esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Variance Calculation of AOA Measurement  
The function of this module was to determine the AOA measurement variance \(\left(\sigma_{\theta j}^{t}\right)^{2}\) of the UAV based on the \(\omega_{i}^{t}\) selected by the \(i\)-th UAV in the \(t\)-th time slot when \(i\), \(j\), and \(t\) were all determined. The relationship between these two parameters is already shown in Equation (22).
# Effects of Veneering Ceramic and Methods on Failure Load of Veneered Zirconia
## 1 Introduction  
Advances in dentistry have increased the use of computer-aided design/computer-aided manufacturing (CAD/CAM) systems in fabricating ceramic restorations [1]. These machinable ceramic restorations are made from highly uniform crystalline materials as compared to conventional fabricated restorations [2]. As a result, the Weibull modulus of oxide ceramics and thus the reliability of the restorations was significantly increased [3]. New aesthetic materials have been made available over time, showing increased aesthetic and mechanical properties [4, 5, 6, 7, 8].  
Current processing technologies unfortunately cannot make zirconia frameworks as translucent as natural teeth, so they are veneered with weaker porcelain to achieve acceptable esthetics. This veneer material is usually hand layered onto the zirconia core and fused onto the ceramic core by sintering [9].  
As veneer material is weaker compared to the core, it fails at low loads when it is placed under tension [10]. Cracks may originate from the interface between the core and the veneer, from the free surface of the veneer and from the inner surface of the core [11]. The placement of the veneer material may critically affect the fatigue properties of the layered restoration. The typical failure pattern of a veneering material in the daily clinical practice is known as ceramic chipping [12; 13; 14]. This fracture pattern is associated with a thin layer of glass ceramic that remains on the zirconia framework [12; 13; 15; 16]. This indicates a reliable bond of veneering ceramics to the framework, but also reveals a weakness of the veneering porcelain [17]. Heat pressing of the veneer porcelain onto the zirconia core was introduced as an alternative veneering technique to overcome cohesive failure [18]. However, in vitro studies revealed no differences in failure modes and reliability of standardized trilayer configuration [19] but also in load bearing capacity of crown systems with press veneering ceramics compared to hand-layered veneering [9; 17].  
Recently, CAD-/CAM-milled veneering material was introduced, the zirconia core and veneering materials were milled separately. Both corresponding parts of the restorations can be joined together by two techniques. The first one is sintering technique, which is to sinter them by means of a glass ceramic powder [17]. Another technique is to bond veneering and core parts together with resin cements. In vitro studies demonstrated that crowns made with sintering technique of a CAD-/CAM-generated glass-ceramics for veneering materials to zirconia coping had higher failure load compared to crowns made with other veneering techniques [10; 20]. The objective of this study is to evaluate the effect of veneering materials, veneering methods, interface materials and aging on the failure load of bilayered veneer zirconia.
## 3 Method
### Two-Stage Linear Model  
Crops typically experience rapid growth in the early stages, reaching a peak before gradually aging. Thus, we divided the crop growth period into an ascending phase and a descending phase (Figure 2). According to FAO56 guidelines [80], the rapid growth period of maize typically spans from 35 to 50 days, counting from the date of emergence. Therefore, within this date range, we identified the day with the smallest change in LAI as the cut-off point between the ascending phase and descending phase, denoted as \(\mathrm{D_{Peak}}\). We discovered that \(\mathrm{V_{m25}}\) exhibits a stronger correlation with leaf age than with LAI, particularly during the descending phase (Figure 2).  
Building upon this discovery, we proposed a Two-stage linear model that utilizes leaf age to simulate \(\mathrm{V_{m25}}\) (Figure 3). In this model, \(\mathrm{V_{m25}}\) is linearly fitted separately for the ascending and descending phases (Equations (16) and (17)). Our Two-stage linear model is as follow:  
\[\mathrm{V_{m25}}=\left\{\begin{matrix}\mathrm{k_{1}}\times\mathrm{D}+\mathrm{ V_{m25,ini}}&,&\mathrm{D_{ini}}<\mathrm{D}<\mathrm{D_{Peak}}\\ \mathrm{k_{2}}\times\left(\mathrm{D}-\mathrm{D_{Peak}}\right)&,&\mathrm{D_{ peak}}\leq\mathrm{D}<\mathrm{D_{end}}\end{matrix}\right. \tag{15}\]  
where \(\mathrm{D_{ini}}\) represents the leaf age when the crop initiates rapid growth, recorded as 0; \(\mathrm{D_{Peak}}\) represents the leaf age when the \(\mathrm{V_{m25}}\) reaches maximum; \(\mathrm{D_{end}}\) denotes the ending leaf age of the crop growth period, typically when the GPP value observed by the flux sites approaches 0; and D represents the current leaf age. \(\mathrm{k_{1}}\) and \(\mathrm{k_{2}}\) denote the slopes of the ascending and descending stages of crop development, respectively.  
\[\mathrm{k_{1}}=\frac{\mathrm{PeakV_{m25}-V_{m,ini}}}{\mathrm{D_{Peak}}} \tag{16}\]  
\[\mathrm{k_{2}}=\frac{\mathrm{PeakV_{m25}}}{\mathrm{D_{end}}-\mathrm{D_{Peak}}} \tag{17}\]  
Leaf age is commonly used to describe the growth status and developmental stage of plants. Its specific definition and calculation methods can vary depending on the research objectives and plant species. In our study, leaf age refers to the physical age of the plant, meaning the actual time from emergence to the current leaf development stage, measured in days. The emergence dates for US-Ne1, US- Ne2, and US-Ne3 can be obtained from the literature. For the Daman and Fenzidi, the emergence dates can be inferred from the changing trends in LAI values.  
Figure 2: R\({}^{2}\) between \(\mathrm{V_{m25}}\) and LAI, DOY during (**a**) the ascending stage and (**b**) the descending stage. QR indicate quartiles. The top and bottom boundaries of boxes indicate the values of the 25% percentile (QR1) and 75% percentile (QR3) values, respectively. The lines at both ends represent the maximum range (QR3 + 1.5 IQR) and the minimum range (QR1–1.5 IQR). The lines in the middle of the box represent the median, and the black diamonds represent the mean.
## 2 Methods
### Network Model
#### 2.3.1 Bidirectional-LSTM-Based Encoder  
Due to the long sequences to model and the large number of time steps during training, we used a bidirectional encoder to better understand the time dependencies between the two ends of the sequences. There, our encoder network consists of three layers: a linear layer and two Bidirectional Long Short-Term Memory (BiLSTM) layers, which are arranged incrementally, as shown in Figure 2.  
As the LSTM architecture shows in Figure 3, three gates control the data flow: an input gate and a forget gate, where the information is stored or vanished from the memory cell \(c_{t}\), which are represented by \(i_{t}\) and \(f_{t}\), at the \(t\)th time step, respectively; and an output gate denoted by \(o_{t}\), which controls the output state (also called the \"hidden state\") (\(h_{t}\)). The LSTM propagation is formulated as follows:  
Figure 2: Structure of the proposed Seq2 Seq model, where the attention layer is ignored for clarity.  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{5}\] \[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f})\] (6) \[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\] (7) \[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot tanh(W_{c}x_{t}+U_{c}h_{t-1} +b_{c})\] (8) \[h_{t}=o_{t}\odot tanh(c_{t}) \tag{9}\]  
where \(x_{t}\) is the input vector at the \(t\)th time step, \(c_{t}\) is the current long-term memory cell, \(\sigma()\) is the sigmoid function, \(\odot\) represents the element-wise multiplication, and \(W_{*}\) (i.e., \(W_{i},W_{f},W_{o},W_{c}\)) are the model parameters that map from input dimension to hidden dimension, while \(U_{*}\) maps from the previous hidden dimension to the current hidden dimension.  
After processing, each BiLSTM time step \(t\) will generate two hidden states:  
\[h_{t}=[\overrightarrow{h_{t}},\overleftarrow{h_{t}}] \tag{10}\]  
where \(\overrightarrow{h_{t}}\) is the forward LSTM network vector, which produces the high-dimensional features of the input signal, and \(\overleftarrow{h_{t}}\) is the backward or inverse LSTM network output vector, given, respectively, by Formulas (11) and (12).  
\[\overrightarrow{h_{t}}=\overrightarrow{LSTM}(h_{t-1},x_{t},c_{t-1}) \tag{11}\] \[\overleftarrow{h_{t}}=\overleftarrow{LSTM}(h_{t+1},x_{t},c_{t+1}) \tag{12}\]
# Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China
## Materials and Methods
### HS-SPME/GC-MS Analysis  
Kiwifruit samples (20 kiwifruits per sample) were peeled and homogenized to obtain fruit pulp. Five milliliters of each kiwifruit pulp was transferred to 20 mL headspace vials. Then, 1.5 g of sodium chloride and 120 uL of the internal standard mixture were added to kiwifruit pulp. The determination and selection of the internal standard are shown in Table S2 and Table 1. The concentration of all internal standards was 100 uL/L.  
The volatile components in kiwifruit samples were analyzed by HS-SPME (headspace solid-phase microextraction) combined with GC-MS. The methods were based on previous studies [15; 16] with minor modifications. Samples were equilibrated at 45 \({}^{\circ}\)C for 30 min, and the volatile compounds were extracted from the headspace to the SPME fiber (50/30 um, DVB/CAR/PDMS, Supelco, Bellefonte, USA) for 30 min. Then, the SPME fiber was injected into the injection port of the GC-MS system and desorbed at 250 \({}^{\circ}\)C for 2 min. A GC-MS-QP2010 (Shimadzu, Kyoto, Japan) was employed for qualitative and quantitative analysis of the volatile compounds. A fused silica capillary column (DB-1 MS; 30 m \(\times\) 250 um \(\times\) 0.25 um) and helium gas (1.93 mL/min) were used for detection. The initial temperature was 40 \({}^{\circ}\)C, maintained for 3 min, the temperature rose to 120 \({}^{\circ}\)C at a speed of 4 \({}^{\circ}\)C/min, then rose at a rate of 6 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and was maintained for 9 min. The mass spectrometry (MS) conditions were as follows: electron impact ionization (EI), ion source temperature was 230 \({}^{\circ}\)C, ionization energy was 70 eV, and the scanning range of MS was \(m/z\) 35-500 in full-scan mode.  
Figure 1: Origins, cultivars, flesh colors, and species of kiwifruit.  
According to the NIST 14 mass spectrometry database, each volatile component was identified by matching degree, retention time, and retention index (RI). Volatile components with matching degrees greater than 85% were selected as effective aroma components. Then, the internal quantification standards in Table 1 were used to quantify the volatile components in the kiwifruit samples. Meanwhile, internal verification standards were used to verify the quantitative results. The results showed that the relative standard deviation (% RSD) between the internal quantification standard and the internal verification standard was less than 15%. All tests were performed in triplicate.
## 2 Integrated Modeling
### Control Subsystem  
In this paper, the control subsystem model adopts the real attitude control strategy of the satellite (shown in Figure 5). When the control subsystem is running, imaging mission instructions are uploaded to the satellite from the ground remote system. The center control computer generates control instructions according to the proportional-derivative (PD) control strategy. According to the control instructions, flywheels generate control torque, which acts on the structural subsystem to change the attitude. The star sensor measures attitude information and transmits it to the central control computer for calculating the next control instructions, thus forming a closed-loop attitude control subsystem.  
Figure 4: Typical mode shapes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency (Hz)** & **Mode Damping (\%)** \\ \hline
127.7 & 0.412 \\
197.3 & 0.301 \\
255.9 & 0.382 \\
320.3 & 0.493 \\
394.5 & 0.594 \\
493.2 & 0.317 \\ other & 0.500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modified mode damping.  
A traditional attitude control subsystem regards a satellite as a rigid body and obtains the attitude information after control torque is applied through a Euler dynamic equation. However, in this paper, attitude information is obtained through a state-space equation (which regards a satellite as a flexible body). Since the star sensor is the component that obtains attitude information in orbit, the response information of the finite element node of the star sensor is selected as the input of the control subsystem in Section 2.3. The needs of attitude adjustment and stability control would be met by the feedback control process.
# Analysis of Cement Sheath-Rock Damage Mechanism--A Case Study on Water Injection Wells
## 4 Numerical Simulation Analysis of Water Channeling outside Casing in Water Injection Wells
### Study on the Evolution of Damage for Bonded Interface Rock with Different Widths (w)  
We conducted numerical simulations on models of bonded rock surfaces with varying width dimensions: 0.02 m, 0.05 m, 0.1 m, 0.15 m, and 0.2 m. The objective was to explore the influence of width variations on the progression of damage. The numerical simulation results were analyzed to evaluate the impact of width on rock damage under fluid-solid coupling and examine the patterns of damage evolution on bonded rock surfaces with varying widths. As shown in Figures 11 and 12, it is evident that increasing the water injection pressure leads to the primary propagation of damage along the interface between the rock and the protective cement sheath. During the bonded rock surface damage, significant damage is observed on one side of the cement sheath at the interface position. Hydraulic pressure mainly propagates along the damaged zone and around the cracks.  
Based on Figures 13 and 14, it can be observed that as the width (w) of the cemented interface rock increases from 0.02 m to 0.2 m, the shear stress exhibits a relatively small change. The peak shear stress remains relatively stable with respect to the width of the cemented interface. The numerical solutions obtained from the model closely match the analytical solutions, with a maximum error of 5.44%.  
These findings suggest that the damage evolution of the cemented interface rock is minimally affected by its width. The shear stress behavior shows little sensitivity to changes in width dimensions. Therefore, it can be concluded that the width of the cemented interface has a limited influence on the overall damage evolution process.  
Figure 11: Evolution law of damage in bonded interface rock with different widths.  
Figure 12: Distribution of fluid pressure in bonded interface rock with different widths.
# Numerical Study on Tip Vortex Cavitation Inception on a Foil
## 2 Numerical Approaches
### Numerical Solution Methods  
The governing equations for two-phase incompressible flows were solved in the commercial program Star-CCM+ by using the finite volume method and a volume of fluid approach ([PERSON] and [PERSON], 2017). The segregated flow approach was used to solve the equations in which the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm was adopted to resolve the pressure-velocity coupling. The spatial differencing of the convective terms uses a second order accurate upwind scheme. A second-order central differencing was used for the viscous terms. For the solutions of non-cavitating tip vortex flows, steady computations were carried out. The cavitating flows that require unsteady simulations with the multiphase volume of fluid method were solved with a second order time implicit scheme and 10 inner iterations to reach convergence for a given physical time, where the converged steady-state solutions for non-cavitating flows were utilized as the initial conditions. The computational time step was set to be \(1\times 10^{-3}\) s. This time scale is small enough for the current problems that cover rather stationary tip vortex flows. Unstructured grid based on a hexahedral mesh topology was used in the present simulations. A prism layer was used to generate orthogonal prismatic cells next to the wall surfaces, in which the height of first cells from the wall was determined so that the dimensionless wall distance \(y^{+}\) nearly equals 1. As it is well known, one of the important parts of numerical analysis of tip vortex flows is to find the vortex path and to establish a proper grid resolution around the vortex. To obtain an appropriate grid resolution at the tip vortex core region, a set of refining control volumes was adopted along the tip vortex, where a proper grid size was determined to capture the small-scale tip vortex.
## 4 Methodology  
Figure 1 describes the development process of this mathematical model to optimize the vehicular flow of Calle 50 in Panama City:  
The project begins by identifying the problem and the motivation for the study. This step is essential, as it provides a clear understanding of the problem and helps create the variables needed to develop the mathematical model.  
The mathematical model is based on queuing networks, a mathematical tool used to analyze and optimize the system's performance involving queues or waiting lines. In the context of the study, the queuing network is used to model the flow of vehicles. The system's performance is evaluated as a function of the waiting time experienced by the vehicles.  
To check and verify the effectiveness of the mathematical model, simulations, and tests are performed. These simulations create a computer model of the vehicle flow, which allows for observing the system's behavior under different conditions. By varying different parameters, such as traffic volume, road capacity, and traffic control mechanisms, the impact of each parameter on the waiting time experienced by vehicles can be evaluated.  
Figure 1: Block diagram of the model.  
The research aims to optimize vehicle waiting time and improve traffic flow. This can bring several benefits, such as reduced travel time, increased safety, and improved fuel efficiency. The research can also help traffic planners and policymakers make more informed decisions and implement more effective traffic management strategies.
* Summary: The problem and motivation of the study are identified; this will allow us to create the necessary variables to develop the mathematical model based on queuing networks and thus be able to perform the information analysis of the vehicular flow. Finally, we perform tests and simulations to verify the optimization of the waiting time experienced by vehicles.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
###### Abstract  
5G New Radio (NR) operating in the unlicensed spectrum is accelerating the Fourth Industrial Revolution by supporting Internet of Things (IoT) networks or Industrial IoT deployments. Specifically, LTE-Advanced (LTE-A) is looking to achieve spectrum integration through coexistence with multi-radio access technology (RAT) systems in the same unlicensed bands with both licensed-assisted and stand-alone access. The listen-before-talk (LBT) mechanism is mainly considered to enable an LTE operator to protect other incumbent unlicensed systems. In this article, we investigate the behaviors of multiple LTE operators along with the deployment of WiFi networks in the unlicensed spectrum from both short- and long-term points of view. In countries without mandatory LBT requirements, we show that an LTE operator is susceptible to collusion with another LTE operator, thus exploiting scarce spectrum resources by deceiving other wireless networks into thinking that channels are always busy; hence, mandatory usage of LTE with LBT is highly recommended at national level to achieve harmonious coexistence in the unlicensed spectrum. We discuss several possible coexistence scenarios to resolve the operator's dilemmaas well as to improve unlicensed spectrum efficiency among multi-RAT systems, which is viable in the near future.  
5G New Radio (NR); long-term evolution (LTE); LTE-license assisted access (LTE-LAA); LTE in unlicensed spectrum (LTE-U); listen-before-talk (LBT); clear channel assessment (CCA); operator's dilemma; game theory +
Footnote †: journal: Sensors
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS
### Psychiatry and Cognitive Impairment
In these two studies, the ability of TD-NIRS to measure absolute haemoglobin concentration and \(\text{StO}_{2}\) were crucial, since the measurements were taken several weeks apart. Figure 5: Example of the data produced by TD-NIRS systems. **(a)** Coronal slices of tomographic images of the blood volume and the \(\text{StO}_{2}\) of a baby who had a left ventricular haemorrhage. The haemorrhage can be seen as an increase in blood volume and a decrease in oxygen saturation. Extracted from [69]. **(b)**\(\text{StO}_{2}\) and haemoglobin concentration ([tHb]) in a patient with septic shock during the hour prior to a pulseless electrical activity arrest and cardiopulmonary resuscitation (CPR). The decrease in \(\text{StO}_{2}\) can be seen 15 min prior to the arrest. Extracted from [95]. **(c)** Illustration of a measurement from [86], on a patient with an internal carotid artery (ICA) occlusion on the affected side and a crossflow over the anterior communicating artery (ACoA). The anatomical situation and the influence on the bolus passage are shown schematically on the right diagram, with the affected hemisphere shown in black colour and the unaffected one shown in light grey. The left side of the figure compares the conventional CW-NIRS based approach, which is represented by the time-integrated signal \(\text{N}_{\text{tot}}\) to the moment TD-NIRS approach based on the analysis of variance. The colours of the bolus curves correspond the diagram (black: affected hemisphere, light grey: unaffected hemisphere). In the CW-NIRS approach, the bolus curves differed only slightly. In contrast, in the TD-NIRS approach, the variance signals showed a significant difference between the hemispheres. **(d)** Example of brain activity of a patient with locked-in syndrome during mental imagery (MI), from [107]. The “rest” period corresponds to data acquired without MI activation and is presented as a reference for the contrast observed during the question periods. If the answer was “Yes” the patient was asked to perform a MI task (i.e., play tennis). If the answer was “No”, the patient was asked to rest. The grey boxes indicate the response period. One can see that the patient responded “yes” to two questions. At the time of this study, the patient had regained limited eye movement, so those responses could be confirmed.