esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Patient Selection and Treatment Setup  
Fifty-eight Sicilian women aged between 30 and 70 years with early-stage BC were included in this study. The mean patient age was 55 years, with a standard deviation of 10 years. These women were treated at Humanitas-Istituto Clinico Catanese (H-ICC) (Misterbianco, Italy) between 2015 and 2021. All data were fully anonymized and cannot be traced back to individuals. RT was delivered using a tangential field technique, encompassing the chest wall and a small portion of the ipsilateral lung within the irradiated volume. An anterior field with an angled gantry was used to avoid the spinal cord, esophagus, and contralateral breast. Treatment plans were calculated using the Analytical Anisotropic Algorithm (AAA, version 15.6.06) within the Eclipse v. 16.1 Treatment Planning System (TPS) by Varian Medical Systems (Palo Alto, CA, version 15.6.8, [[https://www.varian.com/](https://www.varian.com/)]([https://www.varian.com/](https://www.varian.com/))), based on CT scans with 2.5 mm slice thickness. Therapy was performed using a Varian TrueBeam 2.7 linear accelerator ([[https://www.varian.com/](https://www.varian.com/)]([https://www.varian.com/](https://www.varian.com/))), which produces 6 MV photon beams with two opposite tangential static field techniques. For all treatment plans, the maximum dose (always within the PTV) was lower than 110% and at least 95% of the prescribed dose covered 95% of the PTV volume. OAR dose constraints, converted to equivalent dose, were respected: V5 < 40%; V20 < 20%; D\({}_{\text{mean}}\) < 7 Gy [29, 30, 31, 32, 33]. Treatment plans were rescaled to an HF schedule using the same irradiation geometry--including field sizes, beam angles, and beam arrangements--to assess differences in ipsilateral lung carcinogenesis risk, and all OAR dose constraints were respected.
## 5 Opportunities and Alternatives
### Electrical Sector Applications
For example, the Central American region is interconnected between six countries: Guatem, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama. They transmit more than 300 MW in power lines up to 230 kV [100]. Although this region could be a good place to use drones, their use is still in its early stages. Similarly, in Brazil, due to a combination of large-scale centralized expansion projects and small-scale distributed generation, the power system now comprises solar 1.31, wind 12.9, nuclear 2, biomass 14.7, fossil fuels 26.9, and hydro 102.2 gigawatts as of July 2018. With this growth and its critical infrastructure [101], this country has one of the largest aerial power line structures in the world with a length of up to 95,000 km, and companies such as Companhia Hidrelectrica do Sao Francisco (CHESF) are researching with UAS to monitor its lines [102] with long-range UAS that have inspection platforms and various prototypes [103]. Let us continue our analysis with Peru, where electrification increased by 97% and generation by 186% in 2015, after the company Statkraft took full control of operations in six regions of Peru in 2014. They started a program named the Performance Improvement Program (PIP) to reduce costs by 15% throughout 2016, 2017, and 2018. Part of this program was the drone inspection project, which was responsible for monitoring the company infrastructure [104]. Similar to Peru, Chile with the company Enel has a program to monitor distribution power lines [105]. In Panama, Empresa de Transmision Electrica S.A. (ETESA) acquired drones to assist in inspection and design tasks [106]. Other Latin American countries are beginning to incorporate UAVs into their operations, as seen at the International Air and Space Fair (FIDAE) in Santiago de Chile in 2018, where last-generation drones were exhibited, showing many possible applications of UASs in the continent [107].
## 2 Theoretical Analysis of Tree Flash Fault Formation in Hybrid Transmission Lines  
Schematic of Tree Flash Fault Occurrence Process (see Figure 1). \(L\) represents the height of the tree; h represents the height of the transmission line tower; \(\text{f}_{0}\), \(\text{f}_{1}\), and \(\text{f}_{2}\) represent the sag distance of the conductor; and d represents the safety distance.  
Specifically, \(\text{f}_{0}\) refers to the initial sag distance of the conductors in normal operating conditions. When the conductors are affected by external environmental factors and self-heating, leading to an increase in conductor temperature and length, the sag distance also increases accordingly. At this point, the sag distance may increase to \(\text{f}_{1}\) and eventually reach \(\text{f}_{2}\) as the severity of the conditions intensities. At the same time, when the height of the trees directly below or on both sides of the overhead transmission lines reaches due to rapid growth and lack of timely trimming, and the clearance distance between the trees and  
Figure 1: Illustration of tree flash fault occurrence.  
the conductors is less than the safety distance L, the transmission conductors will discharge electricity to the trees, leading to the occurrence of tree flash faults. The critical condition for the occurrence of tree flash faults is denoted as \(\rm L\geq h-f-d\).  
The critical condition \(\rm L\geq h-f-d\) ensures a safe distance between trees and power lines. When the critical condition is not met, i.e., when \(\rm L<h-f-d\), the distance between trees and power lines is too close, posing the risk of discharge from the power lines to the trees, which may lead to tree flash faults. Adhering to the critical condition helps prevent tree flash faults and ensures the safe operation of overhead transmission lines. The meaning of key conditions: L: The net clearance distance between trees and power lines, representing the shortest distance between trees and power lines under conditions of tree growth and changes in wire sag. h: The height of trees, serving as a crucial parameter for measuring the vertical distance between trees and power lines. f: The sag of power lines, indicating the vertical downward distance of power lines, influenced by external environmental factors and the weight of the power lines themselves. d: The safety distance, referring to the minimum net clearance distance between power lines and trees, ensuring the prevention of tree flash faults under various conditions. The establishment of safety distance d is crucial to ensure a safe distance between power lines and the surrounding environment, especially trees, thereby preventing potential electrical discharge and tree flash faults.
## 3 Rate-Adaptive Scheme of Joint Control of Photoelectric Domain
### Power-Adaptive Control of Optical Domains  
When the distance between the transmitter and receiver is small, the incident optical power is strong, which leads to saturation distortion of the PMT. Therefore, the incident optical power needs to be controlled. Compared with the attenuator plate, the LCLV has a  
Figure 4: **(a)** Constellation diagram of UN\(-\)16 QAM receiver when SNR = 10 dB; **(b)** constellation diagram of UN\(-\)16 QAM receiver when SNR = 15 dB; **(c)** constellation diagram of PS\(-\)16 QAM receiver when SNR = 10 dB; **(d)** constellation diagram of PS\(-\)16 QAM receiver when SNR = 15 dB; **(e)** comparison of BER performance before and after shaping; **(f)** BER performance comparison of different shaping signals.  
higher regulation precision of light, and the light's transmittance can be controlled only by changing the voltage applied at both ends of the LCLV [6].  
According to Section 3.1, after the signal arrives at the receiver, the received signal's power needs to be estimated. When the incident optical power approaches or exceeds the saturation threshold, the PMT will exhibit a complex nonlinear performance, resulting in the distortion of the received signal and a limited dynamic range [27]. In order to expand the dynamic range of the PMT, an LCLV is used to control the incident optical power when a short distance between the transmitter and receiver leads to PMT saturation. To ensure that the SNR is at its maximum and the signal is not distorted, the received power should be maintained at the maximum value in its linear region. Therefore, the transmittance of the LCLV needs to be adaptively adjusted according to the incident light's power. The expression is as follows:  
\[T=\frac{P_{optimal}}{P_{in}}, \tag{12}\]  
where \(T\) represents the light transmittance of the LCLV, \(P_{optimal}\) represents the maximum power of the PMT in its linear region and \(P_{in}\) represents the incident light's power. Based on the relationship between the transmittance of the LCLV and the driving voltages at both ends summarized in [6], the relationship between the LCLV driving voltage and the incident light's power can be further derived.  
In summary, the above methods are combined to design a rate-adaptive algorithm for joint control of the photoelectric domain. First, the power and SNR at the current distance are estimated using a test signal. The receiver determines whether the PMT is saturated based on the estimated power and then decides whether to adjust the transmittance of the LCLV. Secondly, the modulation method to be used is determined based on the estimated SNR. When \(H\
eq\log_{2}(M)\), the shaped signal is transmitted. Based on Equation (9) and the KL divergence, the probability mass function is calculated, and then the corresponding symbol sequence is generated. Otherwise, uniform QAM signals are transmitted. The specific flow chart is shown in Figure 5.  
Figure 5: Rate-adaptive flow chart for joint control of photoelectric domain.
## 4 Experiments and Discussions  
### Sliding Form Controller for Ship Dynamic Positioning System  
A dynamic positioning system is a closed-loop control system; using various types of sensors to measure the ship's motion status and position changes, as well as the size and direction of the external wind, waves, currents, and other disturbing forces, the control system controls the ship's thrust device to generate the appropriate thrust and torque in order to offset the disturbing forces so that the ship can maintain the target position and the port direction. The control system is the core of the DPS; the traditional control method of the ship power positioning system does not meet the present ship positioning accuracy. This paper adopts the power positioning linear and terminal sliding mode controller designed based on the sliding film control theory, as well as the combination of Kalman filtering and a terminal sliding mode controller, to carry out the simulation by bringing in the motion model of the dual-purpose intelligent research and practical training ship of Dalian Maritime University.
## 2 Materials and Methods
### Isolation of Volatiles  
A dynamic headspace solid-phase microextraction method was used for the isolation and concentration of the volatile compounds. The sample was first equilibrated in an autosampler (Model GC Sampler 80, Agilent Technologies, Santa Clara, CA, USA) at 60 \({}^{\circ}\)C for 30 min and agitated at 250 rpm with on and off steps of 5 s and 2 s, respectively. This step helped to promote absorption from the headspace. An SPME fiber coated with 50/30 \(\upmu\)m divinylbenzene/carboxsen/polydimethylsiloxane (DVB/CAR/PDMS) was then exposed to the headspace of the sample for 30 min at 60 \({}^{\circ}\)C for it to adsorb the analytes. After the adsorption process was completed, the fiber was then inserted into the injection port of the gas chromatography-mass spectrometry (GC-MS). The fiber, which had the volatile compounds adsorbed onto its surface, was desorbed into the GC injection port for 5 min
## 2 Establishment of a 3D Rock-Breaking Model for Disc Cutters
### Discrete Element Method  
The discrete element method (DEM) models rock as an assembly of particles, each considered a separate body subject to forces and moments exerted by neighboring particles. DEM simulations solve the equations of motion for the entire system based on [PERSON]'s second law and constitutive models. Explicit iterative calculations are performed, as illustrated in Figure 3, where [PERSON]'s second law and force-displacement relationships are repeatedly applied.  
Figure 3: The calculation process of an explicit loop.  
Figure 2: Structure of disc cutter (1: cutter shaft; 2: disc-cutter seal; 3: cutter hub; 4: cutter ring).  
DEM employs several assumptions for numerical calculations:  
1. Each particle is treated as a rigid body.
2. Contact occurs over a small area: as a line segment in 2D and a disk in 3D.
3. Contact is flexible, allowing small overlaps at contact points.
4. Bond strength exists at the contact interface.  
Particles in the DEM can move in two ways: translational motion and rotational motion about their center. The translational motion is governed by the following vector equation:  
\[F_{i}=m(a-g_{i}) \tag{1}\]  
where \(F_{i}\) is the vector sum of the external forces acting on a particle; \(m\) is the particle mass; and \(g\) is the acceleration of gravity.  
Rotational motion is described by the following equation:  
\[M_{i}=H_{i} \tag{2}\]  
where \(M_{i}\) is the sum of all external torques, and \(H_{i}\) represents the angular momentum.  
The following Euler equations of motion can be obtained from Equation (2):  
\[\begin{split} M_{1}&=I_{1}\omega_{1}+(I_{3}-I_{2}) \omega_{3}\omega_{2}\\ M_{2}&=I_{2}\omega_{2}+(I_{1}-I_{3})\omega_{1} \omega_{3}\\ M_{3}&=I_{3}\omega_{3}+(I_{2}-I_{1})\omega_{2} \omega_{1}\end{split} \tag{3}\]  
where \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) represent the angular accelerations of the spherical particles along three different directions; \(M_{1}\), \(M_{2}\), and \(M_{3}\) are the components of the resultant torque acting on the spherical particles along each coordinate axis. \(I_{1}\), \(I_{2}\), and \(I_{3}\) are the components of the moment of inertia of the particles along the spatial coordinate axes, and their values are identical for spherical particles.  
Cracks are generated when contact forces exceed the bond strength. Tension cracks occur when the normal force exceeds the tensile strength or becomes negative, while shear cracks form when the tangential force exceeds the shear strength.
## 2 Materials and Methods
### Proposed Machine Learning Pipeline  
The complete ML pipeline of the proposed methodology is summarized in this section. The pipeline starts with the signal preprocessing, which includes the loading of the data, the fusion of variables and axes from all sensors, the removal of NaNs (i.e., Not a Number) and outliers, the removal of noise, and the balancing of the classes. The next step is the feature engineering and data transformation that is needed so that the data will be in proper condition for the training. This includes the temporal window definition, the overlap of temporal windows, the class assignment and labeling, the splitting of the training and test datasets, and the normalization of the training dataset. The following process is the ML model training with the use of an LSTM algorithm architecture, which learns each sub-activity's features the training dataset. Finally, validation of the model is done with a 10-fold cross-validation, where the training and test dataset shuffle after each training is over and the performance metrics are calculated. With the completion of the 10-fold cross-validation, the performance metrics are averaged and presented. A schematic of the pipeline is presented in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Formula** \\ \hline Accuracy & \begin{tabular}{c} Ratio of correctly predicted observation to the total \\ observations (preferred in balanced datasets) \\ \end{tabular} & \begin{tabular}{c} (TP+TN)/(TP+FP+FN+TN) \\ \end{tabular} \\ Precision & \begin{tabular}{c} Ratio of correctly predicted positive observations to the \\ total predicted positive observations to all \\ observations in actual class \\ \end{tabular} & \begin{tabular}{c} TP/(TP+FP) \\ \end{tabular} \\ Recall & \begin{tabular}{c} Ratio of correctly predicted positive observations to all \\ observations in actual class \\ \end{tabular} & \begin{tabular}{c} TP/(TP+FN) \\ \end{tabular} \\ F1 score & \
\begin{tabular}{c} The weighted average of Precision and Recall (preferred \\ in unbalanced datasets) \\ \end{tabular} &
\begin{tabular}{c} (\(2\times\text{Recall}\times\text{Precision}\))/(Recall + Precision) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics used in the present study.
## 3 Technology Developments for Production of Carotenoids
### Development of Microalgae for Caroenoid Production  
Algae are a diverse group of aquatic, photosynthetic organisms, generally categorized as macroalgae (_i.e._, seaweed) and unicellular microalgae. Microalgae have recently garnered interest for production of valuable chemicals including carotenoids [41, 131], because they are generally regarded as safe (GRAS) for human consumption and possess the renewable-energy capturing ability of photosynthesis. Moreover, these organisms can be used for genetic manipulation and high-throughput analysis [132]. Some microalgae are also native carotenoid producers (_i.e._, _D. salina_ for \(\beta\)-carotene and _H. pluvialis_ for astaxanthin). The carotenoid production from microalgae is closely related to culture conditions such as illumination, pH, temperature, nitrogen availability and source, salinity, the oxidant substances, and growth rate [12, 133, 134]. _D. salina_ is a model species of green microalgae which is widely cultivated outdoors for \(\beta\)-carotene production [131]. A systematic evaluation has been done to decipher the relationship between abiotic stresses (Nitrate concentration, salinity and light quality) and lutein synthesis in _D. salina_[135]. The abiotic stresses can also be applied to adaptive evolution of microalgae [136], in a similar manner to strain evolution in yeast for \(\beta\)-carotene production [107]. The freshwater microalga _Chlamydomonas reinhardtii_ is the first and the best studied transformed Chlorophyte, and the nuclear genetic manipulation is easy and well established. It has been engineered with \(\beta\)-carotene ketolase from _H. pluvialis_ to synthesize ketolutein (H\({}_{\mathrm{bl}}\)H\({}_{\mathrm{a1}}\), Figure 3) and adonixanthin (H\({}_{\mathrm{a1}}\)H\({}_{\mathrm{a2}}\), Figure 3) [137]. It is possible to produce diverse valuable carotenoids from marine microalgae with the development of more available genetic tools and technologies.
## 4 Results and Discussion
### Generation Events
#### 4.1.3 How Are Interactions Distributed throughout the Week?  
Another relevant aspect of user habits regarding time refers to the distribution of their interactions throughout the week. Table 13 presents the global distribution of interactions. Table 14 shows the distribution of interactions depending on the category they belong to.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Day Slot** & **Hours** & **Samples** \\ \hline Midnight & 00:00 to 02:59 & 1.75\% \\ Dawn & 03:00 to 05:59 & 0.55\% \\ Morning & 06:00 to 08:59 & 5.04\% \\ Mid-morning & 09:00 to 11:59 & 21.16\% \\ Noon & 12:00 to 14:59 & 17.37\% \\ Afternoon & 15:00 to 17:59 & 18.08\% \\ Evening & 18:00 to 20:59 & 25.89\% \\ Night & 21:00 to 23:59 & 10.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Test 2: Percentage of usage in different time periods.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Day Slot** &
\begin{tabular}{c} **Social** \\ **Interaction** \\ \end{tabular} & **Nutrition** & **Leisure** & **Care** & **Emotions** & **Health** \\ \hline Midnight & 0.51\% & 1.69\% & 1.45\% & 1.85\% & 3.11\% & 1.62\% \\ Dawn & 0.20\% & 0.77\% & 0.43\% & 0.49\% & 1.29\% & 0.55\% \\ Morning & 1.61\% & 5.64\% & 6.72\% & 4.01\% & 3.32\% & 2.99\% \\ Mid-morning & 18.30\% & 18.53\% & 21.69\% & 22.52\% & 17.58\% & 18.99\% \\ Noon & 10.30\% & 17.63\% & 15.09\% & 19.71\% & 17.76\% & 19.37\% \\ Afternoon & 11.01\% & 21.37\% & 15.20\% & 18.94\% & 26.25\% & 20.08\% \\ Evening & 52.12\% & 23.50\% & 30.82\% & 21.26\% & 19.33\% & 24.22\% \\ Night & 5.95\% & 10.87\% & 8.58\% & 11.23\% & 11.36\% & 12.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Test 2: Usage of categories in different time periods.  
The general distribution shows that usage through the week is rather evenly distributed, peaking in the middle of the week and declining on Sundays. Category-wise, we observe that nutrition has the most consistent distribution over the week, while categories like social interaction, emotions, and leisure are mostly reserved for working days, when users may attend to school, their workplace, or therapy sessions.
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows. Section 2 provides details of the system model. In Section 3, outage probability and ergodic capacity expressions are provided. Section 4 presents numerical results and discussion. Finally, Section 5 provides some future research directions along with concluding remarks.
## 1 Introduction
[PERSON] et al. [28] observed that the properties of the PAW depend on the treatment time when the GAD air plasma was used. In summary, it can be stated that with the increase in the discharge power, the densities of the atomic oxygen, molecular nitrogen ion, and nitrogen ion in air plasma increases. As a result, this will stipulate the formation of higher concentrations of nitrates and nitrates in plasma-activated waters [6; 12; 23; 25; 27]. Additionally, the production of the higher concentrations of NO and NO\({}_{2}\) in the gasses will lead to the formation of the plasma-activated water/medium with higher NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\) concentrations [5; 12; 23; 28]. Many researchers have analyzed, investigated, and compared various types of plasma devices to produce plasma-activated water or uses for nitrogen fixation [3; 4; 5; 9; 10; 11; 12; 13; 14]. However, it is very difficult to compare different discharges or even compare discharges of the same type, which is problematic as different discharge characteristics (pulse duration, power, discharge mode, etc.) are used, and the configuration or geometry of the devices are varied in a broad range [9; 10; 11; 12; 13; 14; 15; 16; 17]. Therefore, the densities of reactive particles produced in the air plasma can vary significantly, which will lead to different concentrations of nitrogen oxides and physicochemical properties of the PAW. In order to properly apply plasma in various fields, it is important not only to determine the composition of air plasma but also to investigate the influence of discharge parameters (voltage, frequency, pulse duration, etc.) on the densities of reactive species and the concentration of the produced nitrogen oxides. Although there are many studies on the application fields of gliding arc discharge, detailed studies on the relationship between the air plasma composition and the produced NO and NO\({}_{2}\) concentration values are still lacking. The main aims of this work were to investigate the variation in the main species produced in air plasma at various air flow rates, output voltages, and frequencies, and to determine the influence of the air flow and output voltage on the concentrations of the NO and NO\({}_{2}\) gasses produced in GAD plasma. Additionally, a relationship was investigated between the intensity of the emission bands and the produced concentrations of NO and NO\({}_{2}\) gasses.