster inference speed and uses fewer computational resources in hippocampus segmentation tasks, which indicates that it has potential clinical value in the medical imaging field. For example, it can be used to assist clinicians in the diagnosis and evaluation of hippocampus lesions, as well as the quantitative analysis of hippocampus structure in neuroscience research. However, there are some shortcomings associated with our research, one of which is the inadequacy of the datasets. Due to the limitations of MRI image collection and hippocampus labeling, we can only use a limited number of MRI images for training and evaluation, which may limit the model's generalizability to broader datasets. The second limitation is the fixed size of the training data. Fixed-size training data may not fully cover hippocampus of different sizes and shapes. Thus, we propose some possible methods for future work. First, more hippocampus images can be collected labeled accurately to expand the dataset. Second, for hippocampus with different sizes, the introduction of adaptive segmentation methods should be considered so that the model can adapt to different sizes of images. In future work, we intend to solve these problems then apply the method in practice.  
Conceptualization, J.J. and H.L.; methodology, J.J. and H.L.; software, J.J. and X.Y.; validation, X.Y. and B.X.; writing--original draft preparation, J.J.; writing--review and editing, L.K.; investigation, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province (2021)J30456, 2021)J30734), the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2022B60), the National Defense Science and Technology Key Laboratory Fund Project (2021-KJWPDL-17), and the National Natural Science Foundation of China (61972055).  
Informed Consent Statement: Not applicable.  
Data Availability Statement: In this study, we used two public datasets: the MICCAI dataset, which is available at [[https://my.vanderbilt.edu/masi/workshops/](https://my.vanderbilt.edu/masi/workshops/)]([https://my.vanderbilt.edu/masi/workshops/](https://my.vanderbilt.edu/masi/workshops/)) (accessed on 15 April 2023), and the HarP dataset, which is available at [[http://www.hippocampal-protocol.net](http://www.hippocampal-protocol.net)]([http://www.hippocampal-protocol.net](http://www.hippocampal-protocol.net)) (accessed on 23 March 2023).  
The authors declare no conflict of interest.
# Quantitative Ultrasound Analysis of Oral Mucosa: An Observational Cross-Sectional Study
## 1 Introduction
According to [PERSON] et al., the two methods are superimposable and must be selected based on the anatomical position of the area to scan with ultrasound. It is recommended to execute both approaches in some regions to have full details [1]. Various authors deciphered the sonographic characteristics of healthy oral mucosa [6]. With the progress of technology, some recent ex vivo and in vivo studies suggested that US can discern tissue layers based on greyscale images through acoustic backscatter [12]. The primary consideration in assessing the accuracy of an imaging technique is the grade to which that procedure matches directly with histopathology [20]. Quantitative analysis of echograms for medical diagnosis improvement (support) has received broad interest over the past 30 years [23; 24]. By analyzing the texture of echographic images, additional information is gathered on the tissue type. The technique is based on the production of high-frequency sound waves directed at the tissues. The interaction between the waves and the tissues will produce return waves that will be picked up by the same emission source and processed in grayscale images [3]. A more reflective tissue will return to the source an ultrasound beam of very similar intensity compared to the original one, whereas a less compact one will tend to be more easily crossed by the beam [13]. The value of the returned beam corresponds to echo intensity (echo levels--ELs), and each value, or range of values, of EL will correspond a gray value from 0 to 255. EL is measured in decibels (dB) and is linearly related to the intensity. Zero dB equals max intensity, i.e., white with gray level 255 [25]. This observational cross-sectional study aimed to:  
* obtain multiple, high-definition US images in different regions of the healthy oral cavity. * perform a quantitative analysis of the high-definition US images obtained based on the analysis of greyscale (GL), echo levels (ELs--dB), and attenuation values (ATT--dB/cm),  
with the final goals of defining the oral mucosa's ultrasound characteristics and making it possible to identify the various layers based on their echogenicity.
## 3 Instrumentation
### COSF Measurement  
It is worth mentioning that the slip resistance characteristics of the investigated materials are determined by measuring the COSF, which can be obtained using two alternative methods. The first method involves increasing the tilt angle of the PV panel until the weight-bearing frame slides off. In the event of slipping, the abrupt changes in the tensile force indicated by the electronic load cell are significantly larger, serving as a clear indication. The second method involves measuring the \(x\)-component of the force of gravity using the electronic load cell when the weight-bearing frame slips on the PV panel surface while simultaneously determining the tilt angle of the PV panel. Furthermore, accurate knowledge of the lumped weight of the displacement mass and the weight-bearing frame is required. It is important to acknowledge that this method involves multiple parameters, each with its own level of uncertainty, resulting in potential sources of error.  
We analyzed the COSF measurement event with the slip specimen, which has a shore hardness of A20 and a flat outside pattern in dry working conditions; Figure 6 shows an example. The \(x\)-component force of gravity is linearly related to the tilt angle prior to slippage occurrence. As the tilt angle changes from 0 to 34\({}^{\circ}\), the \(x\)-component force of gravity gradually increases from 0 N to 0.9 N. This force is likely caused by the weight of the water contaminant on the PV panel acting on the weight-bearing frame. When the tilt angle reaches 35\({}^{\circ}\), this force abruptly changes from 0.9 N to 8.8 N, indicating the occurrence of slippage in the weight-bearing frame. The determination of the COSF using Equation (3) should be applied to a tilt angle ranging from 34\({}^{\circ}\) to 35\({}^{\circ}\). The COSF, in this case, is therefore limited to the range: \(0.67\leq\mu_{s}\leq 0.7\) (i.e., \(\tan 34^{\circ}\leq\mu_{s}\leq\tan 35^{\circ}\)). By using the second  
Figure 5: Theoretical and measured tilted angle.  
method (i.e., Equation (4) with a lumped weight of 1600 g, a tilt angle of 35\({}^{\circ}\) and a measured \(x\)-component of the force of gravity \(P_{x}^{m}\) of 8.8 N, the COSF is calculated to be approximately 0.68. Consequently, the determination of the COSF is conducted through a two-step process as follows: first, the indication of a slip occurrence, and second, a calculation involving the recorded maximum tilt angle and the measured \(x\)-component of the force of gravity.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## Appendix C Optimum Timing Between Sampling and Averaging  
Equation (43) gives the lower bound of the variance of the natural frequency of a wireless read resonator  
\[{\sigma_{\omega_{d}}}^{2}={\sigma_{\alpha}}^{2} \geq \frac{\left(1-e^{-2\alpha T}\right)^{3}}{2T^{2}e^{-2\alpha T}} \cdot\frac{1}{\eta}\cdot r(\alpha NT),\text{ with }\] \[\frac{1}{\eta} = \frac{F_{LNA}k_{B}\delta f_{s}}{P_{t}\cdot gt^{2}\cdot g^{2}\cdot e ^{-2\alpha t_{gate}}\cdot e^{-2\alpha n}{H_{R}}^{2}(\omega)\cdot\left(1-e^{(j \omega_{d}-j\omega-\alpha)t_{band}}\right)^{2}}\cdot\left(\frac{4\pi R}{\lambda }\right)^{4}\]  
The measurement time for one single measurement cycle \(t_{cycl}\) consists of the stimulation time \(t_{load}\), the waiting time until the ambient echoes have faded away \(t_{gate}\) and the time for data acquisition \(t_{day}=N\cdot T\):  
\[t_{cycl}=t_{load}+t_{gate}+t_{day}\] (A48)  
The number of averaging \(N_{A}\) that can be performed in the total time available for the measurement \(t_{total}\) is  
\[N_{A}=\frac{t_{total}}{t_{cycl}}.\] (A49)  
With the help of these variables and \(\omega\approx\omega_{d}\), we obtain the variance of the \(N_{A}\) times averaged natural frequency \(\sigma_{\omega_{d},N_{A}}^{2}\) to  
\[\sigma_{\omega_{d},N_{A}}^{2} = \frac{\sigma_{\omega_{d}}^{2}}{N_{A}}\] (A50) \[> \frac{\left(t_{load}+t_{gate}+t_{day}\right)\cdot\left(1-e^{-2 \alpha T}\right)^{3}\cdot r(\alpha NT)}{2T^{3}\cdot e^{-2\alpha T}\cdot(1-e^{- \alpha t_{load}})^{2}}\cdot\] \[\cdot\frac{F_{LNA}k_{B}\delta}{t_{total}\cdot P_{t}\cdot gt^{2} \cdot gt^{2}\cdot e^{-2\alpha t_{gate}}\cdot e^{-2\alpha n}{H_{R}}^{2}( \omega)}\cdot\left(\frac{4\pi R}{\lambda}\right)^{4}\]  
The second part of the above equation only scales the result and can be summarized into a constant. Figure 14 visualizes Equation (A50) for a resonator with a quality factor of 100 and a resonance frequency of 1 Hz. In the left graph, the quality factor of the communication channel was set to 10, i.e., \(t_{gate}=10\), and \(t_{load}=t_{day}\). We see a minimum in \(sigma_{\omega_{d},N_{A}}\) after 85.75 oscillations, i.e., the oscillator was not fully charged, and sampling is stopped before the resonator has completely settled. The gain from starting a new measurement and then averaging the data results in slightly higher accuracy than fully exciting the oscillator and sampling the entire decay. However, the graph is very flat between 0.85 and 1.0 \(Q/f_{d}\) and \(\sigma_{\omega_{d},N_{A}}\) only differs by 1%. Fully loading and sampling the oscillation might lower Fresnel ripples in the spectrum.  
In the right diagram of Figure 11, \(t_{gate}\) was varied between 1 s and 40 s, i.e., the quality factor of the mobile channel was set between 1% and 40% of the quality factor of the resonator, and \(t_{load}=t_{tag}\) was left. The optimal time for \(t_{datq}\) slowly increases from 0.85 to 0.90 \(Q/f_{d}\) as the length of \(t_{gate}\) increases in order to increase the proportion of sampling in the total available time. As more time is lost due the gate, the averaged half width decreases.
## 5 Conclusions  
This paper addressed the challenge of accurately estimating the pose of ILI robots and pipe diameters using a 3D ToF sensor. We identified and analyzed the limitations of the C-EPD scheme, highlighting its estimation bias due to the random depth errors of the 3D ToF sensor. To address these issues, we proposed the RU-EPD scheme. RU-EPD uses an error-based residual function to eliminate estimation bias and improve accuracy. Simulations and experiments demonstrated that RU-EPD significantly outperformed C-EPD, achieving robust and unbiased estimations under varying conditions. Specifically, RU-EPD achieved an at least six times lower estimation bias and a 2.5 times smaller estimation error range in pipe diameter and about a 2 times smaller estimation error range in pose estimation. RU-EPD enabled the identification of the change in thickness among the pipes. In summary, RU-EPD offers a practical and reliable solution for pipeline inspection. Future work will explore three key areas: (1) enhancing the robustness of RU-EPD through theoretical convergence analysis, (2) developing an efficient point sampling scheme for real-time estimation, and (3) extending RU-EPD to handle complex pipeline geometries such as elbows and T-junctions. In these geometries, RU-EPD needs to adapt to the torus model or a combination of ellipse, plane, and torus models.  
Conceptualization, H.-H.N. and H.-Y.J.; Data curation, H.-H.N., J.-H.P., J.-J.K. and K.Y.; Formal analysis, H.-H.N. and H.-Y.J.; Funding acquisition, D.-K.K. and H.-Y.J.; Investigation, H.-H.N. and H.-Y.J.; Methodology, H.-H.N. and H.-Y.J.; Project administration, D.-K.K. and H.-Y.J.; Resources, H.-H.N. and H.-Y.J.; Software, H.-H.N., J.-H.P., [PERSON]-[PERSON] and K.Y.; Supervision, D.-K.K. and H.-Y.J.; Validation, H.-H.N., J.-H.P., [PERSON]-[PERSON] and K.Y.; Visualization, H.-H.N.; Writing--original draft, H.-H.N.; Writing--review and editing, H.-H.N. and H.-Y.J. All authors have read and agreed to the published version of the manuscript. This research was supported by the \"Development of Next Generation Pipeline Robot Technology for the Long-Distance Inspection of the Low Pressure of Gas Pipeline\" (RD 2019-0251) research project from Korea Gas Corporation (KOGAS). The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 4 Model Building
### Class Imbalance  
Upon inspecting the percentage distribution of the records between the \"yes\" (or \"no\") attribute in the target variable \"attended\", we find a considerable imbalance between both classes. An imbalanced classification problem is well-known, in which the distribution is biased. Figure 14 shows 90% of the dataset's records are labelled as \"yes\" and 10% are labelled as \"no\".  
Imbalanced classifications are a challenge for predictive modelling as most of the ML algorithms used for classification were designed based on the assumption of an equal number of examples for each class. This model generally results in poor predictive performance, specifically for the minority class. Thus, the problem is more sensitive to classification errors for the minority class than the majority class [40].  
To solve the problem of class imbalance, and after testing several different under-sampling and oversampling algorithms, the Synthetic Minority Oversampling Technique  
Figure 13: Flow-chart of proposed research framework.  
(SMOTE) algorithm provided by the Imbalanced-learn (imblearn) ([[https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)]([https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)), accessed on 25 November 2021) library yielded the most considerable performance improvement. Not only did SMOTE solved the issue of class imbalance, but it also improved the classification performance metrics, mainly in precision and recall measures. Figure 15 shows the target class after applying the oversampling algorithm.
## 3 MO Isolator
### Numerical Results  
When external magnetic field \(H_{0}\) = \(7.4\times 10^{5}\) A/m is applied on the two rectangular ferrite sheets, with length, width, and thickness of L = 20 mm, M = 1.5 mm, and N = 0.5 mm, respectively, the function of the MO isolator is calculated by FEM, as shown in Figure 5. The electromagnetic field propagations are simulated at the central frequency of 26 GHz with scattering boundary condition (SBC).  
As shown in Figure 5a, a signal launched from the input port (Port 1) is transmitted stably to the transmission port (Port 2). Irreversibly, when a signal is launched form Port 2, the power of the signal is lost by the ferromagnetic resonance, and Port 1 is isolated, as shown in Figure 5b. The MO isolator's function of one-way transmission and reverse isolation are perfectly implemented. In Figure 6, the transmission characteristics of the MO isolator are also numerically investigated within the frequency range from 22 to 28 GHz, which is totally included in the PBG mentioned above.  
The numerical results show that the isolation of the MO isolator is optimized to be 49.49 dB at 26.30 GHz by finite element method (FEM) and keeps above 15 dB between 24.85 to 27.15 GHz (the relative bandwidth is about 8.85%). The MO isolator also has an excellent forward transmission characteristic that the transmission efficiency keeps above \(-\)3 dB in a wide frequency range from 22.00 to 27.40 GHz. The reflections (S\({}_{11}\), S\({}_{22}\)) of the two ports are both little, which keep below \(-\)20 dB in the whole frequency range, as shown in Figure 6.  
Figure 5: The function of the MO isolator at 26 GHz: (**a**) Electromagnetic field propagation from Port 1 to Port 2; (**b**) Electromagnetic field launched from Port 2 but Port 1 isolated.
# Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression
## 2 Methods
### Inversion Method
The smaller \(e\) is, the more focused the model parameters are. If \(m\) is very small, the value of \(w_{e}\) tends to 0, and if \(m\) is very large, the value of \(w_{e}\) tends to 1. When minimizing the objective function, it makes the value of \(w_{e}\) smaller, resulting in more \(m\) tending to 0 and thus obtaining a sparse solution. In this paper, the regularized conjugate gradient (RCG) method was used to solve the objective function [16]. Since the magnetism in the crust tends to be within a specific range, the addition of a physical upper and lower bound constraint can ensure that the values of the model parameters are within a reasonable range during iterative calculations [46], as shown in Equation (9), where \(m_{min}\) and \(m_{max}\) denote the upper and lower bounds of the magnetic parameter values, which can be obtained from geological data or logging data, respectively. \[\left\{ \begin{array}{ll}\text{if}&m_{j}<m_{\text{min}},\text{then}&m_{j}=m_ {\text{min}}\\ \text{if}&m_{j}>m_{\text{max}},\text{then}&m_{j}=m_{\text{max}}\end{array} \right. \tag{9}\]  
It is worth noting that the amount of observational data is often very large, resulting in a large sensitivity matrix \(A\). An oversized \(A\) places high demands on computer storage when solving the minimum of the objective function. In addition, the sensitivity matrix \(A\) increases the computational load of the solution process, resulting in a very slow efficiency [36]. In this paper, we solve this problem by compressing the amount of data.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 7 Security Challenges and Solutions
In the absence of authentication mechanisms, any adversary can impersonate as a legitimate user to steal information and interfere communication link of legitimate users. In some attacks, the attacker can monitor the trajectories of UAV and can use this location information for any malicious or illegal activity. Such attacks can leverage the UAVs around the victim UAV. Another critical challenge is the eavesdropping that occurs due to the lack of protective mechanism such as encryption. In such attacks, UAV's data can be accessed by the adversary. Non-repudiation can be utilized to confirm that a node has issues specific information. This mechanism prevents from denial of behavior and strengthens various tasks [117]. The key objective of this mechanism is to inquire specific information on the emergence of any security risk. Integrity of UAV mission is of paramount importance which ensures data transmission without any interruptions and received data is accurate. In the absence of integrity protection mechanism, malicious attacks can damage original data and thus it becomes invalid. One crucial issue is a malicious hardware attack which can tamper UAV's hardware to change its intended behavior [118]. This attack is carried out with the intention to steal confidential data or cause a failure in UAV's mission. Any attack to interrupt UAV's flight control and communication links to alter mission parameters is known as flight control computer attack. This attack can be mitigated through onboard software and hardware mechanisms. It can include real-time monitoring, instantaneous estimation of the controller, alert warning and immediate action on any alteration from the intended controller model. Some attacks can misguide the UAV's trajectory after causing an intentional tampering on onboard navigational sensors. These attacks can cause error in sending accurate location and position of UAVs to the control system.
## 1 Introduction
A major limit of all seeding schemes based on lasers is created by the achievable repetition rate, which is typically below tens of kHz, and their extension to higher repetition rates has so far only been studied theoretically [25; 26; 27]. The single spike SASE operation [28; 29; 30], as well as self-seeding processes [31; 32], are able to achieve only partial longitudinal coherence. Another FEL configuration capable of producing stable radiation pulses is the FEL Oscillator (FELO), in which the radiation process builds up via multiple passes of a stable and high repetition rate pulse train of electron bunches through an undulator embedded in a low-loss optical cavity [23; 33; 34; 35; 36; 37]. The lower gain required in such systems relaxes the requirements on peak current and electron beam quality. Thanks to the technological advancements in the field of optics and mirrors fabrication [38], FEL oscillators (XFELO) [37; 39] and regenerative amplifiers (RAFEL) based on X-ray cavities [40; 41; 42; 43; 44] have been proposed as a direct source of coherent X-rays [36; 37; 40; 45], or as seed for subsequent cascades [24; 25; 46]. However, limit cycle oscillations have been observed and studied in FELOs [47]: these fluctuations, depending on the cavity specifics and working point, can even be comparable to the fluctuations found in SASE FELs. Among all the methods for producing coherent X-ray radiation, we analyze here the case of a tender X-ray FEL Oscillator, driven by a moderate energy electron beam and based on a short period undulator. Diamond mirrors and beam splitters, foreseen at about 3 keV, constitute a ring cavity where the radiation pulses are synchronized with the electron bunches. In this paper, we show the operation of such a source. The electron beam is supposed to be generated by the accelerator of the MariX project (Multi-disciplinary Advanced Research Infrastructure for the generation and application of X-rays) [48], based on superconducting cavities and equipped with an arc compressor. The compact footprint (with a total dimension of less than 500 m) of the infrastructure and the contained costs should permit its construction in medium-sized research infrastructures or within university campuses. Hereafter, we will discuss the nominal parameters and start-to-end simulations of the system. Final comments will be presented in the conclusions.
## 2 Materials and Methods
### Methodology
#### 2.3.3 Accuracy Metrics  
The comparison of the observed and estimated values of the backscattering coefficients was evaluated using two statistical criteria: the mean values of the correlation coefficient (r, Equation (1)) and the root mean square error (RMSE, Equation (2)). The performances obtained at the intra-plot spatial scale were finally compared with those previously obtained at the plot spatial scale, using the same precision metrics [1; 24; 3; 4; 5; 6; 7; 24].  
\[r~{}=~{}\frac{\text{COV}(o,p)}{\sigma_{o}\sigma_{p}}, \tag{1}\]  
\[RMSE~{}=~{}\sqrt{\frac{\sum_{i=1}^{n}(p_{i}-o_{i})^{2}}{n}}, \tag{2}\]  
where \(n\) is the number of observed values, \(o\) is the observed value, \(p\) is the predicted value, COV is the covariance, and \(\sigma_{o}\) and \(\sigma_{p}\) are the standard deviations of observed and predicted data, respectively.