esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Blededing and Concretion Rate  
The bleeding rate directly influenced the stability and filling effect of the slurry, while the concretion rate directly affected the strength and durability of the grouting material [33]. Figure 5 provides a visual representation of the effect of different factors on the slurry's bleeding and concretion rates. As the dosage of zeolite powder increased, the bleeding rate of the slurry gradually increased, while the concretion rate steadily decreased. When the ratio of zeolite powder to GGBFS was 50:50, the bleeding rate of the slurry peaked, with a reduction of 100%. The slurry bleeding rate increased uniformly with elevated CL dosage. Due to the retardation and water-reducing properties of CL, the slurry hydration reaction was retarded, leading to an increase in free water molecules and the bleeding rate. The slurry bleeding rate gradually decreased as the NaOH dosage increased. The increased alkali concentration in the solution promoted a variety of chemical reactions in the slurry [34]. Concurrently, a significant amount of heat was released, thereby facilitating the progression of the reaction. Consequently, the bleeding rate was minimal, while the concretion rate was maximal at a NaOH dosing of 9%.  
Figure 5: Effects of different factors on bleeding and concretion rate: (**a**) zeolite powder–GGBFS; (**b**) CL dosage; (**c**) NaOH dosage; (**d**) water–binder ratio.  
As shown in Figure 5d, an increase in the water-binder ratio led to a gradual increase in the slurry's bleeding rate. As the water-binder ratio increased from 0.7, a significant increase in the bleeding rate was observed, reaching its maximum at a water-binder ratio of 0.8. Due to the increased number of free water molecules in the slurry that did not participate in the chemical reaction, the setting time increased, particles precipitated faster, and the bleeding rate increased. The final results showed that only the bleeding rate of Group 18 exceeded the upper limit value specified in the Chinese technical specification DL/T 5148-2021 [35].
## 2 Materials and Methods
### Paradigm Design  
This study employed a ring-shaped contraction-expansion SSMVEP paradigm. The SSMVEP paradigm consisted of a high-contrast background and a low-contrast annular stimulus. The primary parameters of the low-contrast ring included its inner and outer diameters. The high-contrast background determined the maximum diameter of the entire visual paradigm. The area ratio (C) between the ring and the background was defined as follows:  
\[C=\frac{S_{1}}{S-S_{1}} \tag{1}\]  
In this equation, \(S_{1}\) represents the total area of the annular stimulus, and \(S\) denotes the total area of the background.  
At the onset of the paradigm, the outer diameters of the concentric rings were arranged in an arithmetic sequence. Given the maximum diameter of the visual field (\(r_{max}\)), the outer diameter (\(r_{1}^{i}\)) and inner diameter (\(r_{2}^{i}\)) of each ring were related to the area as follows:  
\[r_{1}^{i}=(2i-1)*r_{max}/2n \tag{2}\]  
\[S^{i}=\pi{{(r_{1}^{j})}^{2}}-\pi{{(r_{1}^{j-1})}^{2}} \tag{3}\]  
\[S_{1}^{i}=\pi{{(r_{1}^{i})}^{2}}-\pi{{(r_{2}^{j})}^{2}} \tag{4}\]  
In this equation, \(i\) denotes the index of the \(i\)-th ring counted from the center outward, and \(n\) represents the total number of rings. \(S^{i}\) indicates the area enclosed between the outer diameter of the \(i\)-th ring and that of the (\(i-1\))-th ring, while \(S_{1}^{i}\) refers to the area of the \(i\)-th annular ring.  
To ensure that the overall luminance of the paradigm remains constant during motion, the area ratio \(C\) between the annular rings and the background must be maintained at a fixed value. Therefore, the inner diameter of each ring needs to be determined through calculation. Based on Equations (1)-(4), the inner diameter \(r_{2}^{i}\) of the \(i\)-th ring can be computed as follows:  
\[r_{2}^{i}=\sqrt{{(1-C)*{(r_{1}^{i})}^{2}}+{{(r_{1}^{i-1})}^{2}}} \tag{5}\]  
Let the variation range of the outer diameter of each annular ring be defined as \([(i+1)*r_{max}/n,i*r_{max}/n]_{\rm c}\). The outer diameter follows a sinusoidal motion trajectory. Accordingly, the time-dependent change in the outer diameter \(r_{1}^{i}\) of the \(i\)-th ring can be expressed as:  
\[r_{1}^{i}=i+1-cos(2\pi ft)*r_{max}/n \tag{6}\]  
In this equation, \(f\) represents the stimulation frequency, and \(t\) denotes the stimulation time.  
In this study, eight types of ring-shaped stimulation paradigms with distinct flickering frequencies (i.e., 3.00 Hz, 3.25 Hz, 3.50 Hz, 3.75 Hz, 4.00 Hz, 4.25 Hz, 4.50 Hz, and 4.75 Hz) were employed, as illustrated in Figure 1. In addition, to evaluate the proposed algorithm's ability to distinguish between idle and control states, a designated idle region was included to collect EEG data corresponding to the idle state. Participants were instructed to fixate within a dashed rectangular frame during idle trials to ensure that no control commands were generated. As a result, a total of nine target classes were collected in this study, including eight stimulation conditions and one idle condition.
## 2 Problem Formulation and System Modeling
### Motion Models for Various Motion Modes  
A schematic diagram of the FC motion mode models is presented in Figure 2. Different motion modes require different off-center arm steering angles. During MMS, the steering angles of the four off-center arms and the angular velocities of the four in-wheel motors must maintain fixed relationships. Each off-center arm has a specific target angle. During CM, the relationships are given by:  
\[\left\{\begin{array}{l}\left|\delta_{fl0}\right|=\left|\delta_{fr0}\right|= \left|\delta_{rl0}\right|=\left|\delta_{rr0}\right|=\frac{\pi}{2},\ \delta_{fl}=-\delta_{fr}=\delta_{rl}=-\delta_{rr}\\ \omega_{fl}=\omega_{fr}=\omega_{rl}=\omega_{rr}\end{array}\right., \tag{1}\]  
where \(\delta_{i}\) is the steering angle of each off-center arm (\(I=fl_{i}f_{r}\), \(r\), \(lr\), which represent the left front, right front, left rear, and right rear wheel of the flexible chassis, respectively), \(\omega_{i}\) is the angular velocity of each off-center steering mechanism, and \(\delta_{io}\) denote the target angle of each off-center arm.  
For in-place rotation, the target angles and angular velocities have the following relationships:  
\[\left\{\begin{array}{l}\left|\delta_{fl0}\right|=\left|\delta_{fr0}\right|= \left|\delta_{rl0}\right|=\left|\delta_{rr0}\right|=\arctan(\frac{L}{B}),\ \delta_{fl}=-\delta_{fr}=\delta_{rl}=-\delta_{rr}\\ \omega_{fl}=\omega_{fr}=\omega_{rl}=\omega_{rr}\end{array}\right., \tag{2}\]  
where \(L\) is the distance between the front and rear off-center shafts and \(B\) is the distance between the left and right off-center shafts.  
During the whole MMS process, the FC frame needs to keep its original posture. For diagonal motion, if CM is directly switched to DM, the moments at centroid from all wheels will be in the same direction, and the FC frame cannot keep its posture. To avoid this problem, DM is switched from IR in this study. This method can maintain symmetrical force on the FC. The target angle and angular velocity relationships are expressed by:  
\[\left\{\begin{array}{l}\left|3\delta_{fl0}\right|=\left|\delta_{fr0}\right| =\left|3\delta_{rl0}\right|=\left|\delta_{rr0}\right|=\frac{3\pi}{4},\ \delta_{fl}=\pi-\delta_{fr}=\delta_{rl}=\pi-\delta_{rr}\\ 3\omega_{fl}=\omega_{fr}=3\omega_{rl}=\omega_{rr}\end{array}\right.. \tag{3}\]  
Similarly, the relationships that govern steering motion are:  
\[\left\{\begin{array}{l}\left|\delta_{fl0}\right|=\left|\pi-\delta_{rl0} \right|=\pi-\arctan(\frac{2L}{2R+B}),\ \left|\delta_{fr0}\right|=\left|\pi-\delta_{rr0}\right|=\arctan(\frac{2L}{2R- B})\\ \delta_{fl}=\pi-\delta_{rl},\ \delta_{fr}=\pi-\delta_{rr}\\ \omega_{fl}:\omega_{fr}:\omega_{rl}:\omega_{rr}=\left|\sqrt{\left(R+0.5B \right)^{2}+L^{2}}+d\right|\colon\left|\sqrt{\left(R-0.5B\right)^{2}+L^{2}}-d \right|\\ \colon\left|\sqrt{\left(R-0.5B\right)^{2}+L^{2}}-d\right|\colon\left|\sqrt{ \left(R+0.5B\right)^{2}+L^{2}}+d\right|\end{array}\right., \tag{4}\]  
where \(d\) is the off-center distance and \(R\) is the turning radius of the FC during steering motion.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 3 Basic Predicted Model
### Quadratic Polynomial Model  
After preprocessing, the clean clock offset sequences can be modeled. An adaptive predicting model of the BDS ultra-rapid clock offsets will be constructed, which takes into account the physical characteristics of the satellite clock, the adaptive weight function, and the initial deviation.  
At present, BDS satellites are mainly equipped with Rb atomic clocks. A Rb clock has significant clock drift characteristics, therefore, the quadratic polynomial model was used as the basic prediction model and the fitting formula can be expressed as follows [34; 35]:  
\[y(t)=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}+\Delta_{i}, \tag{2}\]  
where \(a_{0}\), \(a_{1}\), and \(a_{2}\) are three parameters of the satellite clock model, and they also can be called clock offset (phase), clock velocity (frequency), and clock drift (frequency drift), respectively. \(t_{0}\) is the reference epoch of the satellite clock model, \(t_{i}\) denotes the observation epoch, and \(y(t)\) is the clock offset phase observation. \(\Delta_{i}\) is the residual of prediction model.  
In order to compare the residuals of different fitting models step by step, the fitting residuals of the BDS clock offset observations on 1 January 2017 are obtained by the quadratic polynomial, as shown in Figure 3.  
As shown in Figure 3, the significant periodic characteristics exist between different constellations' residuals after quadratic polynomial fitting. In order to ensure the correctness and reliability of the selection of the periodic terms, the FFT method is used to analyze the quadratic polynomial fitting residuals of GBM and ISU during a period of 100 days (see Section 4).
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Reconfigurable Intelligent Surfaces  
Reconfigurable intelligent surface (RIS) is an emerging technology that can manipulate impinging electromagnetic waves to benefit wireless users [162; 163]. One of the benefits of RIS is to scale up signal power and to reflect signals in the desired path by applying particular phase shifts [164]. A FANET equipped by RISs has the potential to be used for simultaneous wireless information and power transfer (SWIPT) [165]. Furthermore, in ultra-dense urban areas where many obstacles block the signal propagation in terrestrial networks, a FANET of RISs can enable the intelligent reflection from the sky to have a LoS transmission between terrestrial transceivers as shown in Figure 16. Hence, more research and development are required in the domain of RIS-assisted FANETs in the future.
## 3 The Proposed Average Small Signal Modelling and Control Circuits Design
### Conventional CCM PFC Topology
Usually, the bandwidth of the outer voltage loop must be very small to eliminate the harmonics of the output DC voltage reflected by the AC input voltage at 60 Hz [42]. What is more the bandwidth of the inner current loop must be high as compared with the outer voltage control loop to let the inductor current follows the reference current, also the current control loop bandwidth must be less than switching frequency (Fsw) to reject the noise at the switching frequency. In this work, the undamped natural frequency (\(\xi\)) was considered to be about 0.707, the closed loop bandwidth of (Wn) was assumed to be about 150 rad/s and 10,000 rad/s for the outer voltage and inner current loops, respectively. For the reliable operation of a controller to offer good performance with different loading condition, the controllers Kp and KI parameters were set to work with the minimum value of the load voltage (Vo) of 320 V with the rated input voltage. Figure 10 depicts the bode plot of the closed-loop TF of the two control loops, which shows that the current controller offers unity gain for frequencies less than 3 kHz. This current control system, working as a low pass filter, helps to remove the switching frequency noise. Moreover, the voltage controller offers unity gain for frequencies less than 23 Hz. This voltage controller works as a low pass filter to remove the 60 Hz voltage ripple. Figure 10: Bode plots for the control loops for the conventional CCM PFC converter: (**a**) voltage control loop; (**b**) current control loop.
## 5 Computational Complexity Analysis  
In general terms, the computational complexity for detection and decoding in the receiver depends on the number of transmitted bits (\(b\)) and the number of users per subcarrier (\(d_{f}\)); the complexity of both SM-SCMA and RGSM-SCMA [16] is given by \(\mathcal{O}\left(2^{bd_{f}}\right)\). If multiple antennas \(\left(N^{T}\right)\), are used, the complexity increases to \(\left(N^{T}\right)^{2}\mathcal{O}\left(2^{bd_{f}}\right)\). On the other hand, the computational complexity of neural networks [25; 26; 27] that use backpropagation is defined in general terms as \(\mathcal{O}\left(n^{5}\right)\) assuming that we have square matrices and that we have the same number of neurons for each hidden layer of the neural network, so the above expression could be expanded to \(\mathcal{O}\left(n^{3}\times W_{ji}\times epochs\right)\), where \(W_{ji}\) is the matrix of the activation function from layer \(i\) to the layer \(j\) and \(epochs\) is a hyperparameter of gradient descent that controls the number of complete passes through the training dataset. One way of making algorithms run faster is by using parallel execution, for example, running the matrix operations using Graphics Processing Unit (GPU).  
GPUs are specifically designed to run many matrix operations in parallel since 3D geometry and animation can be expressed as a series of linear transformations. This is also why we usually train neural networks on GPUs. Since neural network resides in the Base station, it would be easier to deploy GPU in the receiver circuit.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling
#### 2.4.4 Temporal Bounds for Packet Scheduling  
Addressing the reward modeling, we apply the following reasoning. A good pass in an hour is not the same as an equally good pass occurring after 24 h because: (1) data needs to be transmitted regularly, (2) the circular queue holding data has a finite capacity, and (3) the Swarm modem will drop packets from its transmission queue after a timeout. Thus, to model he preference for more prompt transmissions, a _discount factor_\(\lambda\) is applied to reduce the value of later passes.  
Under the data plan, each modem can transmit at most one packet per hour to remain within the budget. Since there are many satellite passes to consider, the rules are needed for the interval of packet scheduling. Such rules are shown in Figure 6, in which \(t_{min}\) and \(t_{max}\) are the minimum and maximum amount of time (in hours) for a satellite pass, respectively, **a** is a vector of satellite passes between \(t_{min}\) and \(t_{max}\) (\(a_{i}\) is the \(i\)-th element of **a**). Further, **s** is a vector of states (i.e., pass characteristics and RF background noise) of satellite passes of **a**. Let **t** be a vector of midpoint times of satellite passes of **a**.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Bucket Number** & **Max Elevation Angle (*)** & **Pass Duration (minutes)** & **RF Background Noise (dBm)** \\ \hline
1 & 15 to 30 & 10 to 20 & \(-93\) to \(-95\) \\
2 & 31 to 45 & 21 to 30 & \(-96\) to \(-98\) \\
3 & 46 to 60 & 31 to 40 & \(-99\) to \(-101\) \\
4 & 61 to 75 & 41 to 50 & \(-102\) to \(-104\) \\
5 & 76 to 90 & 51 and higher & \(-105\) and lower \\ \hline \hline \end{tabular}
\end{table}
Table 4: State space bucketing for each state variable.
## 2 Channel Model
### Absorption  
The absorption of the acoustic wave varies with frequency \(F\) and is dependent on salinity \(S\), temperature \(T\), depth \(D\) and the water \(p\)H level. There are many expressions for the absorption of the acoustic waves. A semi-empirical expression is formulated by [PERSON] and [PERSON] [45].  
Figure 5: Link budget plot for wireless communication.  
\(Total_{absorption}\)= Boric acid contribution + \(MgSO_{4}\)+ Pure water contribution  
\[\alpha=\frac{A_{1}P_{1}f_{1}f^{2}}{f^{2}+f_{1}^{2}}+\frac{A_{2}P_{2}f_{2}f^{2}}{ f^{2}+f_{2}^{2}}+A_{3}P_{3}f^{2} \tag{18}\]  
**Boric Acid Contribution:**  
The contribution of the boric acid is given as:  
\[A_{1}=\frac{8.86}{C}\times 10^{(0.78 pH-5)};\;\mathrm{dB\;km^{-1}\;kHz^{-2}}. \tag{19}\]  
\[P_{1}=1 \tag{20}\]  
\[f_{1}=2.8\left[\frac{S}{35}\right]^{0.5}\times 10^{(4-1245/\theta)};\;\mathrm{kHz} \tag{21}\]  
\[C=1412+3021T+1.19S+0.0167D, \tag{22}\]  
where \(C\) is the speed of sound in (m/s), \(T\) is the temperature \({}^{\circ}\)C, \(S\) is the salinity in \(ppt\) and \(D\) is the water depth, respectively. The value of the \(\theta=273+T\).  
**Magnesium Sulfate Contribution:**  
The contribution of the \(MgSO_{4}\) is expressed by:  
\[A_{2}=21.44\frac{S}{C}(1+0.025T);\;\mathrm{dB\;km^{-1}\;kHz^{-1}} \tag{23}\]  
\[P_{2}=1-1.37\times 10^{-4}D+6.2\times 10^{-9}D^{2} \tag{24}\]  
\[f_{2}=\frac{8.17\times 10^{(8-1990/\theta)}}{1+0.0018(S-35)};\;\mathrm{kHz} \tag{25}\]
**Pure Water Contribution:**  
The contribution of the pure water for \(T\leq\ ^{\circ}\)C, is presented as:  
\[A_{3}=4.937\times 10^{-4}-2.59\times 10^{-5}T+9.11\times 10^{-7}T^{2}-1.50 \times 10^{-8}T^{3};\;\mathrm{dB\;km^{-1}\;kHz^{-2}}. \tag{26}\]  
For \(T>20\ ^{\circ}\)C
## 3 Single-Ring RISC Problem  
As a moderate attempt to bring the traffic engineering considerations into play, this study concentrates on the RISC problem with single-ring phasing. We start with the concept of ring. In the language of traffic engineering, a ring organizes the signal phases at an intersection to display green in a sequence. Single-ring phasing controls multiple non-conflicting vehicle movements with a vehicle phase and allows the vehicle phases to be served one at a time. For a four-leg intersection with protected left-turn movements and permitted right-turn movements, there are two common options for the single-ring phasing, as shown in Figure 1. The leading left-turn phasing is much more popular than the lagging one [42].  
We profile the single-ring RISC problem as follows: current vehicle phase varies its green time between the minimum green and maximum green, with the step size of one second. Green time is followed by yellow change and red clearance intervals. When the red clearance interval ends, the current vehicle phase rests in red and the next vehicle phase in the sequence starts to display green. Phase skipping is prohibited. A pedestrian phase is timed using the minimum green for its adjacent, parallel through vehicle phase.  
There are many signal timing constraints that must be defined in advance. Each plays its important role in terms of safety or efficiency. Given the neglect of them in previous studies, here we introduce them as follows.  
The minimum green reduces the risk of rear-end crashes and assures a certain number of queued vehicles to be served. The maximum green limits the traffic handling capacity given to a vehicle phase and prevents the road users of any other movement from experiencing excessively long red time. The yellow change interval allows time for drivers to make a decision of stopping behind or passing through stop lines before the onset of red. The red clearance interval separates the conflicts between the current vehicle phase and the next phases. The phase sequence is pre-determined at all times of the day to avoid driver confusion and reduce the risk of red-light-running violations.
## 3 Methodology
### Bi-Level Formalization in Traffic Control Problems
The overall formal description of the bi-level problem can be given in the form:  
\[\min_{\mathbf{y}}f_{y}(\mathbf{x},\mathbf{y})\]  
\[\mathbf{y}\in\mathbf{S}_{\mathbf{y}}(\mathbf{x},\mathbf{y}),\]  
where:  
\[\mathbf{x}\equiv arg\equiv\left\{ \begin{array}{c}\min_{\mathbf{x}}f_{x}( \mathbf{x},\mathbf{y})\\ \mathbf{x}\in\mathbf{S}_{\mathbf{x}}(\mathbf{x},\mathbf{y})\end{array} \right.,\]  
where part of the constraints for \(\mathbf{y}\) is given as a set of solutions of \(\mathbf{x}\), from the lower-level problem. The advantages of this bi-level formalization come from the extended set of optimal solutions (\(\mathbf{x}\), \(\mathbf{y}\)), which allows the optimization to be performed by two goal functions in hierarchical order: \(f_{y}(\mathbf{x}\),\(\mathbf{y})\) and \(f_{x}(\mathbf{x}\),\(\mathbf{y})\), which simultaneously satisfy an enlarged set of constraints \(\mathbf{S}_{\mathbf{y}}(\mathbf{x}\),\(\mathbf{y})\cap\mathbf{S}_{\mathbf{x}}(\mathbf{x}\),\(\mathbf{y})\). Thus, the quality of the control can have better optimal properties. For the particular case of traffic signal optimization, the variables \(\mathbf{y}\) of the upper problem can correspond to the duration of the cycle, and the variables \(\mathbf{x}\) of the lower level can be the green signals. Respectively, the upper-level problem can target the maximization of the traffic flows in the network, while the lower-level problem can minimize the queue lengths and, consequently, the waiting times. The analytical formalization of the traffic optimization problem with simultaneous optimization of green and cycle times is formally presented according to the network topology, corresponding to an area in Sofia.