esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Concepts, Weakness and Benchmark on Hash Table Data Structures
## 3 Results
### The Maps and Their Software Interfaces  
After writing about 48 programs using the map interfaces in Java, C++ and Python, there are a few comments that are worth a mention from the experience (just personal opinions here). First one, it is very easy to write programs in both Java and Python and well, not so easy in C++, no surprise here, but the interesting point is that it is really easy to translate from one to any of the others, so after some tries, the process gets established as: write in Java, translate to C++ and then to Python.  
Obviously, the main concepts about hash tables and their use are the same in all of them, but it is a bit annoying that none of them name any method like the others, and,well, this is quite a surprise because, in fact, they are the _same_ methods. However, even if methods are the same they are a few important differences in programming in them:  
* There is a curious difference in the hash function results. While C++ returns a size_t value (typically 64 bits sized), Java returns an int (32 bits) and in Python it seems to vary around 32 to 64 bits (using sys.getsizeof). As already mentioned, C++ and Java return the same integer when doing the hash of an integer but Python computes something else.
* Of course, Java and Python have memory management and C++ does not. Therefore, programming is slightly easier, but not always! If needed it is possible to use smart pointers in C++ to provide memory managing and similar behaviors, that is, smart pointers are somehow equivalent to Java or Python references to objects. In fact, the implementations of the _cuckoo hashing_ and _HAMT_ uses smart pointers. On the contrary, there are somethings you could do in C++ and you simply could not do it in any of the others.
* Generic programming is quite different from one to the other. The easiest and the most expressive but with the less boilerplate code is Python (by far) since, in fact, any method or function behaves as a template and in addition to the \"magic methods\" provide a straightforward solution to overload operators. Maybe C++ is more powerful due to the general features of the language, but it is quite verbose and sometimes very difficult to check or debug. Finally, generic programming in Java is less flexible than in the others.
* Finally, there is another curious difference in the interfaces. While Python and Java return the old value when removing an element from the hash table, C++ does not. So when the removed item is going to be used after removal, two operations are needed: retrieve the element and then remove it.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 5 Further Research Needs and Challenges in Optimization of Ship Energy Systems
### Optimization with Consideration of System Reliability  
The word'reliability' is used here with the following meaning [101]:  
_\"Reliability is the probability that an item will perform a required function under stated conditions for a stated period of time.\"_  
Usually, the problem of energy systems optimization is solved assuming that the equipment is always available for operation, except of periods for scheduled maintenance. If redundancy is required, modifications of the system are performed after optimization. However, with such an approach, the final system most probably will be nonoptimal.  
An example of a different approach is given in Ref. [102]. The method is first developed theoretically introducing reliability and availability in the mathematical model of the system using the state-space method, while decisions in case of partial failure of equipment are taken by applying the Intelligent Functional Approach [5; 6]. The method was then applied for SDO optimization of a cogeneration system with and without consideration of reliability. It is interesting to note that introduction of reliability changed the optimal system significantly at all three levels (synthesis, design, and operation) and that if reliability (and consequently the failure probability of equipment) is not considered, then the economic performance of the system, as it is expressed by its net present value, is overestimated.  
A literature search can give also other applications of land energy systems optimization including reliability (examples are given in [2]), but applications on ship energy systems have not been located (a note of caution: the word _reliability_ may appear in many publications, but without real consideration in mathematical terms). It is worth applying these methods on ship energy systems too. The similarity between energy systems on land and on board will facilitate this effort.
# Metasurface-Based Polarimeters
## 3 Metasurface-Based Polarimeters
### Metasurfaces Spectropolarimeters
Based on this SPM, the optical rotatory dispersion (ORD) for the specific rotations of d-glucose (chiral molecule) and its enantiomer l-glucose were measured. The ORD of d-glucose shows good agreement with the values found in the literature and the L-glucose ORD is manifested by the opposite behavior as expected (Figure 6d). Finally, we note that this interleaved center-symmetrical approach can be applied with dielectric metasurfaces for spectropolarimetry operating in the visible wavelength range [80]. Figure 6: Interleaved spectropolarimeter. (**a**) Schematic setup of the spectropolarimeter. The spectropolarimeter metasurface (SPM) is illuminated by continuum light passing through a cuvette with chemical solvent, then four beams of intensities \(I_{\sigma+}\), \(I_{\sigma-}\), \(I_{L45}\), and \(I_{L0}\) are reflected toward a CCD; (**b**) Predicted (red dashed curve) and measured (blue circles) polarization states, depicted on a Poincaré sphere; (**c**) Measured far-field intensities for elliptical polarization at two spectral lines (with wavelengths of 740 and 780 nm) and (inset) the corresponding resolving power (black line) and calculation (blue line) of the 50 \(\upmu\)m diameter SPM; (**d**) Optical rotatory dispersion (ORD) for the specific rotations of d- and L-glucose. Black squares and red circles represent the measured ORD of d- and L-glucose, respectively. The blue line depicts the dispersion acquired from the literature. Reproduced with permission from [79], Copyright American Association for the Advancement of Science, 2016.
## 1 Introduction
Initially proposed to be available to every Thai citizen over the age of 16, the plan was later revised to exclude wealthy individuals.7  
Considering the magnitude of this scheme, a critical question arises: will it contribute meaningfully to the long-term welfare and income of the Thai population as intended? This question is especially pertinent given that the economic sectors directly benefiting from this scheme are narrow, focused primarily on retail, wholesale, and food services. An additional question is whether this scheme is an effective way to reduce income inequality across communities in Thailand. While the scheme aims to stimulate local economies and promote equitable income distribution, its focus on specific sectors may limit its reach and effectiveness in addressing broader income disparities. Understanding the effects of various sectors on people's income and their spatial dependencies is crucial for informing the government about potential policy improvements or necessary adjustments. Therefore, this research is guided by two primary questions: RQ1, \"what are the effects of changes in various economic sectors on average household income?\" and RQ2, \"what are the roles and characteristics of spatial dependencies on the effects of economic sectors and the dynamics of household income?\"  
To answer these questions, a spatial econometric model was employed to test the effects of 19 economic sectors comprising the Gross Provincial Product (GPP) of Thailand's 76 provinces on the average household income in these provinces. The study utilised biannual data from 2005-2021 from the National Statistical Office of Thailand. The results of the study reveal that the sectors of agriculture, real estate, professional services, support services, and leisure showed significant direct associations with household income. Furthermore, the study discovered pronounced spatial autoregression, indicating that the income levels in one province could significantly influence nearby provinces. This finding aligns with existing literature emphasising spatial correlation. The next section of the paper reviews literature relevant to the drivers of household income, followed by sections on data and methods, findings, a discussion, and conclusions. The study aims to provide a comprehensive understanding of the relationship between economic sectors and household income in Thailand, offering insights that could inform future economic policies and strategies.
# Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations
## 5 Numerical Experiments
### Results on Flying-V Warehouse Layout  
#### 5.4.1 The Optimal Layout of Flying-V  
When setting one workstation in the warehouse, it is always located on the intersection of a cross-aisle on the bottom edge. For multiple workstations, the candidates may be located on: (1) the top and bottom edges, (2) the intersection of two angled cross aisles, or (3) the intersection of oblique aisles and left and right edges, depending on the cross-aisle angle. Figure 10 illustrates the optimal workstation locations with different quantities and different warehouse lengths; more can be referenced in Appendix A Figures A2-A4. The workstations seem to disperse differently in the warehouse depending on the length-to-width ratio. It is surprising to see that for three workstations, the optimal positions do not include any aisle intersections. For four workstations, the three cross-aisle intersections are only presented in Figure 9(c). It can be concluded that when angled cross aisles reach the upper corner of the warehouse or close to it, the endpoints of the cross-aisle are probably not set as workstations.  
We also apply the \(2n\) rule and \(n+1\) rule to the flying-V layout to evaluate its effectiveness. To apply the rules, we first fix the cross-aisle intersections with any edges as workstations, and then we locate the remaining workstations using the rules. Tables 7 and 8 show the comparative results obtained by optimization model, \(2n\) rule and \(n+1\) rule, by varying the warehouse length. \(N\) is set as \(16\), and \(\theta\) is \(\frac{1}{2}\pi\). In Tables 9 and 10 the results are derived by changing the cross-aisle angle and fixing \(L\) as \(80\). It seems that the GAP is not as small as that of traditional layout. For example, when the warehouse length is \(30\) (here the length-to-width ratio is roughly \(1.0\)), the GAP for \(n+1\) rule can reach as large as \(15.39\%\). However, as the length (or, the length-to-width ratio) increases, the GAP for both \(2n\) rule and \(n+1\) rule decreases dramatically to an acceptable level. Among them, unlike in traditional layout situations, the \(n+1\) rule seems better than the \(2n\) rule that the GAP is normally less than \(5\%\), except when the angle is as much as \(\frac{13}{36}\pi\).  
Figure 10: Optimal flying-V warehouse layout with different number of rows.
## 2 Models and Methods
### Numerical Setup  
Two parts of simulations were performed in this research. To verify the accuracy of the simulation framework, a simulation of a droplet falling on a sphere particle was carried out according to [PERSON] et al.'s research [26], and the outcome was compared with their results of both experimental and numerical simulation. Then, a packed bed structure was generated by DEM method and factors affecting the liquid distribution were investigated in the complex region. The properties of gas and liquid employed in verify simulations and CFD simulations referred to air and water under standard conditions and are given in Table 1.  
All simulations mentioned in this research were conducted with polyhedral mesher. The reason to choose it is that polyhedral cells are relatively easy and efficient to build, requiring no more surface preparation than the equivalent tetrahedral mesh. Besides, they also contain approximately five times fewer cells than a tetrahedral mesh for a given starting surface. It provides great advantages to the simulation regions, since it contains extremely complex geometry and requires large number of cells to describe the shape of the random packing. It will help us to build cells with high quality and speed up the burdensome simulations. All these simulations were implemented with the commercial software STAR-CCM+ 12.02 [28].
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.6 Replay and Man-in-the-Middle Attacks  
To attempt a replay attack, the attacker may intercept public channel messages \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\). However, these messages contain timestamps \(T_{1}\), \(T_{2}\), \(T_{3}\), and \(T_{4}\) and verification parameters \(Q_{i}\), \(L_{j}\), \(Auth_{i}\), and \(Auth_{j}\), and each entity checks the freshness of the messages. Therefore, the network participants can confirm the secret parameter values. Thus, the proposed protocol resists replay and MITM attacks.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 2 Background and Related Work
### Vision-based Water Level Measurements
Depth perception from single monocular images was regarded as an ill-posed problem [28]. To tackle the shortcomings, deep convolutional networks (ConvNets) [29] have been used in recent years and have yielded great success. A CNN-based method [30] was presented to determine the water level via boundary segmentation. Three different techniques were compared against each other: [PERSON]'s thresholding, dictionary learning and a CNN. The CNN outperformed the other two methods in determining the water level as it had a strong generalization ability and was capable of detecting and learning complex features. Furthermore, [PERSON] et al. [31] proposed a method to estimate water levels using time-lapse images and object-based image analysis (OBIA). The output images were semantically segmented based on a fully convolutional network (FCN) architecture whereby the water region was labelled, followed by an estimation of the water contours and a conversion of the pixel ratios to metric units. The method also incorporated a Laplacian detector to observe sudden changes in water level. Vision-based sensing technology has advanced over the years, especially when discussing RGB-D sensors, as it has become more accessible and even more affordable. [PERSON] et al. [32] used a RealSense D435 depth camera to investigate complex free sur face flows by measuring a hydraulic jump event and compared the results against multiple other sensors. The hydraulic jump surface profile and surface fluctuation amplitudes were measured in an experimental rectangular flume where the water jump height was altered. The RealSense camera measured the aerated flow and the air-water mixture with similar results to the characteristic upper-water-level limit of the air-water mixture. The study found that clear water surfaces remained undetected until water colouring was added, which significantly improved the detection of calm surfaces. Other areas where RealSense depth cameras have been used for is simultaneous localisation and mapping (SLAM), object tracking and object pose estimation. [PERSON] et al. [33] discussed the deployment of a SLAM system on a micro aerial vehicle (MAV) using an R200 camera due to it being lightweight and capable of functioning indoors and outdoors at long range. [PERSON] et al. [34] proposed an analysis procedure to solve object tracking and pose estimation using a single RealSense D400 depth camera. Using image segmentation and an iterative closest point (ICP) algorithm, the model created an ROI and performed pose estimation, respectively. It performed well under robust lighting and background conditions and comparatively better than a single-shot detector (SSD) model.
## 2 Materials and Methods
### Persistence Model  
The persistence model has established itself as a solid basis for the prediction of solar radiation, being widely used in studies related to meteorology [32]. This approach, although simple, has proven useful by assuming that solar energy in the future will be equal to the last measurement obtained. Mathematically, this concept is described directly and understandably [33]. By providing an initial reference for comparison with other more complex models, the persistence model plays an important role in the evaluation and improvement of solar radiation prediction techniques [34]. The equation for this model is as follows:  
\[H(t+1)=H(t) \tag{2}\]  
In Equation (2), \(H(t+1)\) represents the forecasted value; meanwhile, \(H(t)\) represents the previous value.  
The persistence model serves as a foundational baseline for assessing solar radiation forecasting methodologies. This approach provides a straightforward method to establish initial predictions without additional variables or complex computations by capturing basic temporal dependencies in solar radiation patterns.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### Random Forest (RF)  
As an idea of integrated learning, random forest inputs the data obtained by random sampling into many decision trees, performs voting, and obtains the final output result.  
Constructing a random forest requires the following four steps:  
Figure 1: 5-layer decomposition of original signal and denoised signal.  
* A sample with a sample size of N is drawn N times with replacement, and one sample is drawn each time, then N samples can be formed. These N samples are used to train a decision tree as samples of the root node of the decision tree;
* If each sample has M attributes, when each node of the decision tree needs to be split, randomly select m attributes from the M attributes to meet the condition \(m\ll M\). From these attributes, information gain is used to select an attribute as the splitting attribute of the node;
* During the formation of the decision tree, each node is split according to step 2;
* Build a large number of decision trees according to steps 1-3, and then construct the entire random forest.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.4 Collision Avoidance I  
In [62], the authors propose a decentralized multirobot collision avoidance policy called _\"our policy\"_ which considers the interactions between robots and their environment, leading to improved safety and efficiency. The authors highlight that the performance gap between decentralized and centralized approaches is evident in dynamic and complex environments. Traditional decentralized methods struggle to achieve the same efficiency and scalability as centralized methods. Centralized methods face scalability issues and require robust communication networks. Existing decentralized collision avoidance policies have limitations, such as imperfect sensing, reliance on external tools, and sensitivity to parameters.  
The authors in [62] propose a framework that uses the PPO algorithm to train the policy in a large-scale robot system in diverse and complex environments. They introduce a dual stage training methodology for collision avoidance, focusing on improving fundamental techniques. In stage 1, 20 robots learned in randomized environments with absent barriers, enabling them to quickly acquire basic capabilities. In stage 2, 58 robots were trained in complex environments with various obstacles to increase their learning difficulty.  
Figure 4: Multi-ROS modular framework. For more information, please refer to Table 4.  
This training method draws inspiration from the learning paradigm, which accelerates the learning process, produces a more reliable collision avoidance strategy, and offers a promising and resilient decentralized approach despite the trade-off between localized and decentralized attention capabilities. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work has made significant progress in bridging the gap between centralized and decentralized approaches for multirobot collision avoidance. They propose a sensor-level policy that correlates sensor data with actions and has a comprehensive training framework. Future research may add dynamic learning systems that can change based on the complexity of the environment in real time. This will help connect centralized and decentralized systems even more in the area of multirobot collision avoidance.
# Stability Evaluation of a Simplified Reclaimer Using Fluid-Structure Interaction
## 1 Introduction
[15], and [16] evaluated the structural resistance and energy produced by wind turbines using FSI techniques. The effect of the geometry and wind direction was assessed. The modeling of the turbulence played an important role in the results obtained. The effect of fluid properties on the frequencies of cylindrical vibrating rods was assessed by [17]. For a fixed-rod configuration, the authors performed FSI simulations with different fluids to cover a wide range of the properties of the modal parameters. FSI analyses have been widely used to evaluate the stability and the effect of the wind on structures. Unsteady FSI on downwind sails was studied by [18]. The objective of the paper was to predict propulsive force and sail dynamic stability. The stability and nonlinear dynamics of shock absorber vehicle valves were assessed by [19]. The effect of the crosswind on cooling towers was evaluated by [20; 21] using FSI. Rigid and flexible structures were investigated by [22]. The authors performed FSI simulations of turbulent wind gusts hitting the structures. The test case was a rigid and membranous hemisphere impacted by different wind gusts. The FSI analysis could provide exclusive results like the flutter effect and structural vibration modes provoked by fluid forces. Testing the main modal response of a force-based frame structure, [23] proposed a methodology in which the numerical method coupling was carried out by a geometric interface between the fluid and solid domains, using different positions of the solid model in the fluid domain. The work shows the accuracy of this interface method in decreasing the structural mesh elements and obtaining similar results compared with commercial FSI software. Another similar work, [19], evaluated the dynamic stability of vehicle dampers. The fluid-structure interaction was simulated in the oil function for the damper. The study provided an optimized spring and a fast time response of the system. The authors evaluated the main vibration frequencies and examined the influence of each parameter on it. Despite the importance of mining equipment and the influence of crosswind on reclaimers, no work was found in the literature evaluating the stability of this equipment when subjected to wind loads. The stability of the reclaimers can be compromised if the wind reaches high speeds. This paper intends to evaluate the effect of the wind velocity on the stability of a reclaimer and to investigate if the maximum wind speed established by the standards is accurate. The evaluation of the crosswind effects on a reclaimer is performed using a two-way coupling FSI technique developed in the solids4 FOAM code. FSI is an integrated advanced numerical analysis, in which the flow outputs are transferred to structural analysis [24], requiring high computational cost and effort and demanding high computational time. Therefore, a simplified model is proposed to simulate the geometry of a reclaimer, obtaining preliminary results in acceptable computational times.