esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks
## 3 Experimental Tests
### Fatigue Test
This accentuates a marked escalation in the decline rate, highlighting that longer crack lengths precipitate a sharp decrease in the laminates' fatigue life, with the trend intensifying as lengths increase. The fitting curve corroborates this with a sharp decline. Additionally, elevating the crack angle correlates with a reduction in the material's fatigue resistance, with significant decreases of 24.3% from 30\({}^{\circ}\) to 60\({}^{\circ}\) and 14.99% from 60\({}^{\circ}\) to 90\({}^{\circ}\). The fitting curve begins with a sharp plunge, which then transitions into a slower, more gentle decline. Conversely, an increase in the distance S between cracks correlates with an extended fatigue life, with increases of 5.6% from 0 mm to 1 mm and 9.89% from 1 mm to 2 mm. The above indicates that when the carbon fiber composite laminate has vertical cracks, the greater the spacing between the cracks S, the longer the fatigue life of the material. This means that under the same crack size and other conditions, the laminate can withstand longer cycles of loading without failure. However, if the length of the crack L\({}_{2}\) in the laminate increases, its fatigue life will decrease sharply, and this trend of decrease is more significant than the impact of changes in the internal crack angle \(\theta\) on the fatigue life. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & **S Inter-Crack Distance** & **\(\circ\) Crack Angle** & **L\({}_{\text{}}\) Crack** & **L\({}_{\text{}}\) Crack** & **Fatigue Life** & **Average** & **CV** \\  & **Distance** & (\({}^{\circ}\)) & **Length** & **Length** & **(Cycle)** & **(Cycle)** & **(Cycle)** & **(\%)** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(Cyclic)** & **(Cycle)** & **(\%)** \\ \hline B2-3-1 (F) & 1 & 90 & 4 & 6 & 33,404, 36,683, 35,673, 35,021, 36,041 & 35,359 & 3.53 \\ C2-3-1 (F) & 2 & 90 & 4 & 6 & 37,833, 37, 586, 417,34, 39,241, 38,254 & 38,856 & 3.97 \\ A2-1-1 (F) & 0 & 30 & 4 & 6 & 54,505, 547, 690, 502, 51, 5103, 48,759 & 52,032 & 4.92 \\ A2-2-1 (F) & 0 & 60 & 4 & 6 & 40,636, 37,579, 41,193, 38,542, 38,967 & 39,383 & 3.80 \\ A2-3-1 (F) & 0 & 90 & 4 & 6 & 32,54, 395, 319, 35, 358, 303, 3691 & 33,348 & 4.55 \\ A3-3-1 (F) & 0 & 90 & 4 & 4 & 327,46, 49,530, 384, 415, 450, 47, 465 & 42,690 & 16.33 \\ A3-3-1 (F) & 0 & 90 & 4 & 8 & 21,464, 21,637, 18,470, 18,565, 19,547 & 19,937 & 7.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fatigue test sample parameters and results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Fatigue Test Parameters** \\ \hline Stress amplitude & 0.8\(\sigma_{s}\) \\ Loading frequency & 30 HZ \\ Loading wave form & Sine wave \\ Stress ratios & 0.1 \\ Temperature: 25 \({}^{\circ}\)C \\ Test environment & humidity level: 60\% \\ Test equipment & Electro-hydraulic servo fatigue testing machine \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fatigue tensile test parameter settings.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Controlled Object** & **Main Contributions** &
\begin{tabular}{c} **FOV** \\ **Constraint** \\ \end{tabular} \\ \hline
[19] & \
\begin{tabular}{c} Nonholonomic mobile \\ robots \\ \end{tabular} &
\begin{tabular}{c} Combining the RRT algorithm with virtual goal-guided \\ constraint planning increases the computational burden and \\ may affect real-time performance. \\ \end{tabular} & \(\surd\) \\ \hline
[21; 22] & 6-DOF robot arm &
\begin{tabular}{c} In 3D space, trajectories that satisfy field-of-view constraints \\ rely on accurate environment and system modeling. \\ \end{tabular} & \(\surd\) \\ \hline
[23] & \
\begin{tabular}{c} 6-DOF robot \\ manipulator \\ \end{tabular} &
\begin{tabular}{c} The potential function approach may suffer from the problem \\ of local minima, which may cause the control system to \\ stagnate at a local optimum. \\ \end{tabular} & \(\times\) \\ \hline
[26] & Quadrotor &
\begin{tabular}{c} The behavior of the system is constrained by the visible set, \\ which is limited by a control barrier function. \\ \end{tabular} & \(\surd\) \\ \hline
[27] & \
\begin{tabular}{c} 6-DOF robot \\ manipulator \\ \end{tabular} &
\begin{tabular}{c} Actuator limitations and visibility constraints can be \\ addressed using the MPC strategy while considering \\ computational complexity. \\ \end{tabular} & \(\surd\) \\ \hline
[29] & WMRs &
\begin{tabular}{c} The Q-learning controller was designed for the simple \\ movements of wheeled robots. \\ \end{tabular} & \(\times\) \\ \hline
[31; 32] & Quadrotor &
\begin{tabular}{c} Designing adaptive servo gains with Q-learning can improve \\ control without considering FOV. \\ \end{tabular} & \(\times\) \\ \hline
[33] & WMRs & \begin{tabular}{c} Designing adaptive servo gains with DDPG can improve \\ control without considering FOV, and DDPG requires \\ extensive data interactions and training, which can result in \\ high computational cost and time overhead. \\ \end{tabular} & \(\surd\) \\ \hline Ours & \
\begin{tabular}{c} 6-DOF robot \\ manipulator \\ \end{tabular} &
\begin{tabular}{c} In the case where the system model is unknown and FOV \\ feature constraints are considered, the DQN directly enforces \\ the feature constraints using the mapped states of the camera \\ FOV image features. \\ \end{tabular} & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing relevant work. conducted on a 6-DOF manipulator equipped with a hand-eye camera. Sections 6 and 7 provide, respectively, the discussion and conclusions.
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### Setup of the Simulation Environment  
We implemented a network slicing simulation environment to simulate behaviors of a slice tenant and evaluate its profit and QoS. The tenant can trade resources according to the strategies defined in the simulation. Once the slice is created, the initial resource \(R^{ini}\) is allocated to the slice, and the slice has to keep the minimum resource (\(\min_{t}R_{n,t}=R^{ini}\)). We considered a scenario with 200 flows, where the ratio \(\varphi\) between inelastic and elastic flows is randomly chosen to be between 0 and 1. We set the number of time zones \(\tau\) to 10 and the lifetime of the slice to 100 time slots. Unless otherwise stated, the unit buying price and unit selling price of the bandwidth were set to $40 and $200, respectively, and the QoS weight \(\omega\) was set to 1.6. We assumed that requests for flow creation are issued following a Poisson process of rate \(\lambda\), and every flow has a random lifetime that follows an exponential random variable of rate \(\mu\). The initial resource \(R^{ini}\) is first bundled into slices and then allocated to slices.
# Intraoperative Use of Mixed Reality Technology in Median Neck and Branchial Cyst Excision
## 3 Results  
### Case 1. Fixation Mask  
Patient M. was diagnosed with a median neck cyst. The patient underwent MST with intravenous contrast enhancement and cystography (Figure 2). The MST was performed in the patient's selected position (the selected position is the uniform position of the patient during examination and surgery; in this example, it is the position in the CT scanner and on the operating table) on her back, in several stages. We used Q-fix (1 and 2) fixators produced in the USA in the form of a perforated thermoplastic cervicofacial mask-frame, with openings for operative access and intubation and designed for radiation therapy.  
The examination revealed for the first time that the lump was 5 cm long and consisted of a cyst with a double-blind fistula descending from the cyst mass down to the lower edge of the isthmus of the thyroid gland (Figure 3).  
For the preoperative preparation and the subsequent intraoperative hologram application, we used the 3D Slicer application to perform image segmentation and to build a 3D model of bone structures, flows, internal and external jugular vein, common and external  
Figure 3: Three-dimensional reconstruction (**left**) [21] and a 3D model built using the segmentation method in the 3D Slicer (**right**). The contrast media allowed for the visualization of the cyst with the double-blind fistula, which ends at the lower edge of the isthmus of thyroid gland).  
Figure 2: Cervical MST with intravenous contrast enhancement and cystography in the patient’s selected position, immobilized using a Q-fix system. Additional use of a bite splint installed between the central incisors to simulate the position of the lower jaw after intubation.  
carotid artery, larynx and tracheal cartilages, thyroid gland, and the cyst along with the fistula (Figure 3).  
Developed Approach  
For this approach, we used a Q-fix fixation mask, which allowed us to fix the head and pectoral arch in a certain position, with a possibility to replicate this position in the subsequent surgery [22]. Owing to the fact that the frame holding the mask remained unchanged (the masks were replaced), this marker could then be used multiple times in other surgeries involving this method.  
The surgery was performed in the patient's selected position--on her back, identical to the position during the MSCT (Figure 4).  
As a result, the fixation mask helped to ensure the patient's position on the operating table, identical to that during the MSCT examination. However, the main disadvantage of this method lies in the need for the production of individual masks for each surgery and their installment during the preoperative process. This requires considerable time and labor costs.
## 2 Optical Lattice Clock Based on \({}^{87}\)Sr  
### Level Structure and Cooling Transitions of \({}^{87}\)Sr  
The relevant energy levels of \({}^{87}\)Sr with transitions and wavelengths used in an optical lattice clock are shown in Figure 1.  
An oven, which is heated to a temperature of 500 \({}^{\circ}\)C, emits a strontium atomic beam at a velocity of 460 m/s. A Zeeman slower formed with ten coils and 461-nm laser is used to slow the velocity of the thermal atomic beam to about 50 m/s. The strontium atoms are further cooled and trapped in a two-stage magneto-optical trap (MOT). The strong dipole-allowed \((55^{2})^{1}S_{0}(F=9/2)-(55p)^{1}P_{1}(F=11/2)\) transition (461-nm, 32-MHz natural linewidth) is used for the first Doppler cooling stage; the corresponding cooling limit is roughly in the sub-millikelvin range. With the 679-nm and 707-nm lasers, optical repumping returns the \((55p)^{3}P_{2}\) and \((55p)^{3}P_{0}\) metastable states to the ground state \((55^{2})^{1}S_{0}\) to cool and trap more atoms. The intercombination \((55^{2})^{1}S_{0}(F=9/2)-(55p)^{3}P_{1}(F=11/2)\) transition (689-nm, 7.5-kHz natural linewidth) between the singlet and triplet states is used in the second Doppler cooling stage; the corresponding cooling limit is near the sub-microkelvin range. A stirring laser corresponding to the \((55^{2})^{1}S_{0}(F=9/2)-(55p)^{3}P_{1}(F=9/2)\) transition provides a rapid randomization among the ten Zeeman sublevels of the \((55^{2})^{1}S_{0}\) state. Additionally, it is also used as a pump laser to drive the entire population of the ten Zeeman sublevels to the \(m_{\rm F}=+9/2\)-\(m_{\rm F}=+9/2\) and \(m_{\rm F}=-9/2\)-\(m_{\rm F}=-9/2\) sublevels.  
After this two-stage cooling and trapping, roughly 3.5 \(\times\) 10\({}^{6}\) atoms are trapped in the red MOT with a temperature of 3.9 \(\mu\)K. The one-dimensional (1-D) horizontally oriented optical lattice is operated at 813 nm and traps the cooled atoms in the Lamb-Dicke regime [20]. The \((55^{2})^{1}S_{0}-(55p)^{3}P_{0}\) transition at 698 nm is used as the clock line because of its narrow natural linewidth of about 1 mHz.
## 2 Fundamental Background
### Defected Ground Structure Layouts
Analogously, it operates like an inductive-capacitive resonating circuit, where the antenna's resonance can be adjusted according to its dimensions. The SCSRR represents another form of SRR that is basically composed of two CSRR, as demonstrated in Figure 5c. The capacity of an SCSRR-based DGS to enhance out-of-band rejection without increasing the insertion loss stays remarkable [97; 98]. Therefore, a SCSRR defect structure consisting of two rectangular CSRRs connected with a slot of \(S_{W}\) and \(S_{L}\), as shown in Figure 6, was considered for investigation. The SCSRR structure was designed to operate at 5.8 GHz, at the same desired operating frequency of the MIMO antenna. The filtering characteristics of the proposed SCSRR structure were studied by utilizing the eigenmode analysis feature of the ANSYS High-Frequency Structure Simulator (HFSS) software 2020 R2. The Brillouin diagram for an unit cell SCSRR structure is plotted along the wave vector \(\beta\) of the periodic structure in Figure 7. Strong rejection characteristics were obtained for the SCSRRs, where the bandgap zone (4.5-6.5 GHz) is clearly seen within the operating frequency range of 5-7 GHz. In order to provide the impact of the slot in comparison to the traditional CSRR unit cell, a comparative eigenmode analysis is shown in Figure 7. The bandpass behavior of the proposed unit cell of SCSRR (CSRR with slot) can be viewed as two cascaded filters. An alternative way to analyze the bandgap behavior of the SCSRR is obtaining the scattering (S) parameters of a microstrip transmission line placed over the substrate. The proposed MIMO antennas employ a Rogers RO4003C as the dielectric substratewith \(\varepsilon_{r}\) of 3.55, tan\(\delta\) of 0.0027, and thickness \(h\) of 1.52 mm. The SCSRR structure was etched on the ground plane below the microstrip line (see Figure 6), which has a width of 3 mm. The width of the microstrip line was designed to match the characteristic impedance of 50 \(\Omega\). Figure 8 shows the simulated S-parameters of the SCSRR. The simulation results prove that the band reject characteristics were accomplished at 5.8 GHz, which is challenging with conventional microstrip resonators. Figure 5: Metal (black) and substrate (white) regions of (**a**) SRR, (**b**) CSRR, and (**c**) SCSRR geometries. Figure 6: Configuration of SCSRR model and dimension of the SCSRR are provided in Table 3.
## 3 Theoretical Framework
### Usage Scenarios and Perceived Information Volume  
The scene generally refers to the spatial environment, while the situation generally refers to the behavioral scene or psychological atmosphere, and the generalized scene describes the situation [29]. Product usage scenarios refer to the spatial environment and behavioral scene. Many videos for digital and home appliance products display products in a specific scenario to deliver more product information to consumers. The perceived information volume is the positive information that the consumer obtains through watching short videos. In marketing activities, retailers often provide positive information to consumers to lift sales. Normally, consumers prefer to search for internal and external information before making purchase decisions. Consumers are stimulated by external information and then try to search internal information from their memory. The correlation with external and internal information will ultimately affect consumers' willingness to purchase [30].  
The situational information shown in videos combines video animation and audio methods to convey more information that cannot be conveyed by static display. Placing products in a specific usage scenario will stimulate consumers, lead to the development of product associations, and send more product information to consumers, who are exposed to a greater volume of information. Thus, the following hypothesis is made:  
**Hypothesis 1 (H1)**. _The display of product usage scenario information will positively affect the information perceived by consumers._
## 3 Experimental Results
### Compressive Strength Tests  
In general, using a high volume of fine aggregate is not practical for the normal strength concrete (but not limited to the high-performance concrete). As such, only concrete specimens with a large volume of coarse aggregate were tested (G-MS-1, D-MS-1, G-NS-1, D-NS-1) for compressive strength. The average compressive strength of three cylinder specimens is presented in Figure 6. Specim D-Ms-1 had the highest compressive strength of 24.1 MPa, while specimen G-Ns-1 had the lowest value of 22.6 MPa. The compressive strength of the specimens with manufactured sand was higher than those with natural sand. The data indicates that the different types of fine aggregate affect the results of compressive strength of concrete. One of the reasons is the CTE of fine aggregate. Since the CTE range of siliceous natural sand is around 12 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C which is higher than the CTE range of granite-gneiss manufactured sand (8 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C ), during hardening process siliceous natural sand might cause a network of micro-cracks around it. The other reason is the shape and texture of aggregate. The angular shape and rough texture produce better bonding between the aggregate particles and cement paste [35]. The specimens with dolomite produced slightly higher compressive strength than those with granite. In addition, the modulus of elasticity was calculated according to the equation of ACI code (4700 \(\sqrt{f^{\prime}}\)) [35] in Figure 6. The results show the same trend as the results of compressive strength. Concrete specimens which have higher compressive strength displayed higher modulus of the elasticity.  
Figure 5: Effect of high volume of coarse aggregate and low volume of fine aggregate on concrete CTE.
# High Throughput Data Relay in UAV Wireless Networks
## 4 Proposed Solution
### Victim Bandwidth Scheduling Optimization  
For any given UAV trajectory \(\mathcal{C}\), problem (5) is simplified as  
\[\max_{\mathcal{A}}R, \tag{6}\]  
subject to  
\[R_{n}\geq R,\ \forall n \tag{6a}\] \[\sum_{n=1}^{N}a_{n}[k]\leq N+2,\ \forall k\] (6b) \[a_{n}[k]\in\{1,2\},\ \forall n,k \tag{6c}\]  
Sub-problem (6) is hard to solve as the optimization variable \(\mathcal{A}\) involves integers. To solve this sub-problem, we first relax the integer variable restriction in (6c), allowing for continuous variables, which results in the following sub-problem  
\[\max_{\mathcal{A}}R, \tag{7}\]  
subject to  
\[R_{n}\geq R,\ \forall n \tag{7a}\] \[\sum_{n=1}^{N}a_{n}[k]\leq N+2,\ \forall k\] (7b) \[1\leq a_{n}[k]\leq 2,\ \forall n,k. \tag{7c}\]  
Such a relaxation in general suggests that the objective value of sub-problem (7) serves as an upper bound for that of sub-problem (6). (7) is a standard linear programming problem, which can be solved by the CVX toolbox [26] in MATLAB. Later in the description of the overall algorithm, we explain how to construct a solution for problem (5) based on solving sub-problem (7).
## 1 Introduction
Two case studies were explored to demonstrate the prediction of epileptic seizure occurrence and the filling of a dysfunctional urinary bladder. However, [PERSON], [PERSON]'s [22] review article did not provide a detailed discussion on the application of ES and DSS with a specific focus on ECG interpretation and diagnosis. This paper provides a comprehensive review of the state-of-the-art methodologies for the development of ES and DSS systems targeted at ECG interpretation and diagnosis. We propose taxonomies of features and methods that have been utilized to interpret ECG signals. Our review covers a wide range of reputable databases, including IEEE, PubMed, ScienceDirect, Springer, Wiley and ACM. In addition, this paper presents the challenges and open research issues that are worthy of exploration towards the development of an efficient ES and DSS system for ECG interpretation and diagnosis. A total of 96 articles have been considered after a thorough screening of the available literature on the target subject based on the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [23]. These articles covered a period of thirteen years from 2010 to 2022. We focused on the recent developments and advances, as highlighted in the reviewed articles. The remaining part of this paper is organized as follows: Section 2 presents the review methodology employed in this study. It discusses the inclusion and exclusion criteria used to select the relevant articles for the study. Section 3 presents an extensive discussion on ECG. Section 4 highlights the composition of ES and DSS systems designed to aid ECG acquisition and interpretation. Section 5 focuses on mobile applications for ECG data acquisition and interpretation. Section 6 presents a taxonomy of methods for ES development, specifically for ECG. Section 7 discusses the taxonomy of the various features that have been used for ECG interpretation. Section 8 highlights a number of contemporary research issues and presents future research directions in DSS development for ECG interpretation and diagnosis.
# Enhanced Phase Retrieval Method Based on Random Phase Modulation
## 3 Results
Thus, the imaging quality of the parallel method suffered from the degradation of two aspects: noise and convergence state. When the noise was small, the convergence state played a vital role. The results of the three methods with noise variance of 30% and a mean of 2% are illustrated in Figure 6b-g. The amplitude result of the serial method displayed the most serious effect of noise. Furthermore, the contrast of the recovered phase of the serial method was affected severely, as we could hardly find the real position of the planet in the sub-image of Figure 6e. The simulation results demonstrate that the parallel framework had a slow convergence rate and strong noise immunity, while the serial framework had a rapid convergence rate and poor noise immunity. On the other hand, the cascaded approach inherited the merits of both methods, thereby realizing rapid convergence while having a good capability of noise immunity.
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 8 Conclusions  
Building an anti-adversarial model is one of the most innovative research topics in the anomaly-based network intrusion detection field, including the software-defined network-based NIDS model [39]. As a successful anti-adversarial prototype, AA-HMM quantifies the model difference (MD) and difference trend (DT) between adjacent windows as indicators of accuracy and pattern fluctuation, which transforms a regular HMM into an online algorithm with strong adaptability. The threshold mechanism (TH and THC) is the core anti-adversarial technique adopted by AA-HMM, which further enhances the adaptability and stability of the model. Particularly, the online architecture of DW, which predicts samples section-by-section, used in AA-HMM may wrap other quantified algorithms to largely improve the performance of the base models. In addition, AA-HMM could be used as a novel visualization tool to indicate if the NIDS is being attacked and to even distinguish the attack type based on the DW trajectory.  
Conceptualization, C.S.; methodology, C.S.; software, C.S.; validation, C.S., A.P. and K.Y.; writing--original draft preparation, C.S.; writing--review and editing, A.P., K.Y. and C.S.; supervision, A.P. and K.Y.; funding acquisition, A.P.  
This research was funded by the Florida Center for Cybersecurity, 2016-1017 FC2 Collaborative Seed Award Program.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AA-HMM & Anti-Adversarial Hidden Markov Model \\ B & benign (samples) \\ BM & Balance Model \\ BW & Baum-Welch \\ CM & Compact Matrix \\ \end{tabular}  
Figure 15: Trajectories of MD and DT.