esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Site  
The study was carried out in a four-story academic building (with a basement) located in Jonkoping, Sweden (lat. 57.778168\({}^{\circ}\) N, long. 14.163526\({}^{\circ}\) E). To the left and right of the building are buildings of comparable height. Physical data were collected from 51 private office rooms on the second, third, and fourth floors. Most of the rooms are approximately 15 \(\pm\) 5 m\({}^{2}\) and are furnished mainly by a large desk, a chair, and one or more bookshelves. The rooms have windows in the north-east (21), south-west (19), north (9), and south-east orientations (7). Three rooms on the north-east and south-west side have a second window in the same direction. Five rooms on the south-west side have a second window on the south-east side. The number of rooms with two windows is highlighted in the parenthesis in Table 1. The size of the windows on the second floor on the north-east side is one third larger compared to that on the third and fourth floors. There is permanent solar shading at the building's south and south-west sides (see Figure 3). All rooms are equipped with conventional suspended luminaires and were used according to the users' needs during the survey, but they were not modeled nor further included in the study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Window Location** & **Frequency** & **Percentage** \\ \hline North-East (NE) & 21(3) & 41.2 \\ South-West (SW) & 14(3) & 37.3 \\ North (N) & 9 & 17.6 \\ South-East (SE) & 7(5) & 3.9 \\ Total & 51 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of rooms based on the location of the windows; rooms with two windows are shown within parenthesis.  
Figure 2: Three main research questions and the results of POE vs. PM comparison for different groups of participants during the first and second times (October and June).
## 3 Results
### Carbonates Grade  
The degree of carbonates in the concentrate reached its minimum value at pH 9.5 for all the depressors used. However, the minimum was reached at a neutral pH when no depressant was used (Figure 3). The minimum concentration of carbonates in the concentrate was 19% using QT + WD as a depressant in the flotation column. It has been shown that carrying out flotation of fluorite at pH higher than 9.5 leads to a greater flotation of carbonates, even when using different types of depressants.  
The effect of pH on the flotation of fluorite with QT has been studied quite thoroughly. In practical applications, the pH ranges from 8 to 9.5. This accounts for multiple factors of adsorption of the QT and the oleate, the acid consumption, and practical recovery and grade balances. [PERSON] et al. [24] indicated that QT remains a depressant up until a pH of around 10. The optimal adsorption pH for QT on fluorite surface is around 7, while the dissolution of calcite requires the pH be at or above 8.  
Figure 3: Cont.  
When depressants were not used, the grade of carbonates in the concentrate was practically the same as that of the feed, around 41%. The worst combination of depressants was again QT + SHMP since the grade of carbonates in the concentrate was still quite high, around 28% at pH of 9.5. It can be seen that using QT alone as a depressant presented very similar results to QT + PS and QT + CMC. The only combination that improved the effect of QT as a depressant has proven to be QT + WD.
## 5 Results and Discussions
### Agent Rewards Accumulation  
The rewards accumulated by the NSA from the simulations are shown in Figure 3. The proposed scheme is evaluated based on network states 1, 2, and 3, as shown in Figure 3a-c, respectively. The simulation is implemented using a total node value of 300 for maximum resource distribution. Additionally, for this simulation, we have only taken the rewards gained for all optimal actions chosen, which resulted in creating a network slice for an NSR.  
Based on the results, it can be observed that the rewards earned by the agent gradually decrease as the number of NSRs received by the network increases. This decline in rewards is due to the increase in resource utilization of the network, which affects the RIFs used for the reward calculation.  
Similarly, the previously defined network states are essential indicators of the current resource availability in the network. Moreover, it is observed that the upper bound for the maximum reward also decreases due to this condition. However, unlike the other two algorithms, the DCB-NS strategy achieves higher rewards. This advantage is due to the efficiency of the UCB strategy in selecting the ideal actions by evaluating their confidence value, which increases the certainty of higher incentives for each scenario. On the other hand, Thompson sampling leads to an unbalanced distribution of actions due to the random nature of the sampling process, which is in contrast to the consistent performance of UCB [32]. Moreover, the algorithm is likely to become unstable during extended training periods. The Epsilon-Greedy algorithm achieves the lowest rewards because it simply relies on the randomness of the Epsilon value to balance exploration and action utilization [22]. Moreover, this algorithm explores only the neighboring solutions once an inferred distribution is known, leading to inefficient action policies. This property  
Figure 3: Cumulative Agent Rewards for (**a**) Network State 1, (**b**) Network State 2, and (**c**) Network State 3.  
contrasts with the uniform distribution of action probabilities of the proposed scheme using the Softmax function, which ensures a higher likelihood of action exploration.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 4 Covariance Analysis
### Measurement Model  
The mathematical model for the covariance analysis adapted in this paper is based on previous works [29] (to which the reader is referred for a full derivation). The design matrix \(H_{k}\) is repeated here and completed with the DEM measurement for completeness in Equation (11), in which \(\|\cdot\|\) represents the Euclidean norm, \(\mathbf{r}_{r}^{m}\) represents the distance from the rover to LCNS satellite \(m\), \(\mathbf{r}_{r}^{DEM}\) represents the distance from the centre of the Moon to the rover given by the DEM and \(\mathbf{0}\) represents a \(3\times 1\) column vector containing zeroes.  
\[H_{k}=\begin{bmatrix}H_{p}\\ H_{p}\\ H_{DEM}\end{bmatrix}=\begin{bmatrix}-\frac{\mathbf{r}_{r}^{T}}{\|\mathbf{r}_{r}^{T}\| }&\mathbf{0}^{T}&1&0\\ \vdots&\vdots&\vdots&\vdots\\ -\frac{\mathbf{r}_{r}^{T}}{\|\mathbf{r}_{r}^{T}\|}&\mathbf{0}^{T}&1&0\\ -\frac{(\mathbf{r}_{r}^{T}-(\mathbf{r}_{r}^{m}\mathbf{u}_{r}^{m})\mathbf{u}_{r}^{T})}{\|\mathbf{r }_{r}^{T}\|}&-\frac{\mathbf{r}_{r}^{T}}{\|\mathbf{r}_{r}^{T}\|}&0&1\\ \vdots&\vdots&\vdots&\vdots\\ -\frac{(\mathbf{r}_{r}^{m}-(\mathbf{r}_{r}^{m}\mathbf{u}_{r}^{m})\mathbf{u}_{r}^{m})^{T}}{\| \mathbf{r}_{r}^{M}\|}&-\frac{\mathbf{r}_{r}^{T}}{\|\mathbf{r}_{r}^{m}\|}&1&0\\ \frac{\mathbf{r}_{r}^{DM}}{\|\mathbf{r}_{r}^{DM}\|}&\mathbf{0}^{T}&0&0\end{bmatrix} \tag{11}\]  
Note that this model represents the case where at least 4 satellites are visible such that the rank of the design matrix \(H_{k}\) is equal to or larger than the number of states. However, in case only 3 satellites are visible and 1 DEM measurement is available, the rank of the design matrix is 7, while the number of states is equal to 8 (note that a DEM range-rate measurement would not be realistic as the DEM resolution is 5 m per pixel and the rover moves at a velocity of 0.1 m per second). Therefore, in the case of 3 available satellites, the LCNS range-rate measurements are disabled, meaning \(H_{p}\) in Equation (11) is removed. In such a case, only position is estimated directly, while the velocity of the rover is propagated through the dynamical model.
## 4 Proposed Solution
### Controller Design  
The output of the optimization method is a new position vector \(\overrightarrow{p}_{r}^{i+1}=({x_{r}}^{i+1},{y_{r}}^{i+1})\) for the relay robot. A controller should be designed to move the relay robot to the new position. A closed-loop incremental position controller, as shown in Figure 5, is devised as the solution for Problem 4. The RSO algorithm consists of the following sub-modules: multi-sensor RSS sensing, temporal and spatial smoothing, gradient estimation using the CFD method, gradient normalization and the SGA algorithm. The motion dynamics is controlled using a position controller acting on the relay node. The radio environment variables are unknown (hidden) to the algorithm, as indicated in Figure 5. The controller is stopped on meeting both of the following two conditions:* the difference in the benchmark values of the RSS from the server and client nodes is less than a threshold value, \(|\overline{RSS_{c}}-\overline{RSS_{s}}|<\mathrm{RSS_{difference}}\);
* the \(l^{2}\) norm of the gradient is less than a gradient threshold (which is close to zero), \(\|g\|<g_{\mathrm{difference}}\).  
In addition to the algorithmic parameters, such as \(\Delta x\), \(\Delta y\), \(\kappa\), and stop criterion parameters, such as \(\mathrm{RSS_{difference}}\), \(g_{\mathrm{threshold}}\), the inputs to the algorithm are relay node position vector \(\overrightarrow{p}_{r}\) and five RSS measurements each for server and client sides. The next sub-section presents the implemented algorithm.
## 4 Results and Discussion
### Experiment 2: Evaluation of the Proposed Method on Cross-Database
#### 4.3.2 Characterization of PI-BSMs through Adversarial Learning  
Figure 8 showcases the baseline n-BSM and PI-BSM images derived from the AFib ECG signals of distinct patients in the SPH dataset. For the same AFib rhythm, three baseline n-BSM images exhibit contrasting patterns, making it challenging to discern an identical rhythm type. In contrast, the PI-BSM images exhibit stronger similarity between the same AFib rhythm samples. This is due to the amplitude variation in the 1D signals among patients, even for the same AFib rhythm, as illustrated in Figure 8. Therefore, the beat-score vectors generated to form the baseline n-BSM show varying beat predictions by each patient, leading to a failure to capture shared information for AFib rhythms. Unlike n-BSMs, the proposed method using PI-BSMs enables focusing on learning the beat characteristics while excluding the individual characteristics by adversarial learning. As a result, it has become possible to clearly distinguish between different ECG rhythms by leveraging shared information about the same rhythm across patients.  
Figure 7: PI-BSM images varying with different hyperparameters \(\lambda_{2}\) are shown in (**a**) for AFIB, (**b**) for GSVT, (**c**) for SB, and (**d**) for SR.
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 3 Hyper- and Multispectral Imaging
### Preprocessing  
Due to the advantages of HSI and MSI, which have been mentioned above, the performance of both instruments is primarily affected by their sensitivity, calibration, physical mechanism, and the surroundings [63]. Spike is a common phenomenon that can be found within the spectrum body. It presents as extreme rise and fall waves. The complexity of plant geometries, i.e., spherical, elliptic, wavy, or irregular shape, has made it necessary to correct images with preprocessing, which is inspired by chemometrics [50]. Firstly, the average of spectral data can be employed since averaging can reduce noises and correct the illumination effect [49]. In addition, spectral averaging is the simplest preprocessing method. This method has been successfully applied to predict the plant physiology and leaf water content of maize [64]; the water content, micro-, and macronutrients in maize and soybean [65]; and the water and nitrogen contents in wheat [66]. Secondly, other preprocessing techniques, namely multiplicative scatter correction (MSC) and standard normal variate (SNV), can be established to reduce variability due to scattering or baseline shift. Thirdly, smoothing by applying a Savitzky-Golay filter can also be established.
## 5 Simulation Results
The reason for the increase is that with the increase of the transmission power of the picocell BS, since the SIR threshold of the micro-base station is low, increasingly more users will access the micro-base-station, while because the power of the picocell BS is small, EE will be improved at this time. However, when the transmission power of the picocell BS is greater than a given value, the number of access users will be saturated, and the continuous increase of the transmission power of the picocell BS will not lead to the increase of network throughput, so the EE will gradually decrease. It can also be seen that the simulation results are in line with the theoretical results. In addition, we can conclude that if \(\lambda_{u}\), \(\lambda_{1}\), \(\lambda_{2}\), and \(P_{T1}\) are given, we can find an optimal \(P_{T2}\) that maximize EE. In Figure 4, we evaluate the network EE with various target SIR thresholds to analyze the impact of different optimization schemes on EE. We use MATLAB (R2015b) to realize the golden section optimization algorithm. The CPU of our computer is 3.8 G, and the execution times to solve optimization Equations (14) and (16) are 0.0161 and 0.0159 s, respectively. The final optimal pair is obtained after 16 iterations of alternate optimization, and the execution time of our proposed optimization method is about 0.57 s.  
Figure 3: Network EE versus picocell BS transmit power. From Figure 4 we can see that with the increase of target SIR, the network EE curve rises first and then drops. This is because the achievable data rate of the tagged MUE increases as \(\gamma_{1}\) grows, while the coverage probability decreases as target SIR increases. According to the optimization method given in Section 4.1, we calculate that \(\lambda_{2}^{opt}=4.4\times 10^{-3}\) m\({}^{-2}\). It can be seen that the optimal solution \(\lambda_{2}^{opt}\) is in agreement with the simulation results shown in Figure 2. According to the optimization method given in Section 4.2, which is also the optimization method proposed in [28], it can be calculated that \(P_{\rm T2}^{opt}=1.9\times 10^{-2}\) W. According to the proposed joint optimization algorithm, it can be calculated that the optimal pair is \(\left(P_{\rm T2}^{opt}=2.9\times 10^{-2}\text{W},\lambda_{2}^{opt}=3.8 \times 10^{-3}\text{m}^{-2}\right)\). We also compare the proposed joint optimization scheme with the optimization algorithms proposed in [31], as well as the schemes for fixed power and density. The proposed joint optimization scheme can obviously improve network EE. Therefore, in HCNs, BS density and transmit power should be carefully designed. Otherwise, arbitrarily designed density or transmit power will decrease the network EE.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 3 Hierarchical Task Assignment and Path Finding
### HTAPF: Principles and Methods
#### 3.2.3 Decentralized Task Assignment and Motion Planning  
Task assignment and motion planning are performed concurrently by exploiting a priority-based scheme that assumes robots are ordered and plan sequentially, treating the plans of previous robots as known dynamic obstacles. Such an approach is in general not complete, but scales linearly with the number of robots. We implement this approach in a distributed way, similar to previous work ([PERSON] et al., 2018), as outlined below.  
At the beginning, we pre-compute the all-pair shortest paths in a pre-processing step using the Floyd-Warshall algorithm. This can be computed in a decentralized way by each robot or pre-computed and given to each robot as part of the environment description. We use the all-pair shortest paths in two ways. First, we normalize the all-pair shortest paths and use the resulting values for our heuristic function \(H_{\text{FW}}(\cdot,\cdot)\), which informs our utility function (see Equation (3)). Second, the knowledge of the all-pair shortest paths is an admissible heuristic that is used within the path planning algorithm to speed up the computations.  
To this end, as the path planning algorithm, we use Safe Interval Path Planning (SIPP) ([PERSON], 1991), a variant of A* that can plan efficiently in the space-time domain if the trajectories of dynamic obstacles are known a-priori. We make an adjustment to the algorithm to better cooperate with the assignment algorithm. We let the decision-making algorithm reason on goal regions rather than single cells: when a goal region is selected as the next assignment by the decision-making algorithm, we consider any cell in the goal region \(a_{g}\) as a possible goal, therefore solving a task assignment and path planning problem simultaneously. Specifically, each cell containing a task within a leaf node of the quad-treeis evaluated and the one with lowest cost is selected as destination. This allows the assignment process to reason about the expected number of tasks in the goal region rather than having to consider low-level congestion information of the path planner. We note that there is no explicit synchronization and that our approach is fully distributed and asynchronous.  
We then broadcast our current plan to neighboring robots, that update their local knowledge accordingly using received costs to update their local utility (also see Algorithm 1). Our approach does not rely on a fixed priority (e.g., Reference [12]), or implicit synchronization using tokens (e.g., Reference [43]). However, a disadvantage is that safety can only be guaranteed with the assumed sensing capabilities of neighboring robots. Reactive obstacle avoidance prevents collisions that might be caused by our probabilistic communication model or dynamic ordering of the robots: a movement is performed only if the target cell is free, otherwise re-planning is triggered.
# A Systematic Review of Computational Fluid Dynamics Models in the Stomach and Small Intestine
## 3 Results
### Governing Equations, Boundary Conditions and Initial Conditions  
In these CFD models, the fluid is regarded as a continuum or as a continuous phase and a set of governing equations are derived based on the laws of conservation of mass, momentum, and energy. The fluid density changes within the GI are negligible, i.e., incompressible flow is present, therefore the conservation of momentum is expressed as,  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}=- \frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+\frac{\mu}{\rho}\frac{\partial^ {2}u_{i}}{\partial x_{j}^{2}}+f_{i} \tag{1}\]  
where \(u\) is the flow velocity, \(t\) is the time, \(\rho\) is the fluid density, \(\mu\) is the fluid viscosity, \(p\) is the fluid pressure, \(f\) is any additional body forces (e.g., gravity) and \(i\) and \(j\) denote spatial dimensions (\(x\), \(y\) and \(z\)).  
An additional equation for the conservation of mass is also required since the current system only has three equations for four unknowns (\(x\), \(y\) and \(z\) velocities and pressure). For an incompressible fluid the conservation equation is,  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{2}\]  
Typically a no-slip boundary condition is imposed at walls i.e., the fluid velocity is equal to the wall displacement velocity,  
\[u_{i}=u_{w}, \tag{3}\]  
where the wall velocity (\(u_{w}\)) is determined from the prescribed boundary deformations. At an inlet, such as the entrance to the small intestine, either an inlet flow rate or a velocity profile is typically prescribed. At an outlet, such as the pylorus of the stomach, an outflow condition is typically prescribed. In these CFD models, the fluid within the domain is typically initialized with a zero velocity.  
Figure 1: PRISMA study flow diagram. The number of articles related to the stomach and small intestine are identified by \(n\) and \(m\), respectively.
## 5 Conclusions  
In conclusion, this paper presents a novel approach to V2V communication that combines low-computational algorithms with a high efficiency, tailored for diverse environments. This anti-failure strategy significantly enhances vehicular safety by mitigating the risk of accidents. The utilization of switched beam antennas has proven to be highly  
Figure 17: Algorithm execution time in an antifailure algorithmic system.  
Figure 18: ns3 vehicular node visualization with message communication data rate.  
effective, with a minimal power consumption. Through extensive simulations and experiments, the proposed strategy has demonstrated an exceptional performance, surpassing the limitations of current techniques.  
The results indicate that the integration of algorithms with switched beam antennas enhances the efficiency of V2V networks from several aspects. These include a reduction in the packet error rate, an improved beam selection probability, a stable average throughput, a lower bit error rate, and an improved time to collision compared to non-algorithmic or high-computational-resource approaches. Furthermore, the incorporation of mathematical models into the algorithms enhances the beam power efficiency, particularly in switched beam scenarios with limited computational power. This research provides cutting-edge and efficient solutions for the latest vehicular networks, significantly enhancing their performance.  
For future work, the integration of distributed ledger technology into 5G can be explored to address privacy concerns in V2V communication, ensuring data integrity and preventing tampering. Additionally, it can provide protection against reverse cyberattacks at the DSRC message exchange nodes, further enhancing the security of the system.  
This study primarily focuses on a simulative analysis, which may not fully capture the complexities and dynamics of real-world V2V communication environments. Real-world factors such as varying traffic conditions, interference, and non-ideal channel conditions are not fully accounted for in the simulation.  
Conceptualization, T.H.A. and J.J.T.; methodology, T.H.A. and J.J.T.; software, T.H.A.; validation, J.J.T. and D.-T.D.; formal analysis, D.-T.D.; investigation, J.J.T. and A.M.; resources, A.M.; data curation, C.G.-C.; writing--original draft preparation, T.H.A. and J.J.T.; writing--review and editing, D.-T.D. and A.M.; visualization, C.G.-C.; supervision, J.J.T.; project administration, J.J.T.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. This research was funded by Multimedia University & Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS) number MUME/210058 (FRGS/1/2021/TK02/MMU/02/1) and MMUE/190229 (FRGS/1/2020/TK02/MMU/03/1).  
Not applicable.  
The authors declare no conflict of interest.