esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Survey Findings
#### 4.1.1 Socioeconomic  
In this study, \"the family background\" results indicate that more than 40% of fathers and more than 50% of mothers have no degree. This relatively low level of education leads to the instability of fathers' occupations, which in turn negatively affects the students' educational experiences.  
The most prevalent living arrangement of students is a university residence, with 43.3% of respondents choosing this option. This probably indicates that a significant number of students live in university housing, possibly due to its proximity to campus, to reduce transportation costs, particularly for students living far from their family's house.  
A combined total of 38.3% of respondents live at a parent's house or another family member's house. This could reflect financial reasons, proximity to family, or the desire to stay close to home. The relatively low percentage of students in rented or shared accommodation could indicate that either these students are not as financially independent, or they may be in situations where university accommodation is more accessible or cost-effective.  
The results show also that the principal source of monthly income for over 74.3% of students is their parents, followed by scholarships, and only 8.3% work during study. Most students do not work while studying, but for those who do, the majority work between 15 and 24 h weekly, (see Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value** & **N** & **\%** \\ \hline \multirow{2}{*}{Gender} & Male & 44 & 36.7\% \\  & Female & 76 & 63.3\% \\ \hline \multirow{4}{*}{Age} & From 18 to 20 & 6 & 5.0\% \\  & From 21 to 23 & 63 & 52.5\% \\ \cline{1-1}  & From 24 to 26 & 20 & 16.7\% \\ \cline{1-1}  & Above 26 & 31 & 25.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographic variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Value** & **N** & **\%** \\ \hline \multirow{6}{*}{Father’s highest diploma} & None & 59 & 49.2\% \\ \cline{2-3}  & Bac (high school diploma) & 26 & 21.7\% \\ \cline{2-3}  & Bac + 2 (associate degree) & 13 & 10.8\% \\ \cline{2-3}  & Bac + 3 or 4 (bachelor’s degree) & 13 & 10.8\% \\ \cline{2-3}  & Bac + 5 or plus (master’s degree or doctorate) & 9 & 7.5\% \\ \hline \multirow{6}{*}{Mother’s highest diploma} & None & 71 & 59.2\% \\ \cline{2-3}  & Bac (high school diploma) & 15 & 12.5\% \\ \cline{1-1} \cline{2-3}  & Bac + 2 (associate degree) & 10 & 8.3\% \\ \cline{1-1} \cline{2-3}  & Bac + 3 or 4 (bachelor’s degree) & 22 & 18.3\% \\ \cline{1-1} \cline{2-3}  & Bac + 5 or plus (master’s degree or doctorate) & 2 & 1.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Socioeconomic information.  
This result can be explained by the fact that dropping out of higher education could be indirectly influenced by several variables. Among these, the family environment could influence the academic potential of the individual and attitude toward the aim of acquiring a degree.
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
## 5 Simulation Results
The root mean square error (RMSE) values of responses of different references are listed in Table 2, which is defined to evaluate the tracking performance, as follows:  
\[RMSE=\sqrt{\left(\sum_{i=1}^{n}e_{i}^{2}\right)/\left(n-1\right)}, \tag{37}\]  
where \(e_{i}\) denotes the tracking error in the \(i\)-th discrete time step, and the sampling time is 0.01 s.  
As can be seen from Table 2, the bold numbers stand for the best tracking performance among the three approaches, and thereby the tracking performance of CS1 is evidently more accurate than that of CS2 and CS3, which indicates that compensator \(K_{3}(t)\) enables the output trajectory of CS1 to be closer to the sine reference.
## 3 SPO-Based Collision Detection and Obstacle Avoidance Using APF
### Obstacle Avoidance Using APF
The path parametrization between two starting and goal points is defined as [31]:  
\[q(t_{c})=q_{i}+s\left(q_{f}-q_{i}\right), \tag{22}\]  
where \(q_{i}\) and \(q_{f}\) are the initial and final known positions, respectively. Moreover, s = \(\sigma\)/L, and \(\sigma\) [0, L] is the arc length (which yields the current length of the path). L is defined as \(\mid q_{f}-q_{i}\mid\mid\). The following equation shows the timing law in Cartesian space trajectory:  
\[\sigma(t)= \begin{cases}\begin{array}{c}0.5*a_{max}*t^{2}&t\in[0,\ T_{s}]\\ v_{max}*t-\frac{v_{max}}{2*a_{max}}&t\in[T_{s},\ T-T_{s}]\\ 0.5*\left(-a_{max}*\left(t-T\right)^{2}\right)+v_{max}*T-\frac{v_{max}}{a_{max} }&t\in[T-T_{s},\ T]\end{array},\end{cases}  \tag{23}\]  
where \(a_{max}\) and \(v_{max}\) are known, and \(T_{s}\ =\ \frac{v_{max}}{a_{max}}\). The following Figure 8 graphically represents the equation. To prevent the robot manipulator from being obstructed, the repulsive force from the potential field is used. When the robot reaches the threshold area, then the trajectory tracking equation for system is as follows  
\[q(t_{R})=q(t_{c})-\alpha*\
abla U_{rep} \tag{24}\]  
where \(\alpha\) is the gradient descent rate, which determines the minima of the function. After a series of simulations is performed using the foregoing equation, the following relationship between the threshold radius and gradient descent is derived. The following equation indicates that that the movement of the robot can be fast if its distance is considerable. \[\alpha=e^{R-1}(R>0) \tag{25}\]  
Figure 9 shows the complete methodology used in this study. First, the desired end-effector position is determined from the 3D haptic device, then the desired trajectory of the robot end-effector is generated. The robot follows the trajectory using the inverse kinematics of the system. It operates with the unstructured environment in which we do not have any information about the obstacle. When the robot collides with an obstacle the  
Figure 8: Timing law in Cartesian space trajectory. contact force has an impact on each joint of the robot manipulator in the form of external torque, which is estimated by the SPO. The end-effector reaction force is also estimated using estimated external torque. After obstacle detection, an APF is applied to avoid the obstacle.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Search Strategy
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Concept** & **Definition According to [PERSON] et al. [43]** & **SLR Application** \\ \hline Population & The research focuses on the assessment of adult human breathing patterns using inertial sensors. & Scientific research work on adult human breathing patterns assessment. Focused on the use of inertial sensors, their strengths, and weaknesses. \\ \hline Intervention & Existing techniques and methods to address the problem. & The use of inertial sensors methods and techniques utilized to address adult human BP assessment to identify gaps in current methods, protocols, and device setups. \\ \hline Comparison & Techniques to contrast the intervention used to measure the BP. & The difference between different methods applied to quantify/value/map adult human BP. \\ \hline Outcomes & Measure to assess the knowledge and gaps mentioned in the selected publications regarding the assessment of adult human BP using inertial sensors. & Existing knowledge on the assessment of adult human BP using inertial sensors, such as methods and techniques approach used, data types, and purpose. Mentioned gaps: limitations related to applied methods and techniques and data quality. \\ \hline Context & The particular settings or areas of the population. & Research trends in adult human breathing patterns, existing knowledge in studies of adult human breathing patterns, and the challenges and gaps in adult human breathing patterns measurement. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PICOC Framework (Population, Intervention, Comparison, Outcome, and Context). To refine the results, exclusion keywords were implemented. These keywords aimed to filter out references related to \"children\", \"infant\", \"child\", \"baby\", \"newborn\", \"newborns\", \"animal\", \"rat\", \"rats\", \"rabbit\", \"rabbits\", \"monkey\", \"monkeys\", \"dog\", \"dogs\", \"cat\", \"cats\", \"fracture\", \"trauma\", \"injury\", \"injuries\", \"lesion\", or \"lesions\". Additionally, a language filter was applied, limiting the search results to English, French, Portuguese, or Spanish. Combining all of these parameters yielded a total of 30 publications in Scopus, 51 in WoS, and 40 in PubMed. Additional details of the search strategy are provided in Table S1 (Supplementary Material), allowing the search to be reproduced and updated.
# Detecting Axial Ratio of Microwave Field with High Resolution Using NV Centers in Diamond
## 1 Introduction  
Building and measuring circularly-polarized microwave (MW) fields are of great importance in many fields like magnetic resonance [1; 2] and mobile satellite communications [3; 4; 5]. Conventionally, precision measurement of MW field strength is realized by converting the field strength to easily-measurable electrical signals like using the calorimetric method [6] and the technique of peak demodulation [7]. The antenna axial ratio measurement is conducted with the help of a linearly-polarized antenna [4]. However, the calorimetric method is susceptible to environment temperature, and the technique of peak demodulation does not perform well at weak field strength. The assistant antenna perturbs the properties of the antenna under test (AUT) and usually has a large volume. Quantum-based MW field detection technologies, such as super quantum interference device [8] and cold atoms [9], which offer high sensitivity, require extreme measurement conditions. Vapor cell devices [10; 11] that are capable of high sensitivity MW field sensing at near room temperature have a spatial resolution confined to sub-100 \(\upmu\)m.  
Recently, quantum sensing based on a point defect, the nitrogen vacancy (NV) center, in diamond has attracted wide attention for its long coherence time at room temperature [12]. It has already been deeply researched and applied to magnetic field, electric field, and temperature sensing [13; 14; 15]in many subjects. As its electron spin state can be coherently manipulated by the MW field, it can also be applied to MW field sensing. In previous works, the NV-based MW magnetic field sensing schemes mainly focused on detecting MW magnetic field strength [16; 17].  
In this paper, we propose a scheme to detect the axial ratio of an MW device with high resolution using NV centers in diamond. The axial ratio detection is based on MW magnetic field strength detection of the two counter-rotating circularly-polarized components of an MW field. An NV center is a negatively charged point defect in diamond consisting of a substitutional nitrogen atom and an adjacent carbon vacancy. Its ground electron-spin triplet state can be selectively and coherently manipulated by a specified circularly-polarized MW field. The MW magnetic field strength of specific polarization can be derived from Rabi frequency or magnetic resonance spectra contrast [16; 18]. Firstly, the MW axial ratio detection is demonstrated with a single NV center in diamond with a conventional confocal microscope. Then, wide-field detecting of the MW magnetic field, which greatly promotes sensing speed, is conducted with a wide-field microscopy system. The MW axial ratio detection technique facilitates the test and design of MW devices aimed at quantum spin manipulation [19; 20; 21] and satellite communication.
# Dealing with Low Quality Images in Railway Obstacle Detection System
## 4 Improved Laplacian Edge-Based Blur Detection Method
### Laplace Operator  
The Laplace operator (Laplacian) belongs to the second-order derivative methods, unlike the first-order derivatives such as Sobel, Kirsh and Prewitt operators [48]. Moreover, Laplacian operator \(\
abla^{2}\) presents a derivative of the second-order Sobel operator and it can be defined as  
\[\
abla\cdot\
abla f=\
abla^{2}f=\frac{\partial^{2}(f)}{\partial x^{2}}+\frac{ \partial^{2}(f)}{\partial y^{2}}, \tag{5}\]  
where \(\
abla f\) represents the gradient of a two-dimensional function \(f(x,y)\). Practically speaking, the Laplace operator of the discrete function could be obtained by making a difference on the second derivative of the Laplace operator in the \(x\) and \(y\) (horizontal and vertical) directions.  
Let us define the first-order difference for the \(x\) direction as  
\[\frac{\partial f(x)}{\partial x}=f(x)-f(x-1). \tag{6}\]  
Then, the second-order difference can be defined as  
\[\frac{\partial^{2}f(x)}{\partial x^{2}}=\frac{\partial f(x+1)}{\partial x}- \frac{\partial f(x)}{\partial x}. \tag{7}\]By implementing (6) into (7), we can obtain the desired form of the second-order difference as  
\[\frac{\partial^{2}f(x)}{\partial x^{2}}=f(x-1)-2f(x)+f(x+1). \tag{8}\]  
If we implement the same procedure for the \(y\) direction, the second-order difference for this direction can be defined as  
\[\frac{\partial^{2}f(y)}{\partial y^{2}}=f(y-1)-2f(y)+f(y+1). \tag{9}\]  
Finally, the Laplace convolution matrix (kernel) can be determined by superposition of coefficients [1, \(-2\), 1] from (8) and (9), as  
\[L_{M}=\left[\begin{array}{ccc}0&1&0\\ 1&-4&1\\ 0&1&0\end{array}\right]. \tag{10}\]  
This form belongs to the group of positive Laplacian operators, and it is made up of a standard mask with the corner elements set to zero and the center element set to negative. Practically, this kernel computes the difference between a point and the average of its four direct neighbors. In this way, it identifies regions of an image with fast intensity variations, and it can be very useful in applications where the focus is on the edge identification problem. The premise is that if a picture has high variance, it will have a lot of responses, both edge-like and non-edge-like, like a typical, in-focus image. However, if the variance is low, there is a small spread of responses, indicating that the image has fewer edges. As we know, the more an image is blurred, the fewer the number of edges.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
###### Abstract  
In order to accommodate the ever-increasing traffic demands, numerous approaches have been developed to improve spectrum utilization. Among others, the coexistence of LTE (Long-Term Evolution) and Wi-Fi, addressed by the 3 GPP (3 rd Generation Partnership Project) with hyper access points (HAPs) as bridges, is well recognized as a promising candidate solution. Aimed at improving the spectrum utilization of the unlicensed bands by following LTE-Unlicensed (LTE-U), this article contributes to the determination of the optimal time ratio, \(\delta\), for the time-division multiplexing of LTE and Wi-Fi over unlicensed bands. Symmetric allocation with a duty cycle of 50% cannot be an optimal option. Asymmetric allocation according to the quality of service (QoS) requirements and traffic demands should be considered. The problem is formulated as an optimization problem optimizing the total throughput. The lower and upper bounds of \(\delta\) are obtained according to the QoS requirements of Wi-Fi and the admission control requirements of LTE. The detailed procedure for finding an adequate \(\delta\) is developed and presented. A series of simulations are conducted to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results show that the proposed approach improves the total throughput without compromising the fairness of Wi-Fi, as intended. Ten percent of improvement in throughput compared with LTE-U can be achieved.  
Long-Term Evolution (LTE); Wi-Fi; LTE-Unlicensed (LTE-U); Hyper-AP (HAP) Article
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## Materials and Methods  
### Heat Loss Coefficient Calculation  
The HLC is the most used Key Performance Indicator (KPI) to describe the building envelope energy efficiency. This coefficient reflects transmission heat losses through the envelope per degree difference between indoor and outdoor temperatures from walls, roofs and floors (UA(kW/K)) and ventilation and/or infiltration heat losses per degree difference between indoor and outdoor temperatures (Cv(kW/K))) [43]. Following the method developed by [PERSON] et al. [8], the HLC can be described as Equation (1):  
\[HLC=(UA+C_{v})\hskip 14.226378 pt(\text{kW/K}) \tag{1}\]  
where \(UA\) stands for the building envelope transmission heat transfer coefficient (kW/K) and \(Cv\) stands for the infiltration and/or ventilation heat loss coefficient (kW/K). Moreover, the energy balance used to calculate the HLC, assuming stationary conditions, was developed by [PERSON] et al. [8; 44], and it is presented in Equation (2):  
\[\sum_{k=1}^{N}Q_{k}+\sum_{k=1}^{N}K_{k}=HLC\sum_{k=1}^{N}(T_{int,k}-T_{out,k}) -\sum_{k=1}^{N}(S_{a}V_{sol})_{k} \tag{2}\]  
where \(Q_{k}\) represents the heating gains (kW), \(K_{k}\) represents the internal gains (kW) caused by occupants and electricity consumption (\(K_{k}=K_{electricity,k}+K_{occupancy,k}\)), (\(T_{in,k}-T_{out,k}\)) summarizes the gap between temperatures inside and outside the building in degrees Kelvin and \(S_{a}V_{sol}\) stands for the solar gains (kW).  
Several terms of this heat exchange are difficult to measure for in-use buildings. For example, the solar gains are complicated to know, and therefore, they introduce an important source of uncertainty into the equation. Thus, in this study, only cold and cloudy periods are considered where solar radiation was very low; direct solar radiation can be considered null; and all the radiation can be perceived as purely diffuse [45]. This is why in this study, direct solar radiation is not taken into account, and only diffuse solar radiation is considered. Another parameter that is difficult to estimate is the occupancy. Therefore, with the aim of reducing the sources of uncertainty, the study is developed during weekends when there is no occupancy. Then, the result of Equation (2) will give an idea of how well built the building is in terms of the enclosure of its envelope.  
In this way, the HLC formula that is used in this analysis (average method), taking into account that the periods analysed in this paper were weekends with no occupation and in which there was only purely diffuse radiation, is shown in Equation (3):  
\[HLC=\frac{\sum_{k=1}^{N}Q_{k}}{\sum_{k=1}^{N}(T_{in,k}-T_{out,k})} \tag{3}\]
## 1 Introduction
However, the conditions are changed with the introducing of DC-linked MGs in distribution networks: (i) the MG operators use the private DC-links as the first priority for energy sharing; (ii) they exchange the energy with the distribution network only when the aggregated energy is surplus or deficient. Meanwhile, the reconfiguration of distribution network is a traditional operation problem, which is a process of shifting the state of sectionalizing switches and tie switches to achieve the goal such as minimizing the network loss [19; 20], improving the reliability [21], maximizing the renewable energy penetration [22; 23], and relieving overload in the network [24; 25]. In the distribution network with a number of DC-linked MGs, the traditional reconfiguration framework may not be used, due to the uncontrolled impact from the private energy trading between MGs. As the MGs are operated by independent entities, they can actively change the scheduling and trading scheme, and finally lead to uncontrolled impact on the distribution network. In this regard, the reconfiguration decisions would be more difficult, which are required to be coordinated with the scheduling of DC-linked MGs. As several entities are involved in the operation, the optimization problem should be implemented by multiply parties with different time scales. From the viewpoint of DNO, reconfiguration of distribution network could be a possible way to reduce the network power losses. However, with the connection of DC-linked MGs, the reconfiguration results could be seriously influenced by the dynamical changes of private energy trading and uncertainties of RES. From the viewpoint of MG owners, as the network tariff can be ignored, it is more economic for the MGs to work as a coalition via DC-links. In this regard, it is important to achieve the optimal operation and fair cost allocation for the coalition, which can be seen as a local market in the community level [26; 27]. To this end, in order to solve the aforementioned problems, this paper proposes a cooperative operation method for distribution networks with DC-linked MGs, which has integrated the reconfiguration problem of distribution network and the optimal scheduling problem of multi-MG in a unified framework. As the two parts are operated by different parties with hybrid time scales, this is a new problem which has not been addressed in the literature of smart grid, to the best of our knowledge. The detailed contributions are listed as follows. 1. A multi-party optimization framework is designed, which includes the reconfiguration of the distribution network and scheduling of the DC-linked MGs. 2. For the DC-linked MGs, a stochastic optimal scheduling model for the coalition is formulated, considering the uncertainties of PV, WT, and load power. Moreover, a billing mechanism is designed to distribute the cost the coalitional operations. 3. For the distribution network, an event triggered dynamic reconfiguration model is proposed. The decisions of switch operations are finally co-decided by the expected exchanging power of the MGs and the real-time errors caused by uncertainty factors.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Predictive Maintenance and Diagnostics
#### 4.4.1 Analyzing Maintenance Logs  
Maritime vessels produce extensive maintenance logs documenting inspections, repairs, and operational anomalies [133]. LLMs can analyze these unstructured text logs to identify patterns indicative of potential equipment failures or degradation. By detecting correlations between reported issues and subsequent failures, the model can predict when components are likely to require attention [134].  
This predictive capability enables scheduling of preventive maintenance tasks before critical failures occur, reducing downtime and preventing accidents caused by equipment malfunction. The use of LLMs in analyzing maintenance data enhances the reliability of vessels and contributes to cost savings through optimized maintenance schedules [135].
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction
#### 2.3.4 Support Vector Regression Models  
This study also introduced support vector regression (SVR and NuSVR) for yield prediction. Support vector regression models (SVR and NuSVR) are based on the theory of support vector machines (SVM) and are well-suited for handling small sample sizes and nonlinear problems. NuSVR controls the proportion of support vectors by introducing a relaxation variable, using a linear kernel function (kernel = \"linear\") and a regularization parameter C = 0.5. In contrast, SVR uses a radial basis function (RBF) kernel (kernel = \"rbf\") to handle nonlinear relationships. Although SVR is advantageous for small samples and nonlinear problems, its higher computational complexity limits its application in large-scale data and high-dimensional features [47].