esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence in Medicine and Orthopedics
### AI in Orthopedic Practice  
In orthopedics, AI applications are expanding rapidly:  
Imaging Analysis: Deep learning algorithms can detect and classify fractures on radiographs with remarkable accuracy, sometimes surpassing that of human experts [30]. For instance, CNNs have been used to identify wrist fractures on radiographs, achieving performance comparable to experienced radiologists [31]. [PERSON] et al. [32] conducted a systematic review highlighting AI's role in radiological pediatric fracture assessment, emphasizing its potential to improve diagnostic accuracy and reduce interpretation times.  
Automated Bone Age Assessment: Particularly relevant to pediatrics, AI-driven tools can automate bone age assessment using hand radiographs, providing rapid and consistent evaluations crucial for managing growth disorders [33].  
Surgical Planning and Intraoperative Guidance: AI algorithms assist in selecting appropriate implants, determining optimal surgical approaches, and simulating surgical outcomes [34]. Intraoperatively, AI can enhance robotic-assisted surgeries by providing real-time feedback and adjustments.  
Postoperative Care and Rehabilitation: Wearable devices and mobile applications equipped with AI monitor patients' progress, adherence to rehabilitation protocols, and detect early signs of complications. By analyzing data from sensors tracking movement, strength, and range of motion, AI provides personalized feedback and adjusts rehabilitation programs [35].  
The potential for AI to predict pediatric fracture overgrowth specifically is significant, though it remains a relatively unexplored area. Integrating diverse data sources--including detailed clinical information, imaging findings, genetic markers, and treatment modalities--could enable the development of comprehensive predictive models. Machine learning algorithms could analyze these complex data to identify patterns and risk factors associated with overgrowth, which may not be evident through traditional analysis.
## 3 Data Description and Evaluation Metrics
Additionally, an input-output distribution analysis is conducted to identify and mitigate the presence of outliers in the dataset, which could disrupt model training and evaluation. Figure 6b illustrates that there are no apparent outliers in the dataset. Furthermore, it is evident that the distribution of features \(c,\varphi,\sigma_{h},\sigma_{v},\sigma_{H}\) is more uniform, whereas the distribution of features \(E,\
u,P_{w},r\) is denser. Consequently, the primary focus of the study appears to be on features \(c,\varphi,\sigma_{h},\sigma_{v},\sigma_{H}\). Reasonably setting evaluation metrics for model assessment is crucial for selecting the optimal model correctly. Drawing from previous ML studies [52; 53; 54; 55; 56; 57; 58; 59; 60], commonly used evaluation metrics include the Pearson Correlation Coefficient (R\({}^{2}\)), Mean Absolute Error (MAE), Variance Accounted For (VAF), and Root Mean Squared Error (RMSE). The calculation methods for these metrics are outlined as:  
\[R^{2}=\frac{\left[\sum\limits_{i=1}^{n}{(f_{i}-\overline{f})(y_{i}-\overline{y })}\right]^{2}}{\sum\limits_{i=1}^{n}{(f_{i}-\overline{f})^{2}\sum\limits_{i=1 }^{n}{(y_{i}-\overline{y})^{2}}}} \tag{9}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}|f_{i}-y_{i}| \tag{10}\]  
\[VAF=1-\frac{\text{var}(f_{i}-y_{i})}{\text{var}(y_{i})} \tag{11}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{(f_{i}-y_{i})}^{2}}{n}} \tag{12}\]  
where \(f\) denotes the predicted value, \(\overline{f}\) denotes the mean of the predicted values, \(y_{i}\) denotes the actual value, \(\overline{y}\) denotes the mean of the actual values, \(n\) denotes the number of data. Figure 6: Correlation and distribution of inputs and outputs: (**a**) correlation; (**b**) distribution.
## 1 Introduction
Therefore, the lifetime of the scroll compressor should be studied by considering both the crack initiation and crack propagation behaviors. Hence, crack propagation should be considered to predict the lifetime until failure of the scroll compressor. The general method to depict crack propagation is the Paris law as follows; [19; 20]. \[\frac{\text{da}}{dN}=\Delta G>G_{1c} \tag{7}\]  
where \(N\) is the life cycle, a is the length of the crack, \(G\) is the energy release rate, \(G_{1c}\) is the critical energy release rate. This equation presents that if the energy release rate exceeds critical energy release rate, the crack propagates. This method is based on the linear elastic fracture mechanism (LFEM). Thus, if plastic deformation occurs in the structure, it tends to be inaccurate. To compensate for the weakness, the Darveaux method [20; 21] should be considered (Equation (8) and Figure 1). By using strain energy density per cycle, the Darveaux coefficient is calculated as follows; [20; 21]. \[N_{0}=c_{1}(\Delta w)^{c_{2}},\ D_{N+\Delta N}=D_{N}+\frac{\Delta N}{L}c_{3}( \Delta w)^{c_{4}}\ (c_{1},c_{2},c_{3},c_{4}-material\ constant) \tag{8}\]  
where \(N_{0}\) is cycle number for damage initiation, \(\Delta w\) is inelastic hysteresis strain criteria. \(D_{N}\) is damage variable for degradation stiffness assumption. Thus, the degradation of the element can be calculated as follows; [20]. \[\sigma=(1-D)\overline{\sigma},\ \text{is the stress of the current increment}. \tag{9}\]The Darveuax method was used in many recent studies. [PERSON] [22] used the Darveux method to predict solder joints. By tracking individual solder failures, he mentioned that the Darveuax method can depict the change of inelastic region during repeated cyclic load. Thus, he suggested that the Darveuax method have advantages for depicting ductile material due to the inelastic strain energy density accumulated per cycle. [PERSON] [23] proposed a method to calibrate the damage material parameters using both the Darveuax method and the strain-life curve. Even though there was no recent research which applied the Darveuax method to the scroll compressor, these recent studies have become helpful examples. In the study, a parametric investigation is presented to determine the appropriate parameter for the customized C-specimen test. The entire cycle is calculated separately by dividing the initiation and propagation cycles. The crack initiation cycle is calculated by using Altair Optistruct software, which can accumulate element damage during cyclic loading. Specifically, ABAQUS 6.14 is used to calculate the crack propagation cycle. The specific results of the analysis are expected to aid in determining safe specifications of the scroll compressor.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 4 Analysis and Discussion
### GNSS Receiver Performance  
Instead of a single frequency (L1) receiver, a dual frequency (L1, L2) receiver could be substituted and this would improve accuracy by providing more data (i.e., signals from  
Figure 9: GNSS buoy at 13:24 on 29 August, prior to recovery; spilling waves can be seen in the background.  
twice as many satellite) for processing by the RTKLIB software. Since this experiment was conducted, Emlid Reach have produced an upgrade to the receiver for a similar cost (e.g., using a F9 Ublox dual frequency GNSS receiver). It also supplies a helix antenna, an improvement on the patch antenna; its larger design would reduce the problem with spilling waves, and be less susceptible to reflections (which increase the signal to noise ratio). Use of a duplicate set-up for both rover and base station, both recording at 5 Hz (in this experiment, the base station was recording at 1 Hz) would eliminate problems associated with unknown antenna bias (with the patch antenna) [8], and would allow additional options in the RTLKLIB software to be used, which would then likely improve the position solutions.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology  
The block diagram of the proposed deep two-stream convolutional network is shown in Figure 1 with its respective spatial and temporal streams. The spatial stream uses each video frame as a single image for feature extraction and processing. It uses a pre-trained ImageNet model [30] as the feature extraction part. For the temporal stream architecture, we use a stack of optical flow fields as input to the architectures. Doing so achieves two goals: firstly, the data being processed reduces as there are less data in optical flow fields as compared to RGB images; secondly, the optical flow will capture the moving regions in an image, thus making it easier to identify the HAR. The details of each stream architecture are given in Sections 3.2 and 3.3.
# Explaining Deep Learning-Based Driver Models
## 3 Application of XRAI in Driver Modeling
### Target ADAS
#### 3.1.1 Emotions Model  
The purpose of the emotions model [7] is, given an image of a person's face, predict which is their mood at that moment by analyzing the image. In particular, this model attempts to detect one of seven emotions: happiness, sadness, neutral, fear, anger, surprise, and disgust. To train this model, four different datasets have been used. These datasets have the characteristics specified in Table 1.  
However, before these images are analyzed by the convolutional neural network, it is necessary to preprocess them. The preprocessing process applied in [7] is summarized in Figure 2. It should be noted that, for the face recognition step, OpenCV's face recognition library is used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset’s Name** & **No. of Subjects** & **No. of Samples** & **Image Size** & **Specific Information** \\ \hline The Extended Cohn-Kanade _Dataset (CK+)_[61; 62] & 123 & \begin{tabular}{c} +10.000 \\ (593 different sequences) \\ \end{tabular} & 640 \(\times\) 490 pixels & \begin{tabular}{c} Diverse dataset: it includes men \\ and women of different races \\ \end{tabular} \\ \hline Facial Expression Recognition _Challenge: FER (2013)_[63] & Undefined & +35.000 & 48 \(\times\) 48 pixels & \
\begin{tabular}{c} It was created by searching \\ in Google the target emotions. \\ A small percentage of the images \\ is mislabelled or does not represent \\ a person \\ \end{tabular} \\ \hline The japanese facial expression _(JAFFE)_[64; 65] & 10 & 219 & 256 \(\times\) 256 pixels & The subjects are all Japanese women \\ \hline _The Karolinska Directed Emotional Faces (KDEF)_[66] & 70 & 4900 & 562 \(\times\) 762 pixels &
\begin{tabular}{c} 50\% men, 50\% women. \\ Photographs taken from 5 \\ different angles \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used to train the emotions model.  
The preprocessed image, after Step 3, is the input given to the model, which processes it with a convolutional neural network trained with the architecture shown in Figure 3.
## 3 Key Technologies in Trust Management for WSNs
### Approaches to Extracting Trust Evidence
#### 3.3.3 Extracting Packet Forwarding Rate  
Most models used listening and watchdog mechanisms to extract the packet forwarding rate. [PERSON] et al. [10] employed two variations of the listening and watchdog methods: one where the sender listens to the receiver, and another where a third party simultaneously listens to both the sender and receiver to gather evidence of the forwarding rate. They also used a method to determine the forwarding rate of control packets via ACK messages in SDWSNs. We believe the third-party method, which monitors both the sender and receiver simultaneously, is redundant with the sender-listening method and adds unnecessary complexity. For instance, a sensor node would need to initiate a watchdog for both the data packets it sends and the source data packets it overhears. Additionally, the issue of duplicate records must be addressed.  
[PERSON] et al. [35] mentioned extracting the forwarding rate by querying the statistical information from the traffic profiles of neighboring nodes. However, this method depends on interactions involving request and response messages related to traffic profiles, resulting in increased communication overhead.  
Based on these observations, we summarize the approach for extracting the forwarding rate of data or control packets as follows. Sensor nodes enable the promiscuous receiving mode and start a watchdog timer after sending a packet. If the destination node successfully forwards the packet before the watchdog expires, the successful forwarding count of the destination node is incremented by one; otherwise, the failed forwarding count is incremented by one.  
The ACK-based method for extracting the control packet forwarding rate can serve as a supplement to the listening method. If either the forwarded packet or the ACK message is received by a sender during the monitoring period, it can be considered as a successful forwarding.
## 2 Introduction
Moreover, the above-mentioned papers lack a comparing scheme of geometrically identical antenna arrays, which would explain the requisite reasons for selecting appropriate antenna arrays. Importantly, the NVL technique has yet to be implemented to improve performance. Taking into account all of these considerations, we attempt to propose a novel NVL technique that has the optimum performance regarding the SLL, beam width, interference cancellation, and improved SINR performances compared with other existing techniques. A comparison of geometrically comparable antenna arrays is presented in this study in order to see how NVL stacks up against different methods. This study proposes the linearly arranged circular antenna array (LCAA), the linearly arranged circular planer antenna array (LCPA), the linearly arranged rectangular antenna array (LRAA), and the linearly arranged concentric circular antenna array (LCCAA). Because the conventional technique fails to peak the rightmost signal, the optimal technique is utilized to peak the true signal and to eliminate interference. These proposed beamformers are further studied using robust techniques that dramatically reduce the beam width, such as ODL, FDL, VDL, and NVL. Furthermore, the NVL method has the smallest beam width. With 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) discrepancies, the NVL approach of SINR outperforms the others. The SINR of LCCAA shows a better performance than the others. The LCPAA beamformer outperforms the NVL robust approach, with comparatively better output powers.
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 3 General Framework of MOUTP Based on TSCEA
### Multi-Objective Model
#### 3.1.2 Establishment of Constraints  
The first constraint is the safe distance of the UAV trajectory from obstacles:  
\[\sum_{k=0}^{P_{N}}\|h_{k}^{*}-h_{k}\|>0 \tag{6}\]  
where \(h_{k}^{*}\) denotes the elevation value below the \(k\)-th key trajectory point and \(h_{k}\) denotes the actual distance below the \(k\)-th key trajectory point.  
The second constraint is the maximum flight altitude of the UAV:  
\[\sum_{k=0}^{P_{N}}\|h_{\max}-h_{k}\|>0 \tag{7}\]  
where \(h_{\max}\) denotes the maximum flight altitude of the UAV.  
The third constraint is the speed constraint of the UAV:  
\[V_{uav}=[V_{min},V_{max}] \tag{8}\]  
where \(V_{min}\) denotes the minimum speed of the UAV and \(V_{max}\) denotes the maximum speed of the UAV.  
The fourth constraint is the UAV flight slope \(\theta\), which is the angle between the UAV and the horizontal:  
\[\theta\geq\arctan(\frac{|z_{k+1}-z_{k}|}{|(x_{k+1}-x_{k},y_{k+1}-y_{k})|}) \tag{9}\]  
where \(z_{k}^{i}\) denotes the altitude of the \(k\)-th key trajectory point of the \(i\)-th UAV in three-dimensional space, \(x_{k}^{i}\) denotes the horizontal coordinate of the \(k\)-th key trajectory point of the \(i\)-th UAV in three-dimensional space, and \(y_{k}^{i}\) denotes the vertical coordinate of the \(k\)-th key trajectory point of the \(i\)-th UAV in three-dimensional space.  
The fifth constraint is the UAV flight corner \(\eta\):  
\[\eta\geq\arctan(\frac{a_{i}^{k}(y)}{\sqrt{\left(a_{i}^{k}(x)\right)^{2}+ \left(a_{i}^{k}(z)\right)^{2}}}) \tag{10}\]  
where \(a_{i}^{k}(x)\), \(a_{i}^{k}(y)\), and \(a_{i}^{k}(z)\) denote the acceleration of the \(k\)-th path point of the \(i\)-th UAV in the direction of the \(x\), \(y\), and \(z\) coordinate axes, respectively.
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 3 Results and Discussion
### Classification Models
To minimize the labeling effort caused by above mentioned reason, spectral information of pixel can be taken into consideration when labeling. Furthermore, although the averaged reflectance values of the spectra of decay and black fungus were distinctly different (Figure 5), the reflectance of the spectra of dark decay on mature green jujube surface was closer to that of black fungus, and might lead to confusion. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{8}{c}{**Classified as**} \\ \cline{2-7}  & **Normal** & **Rusty Spot** & **Decay** & **White Fungus** & **Black Fungus** & **Crack** & **Glare** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{**Normal**} & 94.32 & 0.16 & 0.21 & 0 & 0 & 0.03 & 5.28 \\  & 95.36 & 0.32 & 0.21 & 0.03 & 0 & 0 & 4.08 \\ \multirow{2}{*}{**Rusty spots**} & 4.48 & 91.23 & 2.39 & 0 & 0.03 & 0.16 & 1.71 \\  & 4.45 & 90.41 & 2.72 & 0.03 & 0.11 & 0.41 & 1.87 \\ \multirow{2}{*}{**Decay**} & 0.75 & 11.06 & 83.49 & 0.05 & 3.26 & 0.05 & 1.34 \\  & 0.5 & 9.92 & 84.81 & 0.1 & 3.68 & 0.05 & 0.94 \\ \multirow{2}{*}{**White fungus**} & 0 & 0.12 & 0.09 & 95.47 & 1.29 & 3.03 & 0 \\  & 0 & 0.12 & 0.09 & 94.48 & 1.56 & 3.75 & 0 \\ \multirow{2}{*}{**Black fungus**} & 0.22 & 2.06 & 3.64 & 0.29 & 93.79 & 0 & 0 \\  & 0.29 & 1.54 & 2.94 & 0.99 & 94.24 & 0 & 0 \\ \multirow{2}{*}{**Crack**} & 0.27 & 4.74 & 0.37 & 2.69 & 0.13 & 81.73 & 10.07 \\  & 0.13 & 4 & 0.35 & 4.21 & 0.08 & 86.25 & 4.98 \\ \multirow{2}{*}{**Glare**} & 0.14 & 0 & 0.08 & 0.59 & 0 & 0.03 & 99.16 \\  & 0.19 & 0.03 & 0.16 & 0.03 & 0 & 0.18 & 99.41 \\ \multirow{2}{*}{Model 1} & \multicolumn{2}{c}{Model 1} & \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 2: Confusion matrices of the classification models with the use of linear scan dataset.
###### Abstract  
We considered the problem of the estimation of signal-to-noise ratio (SNR) with a real deterministic sinusoid with unknown frequency, phase and amplitude in additive Gaussian noise of unknown variance. A blind SNR estimator that does not require the knowledge of the instantaneous frequency of the sinusoid, through separate estimation of signal and noise power, was derived using the method of moments, a general method to derive estimators based on high-order moments. Statistical performances of the proposed estimators were studied theoretically through derivation of Cramer-Rao lower bounds (CRLBs) and asymptotic variances. Furthermore, results from Monte-Carlo simulations that confirm the validity of the theoretical analysis are presented along with some comments on the use of proposed estimators in practical applications.  
Keywords:signal-to-noise ratio; Cramer-Rao bound; estimation; method of moments; asymptotic performances +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Analysis of Diversity Performance  
Multiple parameters, such as the envelope correlation coefficient, the diversity gain, and the mean effective gain, are used to assess the efficacy of the proposed MIMO antenna.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 5 Conclusions  
The present study demonstrates that the Q-LAC device, employing near-infrared spectroscopy technology, offers a reliable and non-invasive method for assessing muscle oxygen saturation (SmO\({}_{2}\)) during incremental cycling exercise. Our findings indicate that this device is a trustworthy instrument for measuring venous hemoglobin oxygen saturation at submaximal exercise intensities. The low standard errors associated with these measurements further support the reliability of this technology.  
Near-infrared spectroscopy, as implemented in the Q-LAC device, proves to be a valuable tool for investigating exercise intensity and the dynamics of muscle oxygenation in peripheral regions. The non-invasive nature and relative affordability of this technology make it a practical option for researchers and practitioners in exercise physiology and sports science. This approach provides insights into both peripheral and central adaptations, which may be influenced by various factors such as training status, injury, nutritional state, or environmental conditions, all of which can impact cardiorespiratory performance.  
In conclusion, the Q-LAC device shows promise as a complementary method to traditional measures of physiological responses to exercise. Its ability to assess SmO\({}_{2}\) changes during exercise offers an additional perspective on exercise-induced adaptations. Further research is warranted to explore the full potential of this technology across various exercise modalities and populations, as well as its integration with other physiological measures to enhance our understanding of exercise physiology.  
**Author Contributions:** Conceptualization, K.M., M.S., M.N., B.P. and P.Z.; methodology, K.M., M.S., M.N., B.P. and P.Z.; formal analysis, K.M., M.S., M.N., B.P. and P.Z.; investigation, K.M., M.S., M.N., B.P. and P.Z.; resources, K.M.; data curation, K.M.; writing--original draft preparation, K.M., M.S., M.N., B.P. and P.Z.; writing--review and editing, K.M., M.S., M.N., B.P. and P.Z.; visualization, K.M. and B.P.; supervision, K.M.; project administration, K.M.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (the local Research Ethics Committee (20/2022) of Wroclaw University of Health and Sport Sciences, Poland.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the author ([PERSON]) on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 The Improved 3D Reconstruction Method of Satellite Images  
In this paper, we propose an improved satellite image reconstruction method based on the GAN image enhancement model. The processing flow chart of the proposed reconstruction method is shown in Figure 1.  
There are two types of input data, which are satellite image pair and the corresponding RPC files. Firstly, the proposed method implements enhancement processing on the input satellite images, which is based on the GAN model. Secondly, the enhanced satellite image pair and its corresponding RPC files are processed by stereo rectification and stereo matching. After these two processes, a dense disparity map will be generated. Triangulation-based altitude estimation is then performed on the disparity map, and finally, a high-quality 3D point cloud and its digital surface model (DSM) are generated.