esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Experiments
### Experimental Setting and Implementation  
Throughout this study, all computation times encompass the entire algorithmic process, from its initialization phase to reaching the stopping criterion and reporting the best solution found. This comprehensive approach ensures unbiased comparisons between the execution times of sequential and parallel implementations of the same algorithm. Specifically, in the case of parallel implementations running on the GPU, considerations such as algorithm initialization, data movements, overheads associated with kernel calls, and other sequential computation costs inherent in its heterogeneous approach are factored into their computational times.  
The sequential and parallel implementations were written in Julia programming language [39] (version 1.9.4) using double-precision floating-point arithmetic. Julia is an open-source and dynamic high-level language for scientific computation that shares syntax similarities with MATLAB, a widely adopted language in academia and industry for optimization problems. The parallel implementations leveraged the _CUDA.jl_ package [40] (version 5.1.1) that serves as the primary gateway for programming NVIDIA GPUs in Julia.  
To ensure a more rigorous and unbiased comparison of results, meticulous care was taken to implement the sequential and parallel algorithms in a highly standardized manner. This deliberate approach helps ensure a fair and accurate assessment of their performance.  
The algorithmic parameters selected for this study encompassed a range of problem dimensions extending from 500 to 2000, with increments of 500. Additionally, the population size was determined to be 10 times the problem dimension, resulting in a range of population sizes from 5000 to 20,000, with increments of 5000. To improve the accuracy and reliability of experimental results, a total of 31 independent runs were carried out for every combination of algorithm, problem instance, and dimension before averaging the results, and the stopping criterion for each run was set to 1000 iterations.  
The Julia programming language utilizes a just-in-time (JIT) compilation process, translating high-level code into machine code [41]. This particular feature often results in longer execution times during the initial runtime of a Julia program. To ensure the integrity of the computational analysis, a warm-up phase was implemented that runs prior to testing. During this phase, a single run of each test combination is executed, and the results are discarded. Ultimately, this approach effectively prevents the inclusion of the delay caused by the JIT compiler during the first-time loading and compiling of Julia code.  
Both the sequential and parallel implementations used the same parameters, ensuring a consistent evaluation across all experiments.  
Sequential computation times of the tested algorithms serve as a reference point for performing a comparative analysis aimed at quantifying the degree of acceleration achieved by their corresponding GPU-based implementation.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
###### Abstract  
In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km\({}^{2}\) in 2020, with 372.04 km\({}^{2}\) located on the mainland and 206.50 km\({}^{2}\) distributed throughout the country's islands and caves. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.  
Keywords:mangrove; Belize; Google Earth Engine; Sentinel-2 +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Preliminaries
#### 3.1.4. Arithmetic Coding  
Arithmetic coding is a technique used in data compression to encode a sequence of symbols or characters with variable-length codes. Unlike more common methods like Huffman coding, which assigns fixed-length codes to individual symbols, arithmetic coding encodes entire sequences of symbols into a single code. Its typical process involves the following steps:  
* Symbol Probability Modeling: The process starts with a probability model that estimates the probability of each symbol in the input sequence.
* Defining the Range: Represent the entire range of possible values with a fractional range between 0 and 1. This range is divided proportionally to the probabilities of the symbols.
* Mapping Symbols to Subranges: Each symbol in the input sequence is then mapped to a subrange within the total range based on their probabilities.
* Iterative Encoding: As each symbol is encoded, the subrange corresponding to that symbol is selected and used to narrow down the overall range further.
* Outputting the Code: The final output is a binary fraction within the narrowed range, which represents the compressed form of the entire input sequence.  
Arithmetic coding is advantageous in that it can achieve higher compression ratios compared to methods like Huffman coding because it utilizes the probabilities of entire sequences rather than individual symbols. However, it requires more computational resources and may be more complex to implement efficiently.
## 3 Results and Discussion
### Cytotoxicity Assessment  
For samples Mg_10 and Mg_11, no cytotoxic effects were observed, with the cells forming clusters and maintaining normal morphology throughout the assessment period. This indicates that the surface modifications applied to these samples did not compromise cell viability or functionality. However, for the remaining samples, a reduction in cell viability was noted, which was attributed to an increase in the pH of the medium, which reached approximately 8.5. This pH increase correlated with a more pronounced release of hydrogen gas, a typical byproduct of magnesium corrosion. As the pH rose, it created a more alkaline environment, potentially altering cellular behavior and leading to a decrease in viability.  
Figure 14 presents the results for all tested samples. A key observation is that nearly all of the samples did not show cytotoxicity, with cell viability exceeding the 70% threshold compared to the control culture, as outlined in ISO 10993-5 [54] for the evaluation of medical devices. This suggests that these materials can be classified as non-toxic under the specified conditions, thus reinforcing their suitability for biomedical applications.  
The formation of additional protective coatings on the magnesium surfaces effectively reduced biocorrosion and slowed the pH increase, likely due to the buffering properties inherent to the culture medium. The electrolyte composition, which included L-aspartic acid and lysine, could have further influenced the bioactivity of the magnesium implants. Both L-aspartic acid and lysine are known to enhance surface modification processes during plasma electrolytic oxidation (PEO), potentially improving the corrosion resistance and biocompatibility of magnesium alloys. L-aspartic acid, being an amino acid with a carboxyl group, may contribute to the formation of a stable oxide layer, while lysine,with its amino group, could provide additional interactions with the surface, influencing both the formation of the oxide coating and the material's interaction with surrounding biological tissues. These components, when incorporated into the electrolyte, may also play a role in stabilizing the pH levels in the culture medium, further supporting the non-toxic nature of the samples.  
Among all the samples, Mg_11 showed the most promising results, demonstrating a significant improvement in cell viability and overall biocompatibility, which makes it a suitable candidate for further development and clinical applications in biomedical fields, particularly in dental implantology and orthopedic applications.
## 4 Simulation Results and Performance Analysis
### Simulation Results
Hence, its acquisition time is the longest among different signal types. A tradition (serial search) method is superior in its easy implementation of hardware, but is inferior in parameter precision than other methods. Parallel code delay with fixed code/Doppler search space method [28] is higher in parameter precision rapidity, but is also higher in hardware cost. Although parallel frequency with a fixed code/Doppler search space boundary method is rapid in acquisition time, it is limited by the length of FFT and sampling rate. Thus, this method cannot be efficiently promoted and is seldom applied to realistic circuits. In addition, a parallel code delay with adaptive logic control method is low in hardware implementation complexity, high in estimated parameter precision and parallels the proposed method; the parallel code delay with proposed method is more rapid and efficient in acquisition time. Despite the fact that the proposed method is somewhat higher in hardware implementation complexity (count on number of initial population), it has the potential to be implemented in hardware under the gradual and continuous enhancement of integration circuit design technique.
## 3 Formulation for a Mathematical Model of the DCBAP
### Mathematical Model  
This section formulates a mathematical model for the DCBAP. Firstly, the assumptions, parameters, indices, and decision variables for this model are introduced.  
Assumptions  
1. Each ship is handled continuously until it is completed.
2. Each ship has an estimated processing time.
3. Each ship has a desired berthing position.
4. Inter-ship clearance distance is included in ship length.
5. A ship departs immediately if completed.  
Indices  
\(j\) a ship number; \(j\in\ J=\{1,\ldots,N\}\)  
\(x\) a start berthing time; \(x\in\ X=\{1,\ldots,H\}\)  
\(y\) a berthing position; \(y\in Y=\{1,\ldots,L\}\)  
Parameters  
\(L\) the quay length (meters)  
\(H\) the planning horizon (hours)  
\(N\) the total number of ships to call within the planning horizon \(H\)  
\(l_{j}\) the length of ship \(j\)  
\(ETA_{j}\) the expected time of arrival of ship \(j\)  
\(d_{j}\) the desired berthing position of ship \(j\)  
\(\beta\) the berth deviation factor (\(\beta\geq 0\)); increased handling times in hours per 100 m deviating from the desired berthing position of a ship  
\(c1\) the cost rate of waiting time (USD/hour)  
\(c2\) the cost rate of handling time (USD/hour)  
Decision Variables  
\[X_{jxy}=\left\{\begin{array}{c}1,\text{ if the ship }j\text{ is assigned with the berthing position }(x,y)\\ 0,\text{ otherwise}\end{array}\right.\]\(B_{j}\) the berthing position of ship \(j\) (\(j\in J\))  
\(S_{j}\) the actual start berthing time of ship \(j\) (\(j\in J\))  
\(C_{j}\) the completion time of ship \(j\) (\(j\in J\))  
\(\Delta W_{j}\) the increased waiting time of ship \(j\) (\(j\in J\))  
\(\Delta H_{j}\) the increased handling time of ship \(j\) (\(j\in J\)).  
The Mixed Integer Programing (MIP) model of the DCBAP can be formulated as follows.  
\[Min\ Z=\sum_{j=1}^{N}\left(c_{1}\cdot\Delta W_{j}+c_{2}\cdot\Delta H_{j}\right) \tag{9}\]  
\[\text{s.t.}\ \sum_{x=1}^{H}\sum_{y=1}^{L}X_{jxy}=1\quad\forall j\in J \tag{10}\]  
\[\Delta W_{j}\geq 0\quad\forall j\in J \tag{11}\]  
\[\Delta H_{j}\geq 0\quad\forall j\in J \tag{12}\]  
\[B_{j}\leq L-l_{j}\quad\forall j\in J \tag{13}\]  
\[B_{j}\geq 0\quad\forall j\in J \tag{14}\]  
\[l_{j}>0\quad\forall j\in J \tag{15}\]  
\[l_{j}\leq L\quad\forall j\in J \tag{16}\]  
\[X_{jxy}\in\{0,1\}\quad\forall j\in J;\quad\forall x\in X;\quad\forall y\in Y \tag{17}\]  
Equation (9) is the objective function for minimizing the total cost (\(Z\)), including the increased waiting cost, as well as the increased handling cost. Equation (10) stipulates that one berthing position can be assigned to only one ship at a time. Equation (11) is a requirement for \(\Delta W_{j}\) stipulating that \(S_{j}\) should not start before \(ETA_{j}\). Equation (12) defines a requirement for \(\Delta H_{j}\). Equations (13) and (14) are conditions for the assigned berthing position for a ship. Equations (15) and (16) are physical conditions for a ship. Equations (17) comprise a binary constraint for decision variables \(X_{jxy}\).
## 4 Conclusions  
In the paper, the lid-driven square cavity flow with embedded circular obstacles was numerically simulated by using spectral/hp element methods. The variation of the flow at various diameters of internal two-dimensional circular obstacles inside the cavity and Reynolds numbers were investigated. Numerical results were analyzed and compared with the available data in the literature to validate the performance and accuracy of the current methods. The conclusions of this paper can be summarized as follows:  
* The stream function and the locations of the primary and secondary vortices obtained by the present methods are compared with published data. These results are in very good agreement with existing results. The validity of the numerical solution is verified.
* The simulation based on the spectral/hp element methods only needs less preprocessing, has higher accuracy, which can be used not only to analyze the lid-driven square cavity flow but also other 2D and 3D incompressible viscous flows.
* The main advantages of the spectral/hp element methods are high-resolution characteristics and less preprocessing required. The spectral/hp element methods capture new vortices in numerical analysis of the lid-driven square cavity flow with embedded two-dimensional circular obstacles at \(r=1/6\), \(Re=100\) and \(r=2/5\), \(Re=5000\) (see Figures 5 and 8). However, these new vortices cannot be observed in the lattice Boltzmann method.
* Although the spectral/hp element methods have the advantages of less preprocessing, high-resolution characteristics and faster convergence rate, due to the use of high-order interpolation, despite the hierarchic type shape functions being employed, higher computation cost is still inevitable. Hence, it is an important to balance between accuracy and computation cost.  
Conceptualization, J.Z. and B.X.; methodology, J.Z.; software, W.Y. and B.X.; validation, J.Z., B.X. and W.Y.; formal analysis, J.Z. and W.Y.; investigation, B.X.; resources, B.X.; data curation, W.Y.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z. and B.X.; visualization, B.X. and W.Y. supervision, J.Z.; project administration, J.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51769011).  
Not applicable.  
Data Availability Statement: Data is contained within the present article. Other data presented in this research are available in [8; 13].  
The authors declare no conflict of interest.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### Recommendation Phase  
Recommendation phase extracts the candidate POI based on the analyzed databases and makes results based on Top-k Ranking. The POI candidate group is recommended in the recommendation phase by employing an algorithm that is appropriate for each group based on the user classification during the analysis phase. In the case of a user group with high activity, multiple clusters are formed within the group. A POI candidate group is established through collaborative filtering by utilizing similar users. In the case of a user group with low activity, the category similarity is calculated by considering the activity pattern, and the POI is recommended by taking into account the distance and check-in frequency.  
In the recommendation phase, the user's input information is utilized to generate an activity index, which is subsequently used to ascertain whether the user is a member of a group with high or low activity. If user data already exist, these are utilized; otherwise, the user is classified to be in a low-activity group. The groups are classified through labeling: label 1 denotes a group with high activity and label 0 denotes a group with low activity. After the classification of the groups, each algorithm recommends the most suitable POIs.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach
### Dual of the Sub-Problem  
The dual of the above-formulated sub-problem is as follows:  
\[\begin{array}{c}Max\vartheta=-(\sum\limits_{i,j\
eq i}(u_{ij}+v_{ij})+\sum \limits_{i,j\
eq i}\sum\limits_{k\
eq i,j}\left(s_{ijk}+w_{ijk}\right)h_{k}+ \sum\limits_{j,k\
eq j}p_{jk}h_{k}+\sum\limits_{i,l\
eq i}q_{il}h_{l}+\sum \limits_{i,j\
eq i}d_{ij}\left(h_{i}+h_{j}\right)\\ \hskip 28.452756 pt+\sum\limits_{i,j\
eq i}\sum\limits_{k\
eq i,j}\left(a_{ijk} (1-h_{i})+b_{ijk}(1-h_{j})\right)-\sum\limits_{i,j\
eq i}\sum\limits_{k,l \
eq j\
eq k}o_{ijkl}y_{kl})\end{array} \tag{58}\]  
Subject to  
\[2w_{ij}m_{i}h_{i}+2w_{ij}m_{j}h_{j}+u_{ij}+v_{ij}+p_{ji}+q_{ij}+2d_{ij}\geq- \alpha w_{ij}C_{ij},\hskip 28.452756 pt\forall i,j\
eq i, \tag{59}\]  
\[w_{ij}m_{j}h_{j}+w_{ij}m_{k}h_{k}+v_{ij}+r_{ijk}+w_{ijk}+q_{ij}+o_{ijkj}+d_{ij} \geq-\alpha w_{ij}C_{kj}\forall i,j\
eq i,k\
eq i,j, \tag{60}\]  
\[w_{ij}m_{k}h_{i}+w_{ij}m_{l}h_{l}+u_{ij}+p_{ji}+d_{ij}+s_{ijl}-r_{ijl}+o_{ijl} \geq-\alpha w_{ij}C_{il},\hskip 28.452756 pt\forall i,j\
eq i,l\
eq i,j \tag{61}\]  
\[w_{ij}m_{k}h_{k}+w_{ij}m_{l}h_{l}+r_{ijk}-r_{ijl}+s_{ijl}+w_{ijk}+o_{ijl}\geq- \alpha w_{ij}C_{kl},\hskip 28.452756 pt\forall i,j\
eq i,k\
eq i,j \tag{62}\]  
\[w_{ij}m_{k}h_{k}+u_{ij}-r_{ijk}+s_{ijk}+a_{ijk}\geq-w_{ij}C_{ik}\hskip 28.452756 pt \forall i,j\
eq i,k\
eq i,j \tag{63}\]  
\[w_{ij}m_{k}h_{k}+v_{ij}+r_{ijk}+w_{ijk}+b_{ijk}\geq-w_{ij}C_{kj}\hskip 28.452756 pt \forall i,j\
eq i,k\
eq i,j \tag{64}\]  
\[2w_{ij}m_{i}h_{i}+2w_{ij}m_{j}h_{j}+u_{ij}+v_{ij}+d_{ij}\geq-w_{ij}C_{ij} \hskip 28.452756 pt\forall i,j\
eq i \tag{65}\]  
\[-2m_{k}-\
abla ma_{k}+mi_{k}\geq 0\hskip 28.452756 pt\forall k \tag{66}\]  
\[\sum\limits_{k,l(k)=1}ma_{k}-\sum\limits_{k,l(k)=1}mi_{k}\geq 0 \tag{67}\]  
\[d_{ij},e_{ij},p_{ij},q_{ij},a_{ijk},b_{ijk},s_{ijk},w_{ijk},o_{ijkl}\in R^{+} \tag{68}\]\[u_{ij},v_{ij},r_{ijk},m_{k},ma_{k},mi_{k}\ URS \tag{69}\]  
By solving this dual problem, we can generate the cut for the master problem, which is as follows:  
\[\begin{array}{c}-(\sum\limits_{i,j\
eq i}\big{(}u_{ij}+v_{ij}\big{)}+\sum \limits_{i,j\
eq i}\sum\limits_{k\
eq i,j}\Big{(}s_{ijk}+w_{ijk}\Big{)}h_{k}+ \sum\limits_{j,k\
eq j}p_{jk}h_{k}+\sum\limits_{i,j\
eq i}q_{il}h_{l}+\sum \limits_{i,j\
eq i}d_{ij}\big{(}h_{i}+h_{j}\big{)}\\ +\sum\limits_{i,j\
eq i}\sum\limits_{k\
eq i,j}\Big{(}a_{ijk}(1-h_{i})+b_{ijk} \big{(}1-h_{j}\big{)}\Big{)}-\sum\limits_{i,j\
eq i}\sum\limits_{k,j\
eq k}o_{ ijkl}y_{kl}\big{)}\leq\vartheta\end{array} \tag{70}\]
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Optical Phased Arrays
#### Mixed Approaches  
Some successful proposals of lidar imagers have mixed the two imaging modalities presented above, that is, they have combined some scanning approach together with some multiple detector arrangement. The cylindrical geometry enabled by a single rotating axis combined with a vertical array of emitters and detectors has been very successful [84] to parallelize the conventional point scanning approach both in emission and detection. These so-called spinning lidars increase data and frame rate while enabling for each detector a narrow field of view, which makes them very efficient energetically and, thus, able to reach long distances. A comparable approach has also been developed for flash lidars with an array of detectors and multiple beams mounted on a rotating axis [85]. These spinning approaches obviously demand line-shaped illumination of the scene. They may enable 360 deg vision of the FOV, in difference with MEMS or flash approaches which only retrieve the image within the predefined FOV of the unit.  
Another interesting and successful mixed approach has been to use 1D MEMS mirrors for scanning a projected line onto the target, which is then recovered by either cylindrical optics on a 2D detector array, or onto an array of 1D detectors [52]. This method significantly reduces the size and weight of the unit while enabling large point rates, and provides very small and efficient sensors without macroscopic moving elements.
## 2 Materials and Methods
### Flow Cytometry Analysis and Gating Strategy  
Flow cytometry analyses were conducted using standard BD FACSCanto II and Fortessa configurations (BD Biosciences, Franklin Lakes, NJ, USA). The cytometer underwent calibration for FSC resolution using Megamix sizing beads (a fluorescent bead blend with a 2:1:1 ratio of 0.5, 0.9, and 3 \(\upmu\)m diameters) and for SSC resolution using Megamix-Plus SSC (a blend of 0.16, 0.20, 0.24, and 0.5\(\upmu\)m beads), both acquired from Biocytex, France. For silica-viral-mimicking nanoparticle (VMNP) analysis, voltages were set at FSC = 412 V, SSC = 434 V, and FITC = 654 V. For anti-spike-coated MNP (AS-MNP) characterization, voltages were set at FSC = 209 V, SSC = 357 V, FITC = 286 V, and APC = 300 V.  
To eliminate NP doublets and clumps, which could generate a high-fluorescence background and subsequently increase the rate of false positive events, FSC-H vs. FSC-A and SSC-H vs. SSC-A sequential gating was used as a double discrimination strategy. Moreover, to eliminate naked singlet nanoparticles (not involved in viral immuno-capture and thus considered as false negative events), only APC-positive singlets were selected (gated) to determine FITC-positive events. The (%) of double-positive events (APC+/FITC+) was quantified and represented as the MICaFVi value (Figure S1, Supplementary Materials). AS-MNPs incubated with PBS (negative control sample) were used to set the zeroing (background) of the FITC signal.
# Victim Detection and Localization in Emergencies
## 7 Results
### Performance of Different Technologies under Rubble  
UWB and WiFi FTM are outstanding precise technologies for challenging scenarios such as indoors. Nevertheless, once the device is buried under rubble, the performance of the ranging estimation is degraded by severe multipath and attenuation. Figure 4 shows the ranging estimation error of the UWB anchors (UWB1, UWB2, UWB3, and UWB4) and WiFi FTM (WiFi1, WiFi2, WiFi3) in both cases: under rubble (blue) and outside of the rubble (red). The 80 th percentile of error in both technologies increased considerably around 1 m. An important observation was that, during the whole measurement campaign under rubble, no packet was captured by the smartphone from a UWB reference point further  
Figure 3: Schema of the position of the UWB and WiFi devices.  
than 9 m (as a consequence, UWB4 was left out of the measurements in Figure 4), in contrast with WiFi, which could capture the data without any problems.