esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Three-dimensional (3D) printing has become a promising alternative to conventional methods in plastic part production, particularly for customized or low-volume applications such as toys. This study compares toy components produced by Fused Deposition Modeling (FDM) using polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) filaments and those produced by traditional injection molding using ABS pellets. Unlike in many previous studies based on standardized test samples, a real toy part was evaluated in terms of compressive strength, dimensional accuracy, surface quality, and cost. Experimental results revealed that ABS parts produced by injection molding exhibited the highest compressive strength (3.93 kN), followed by PLA-FDM (2.97 kN) and ABS-FDM (0.95 kN). Similarly, injection-molded parts showed superior surface smoothness and dimensional accuracy. Cost analysis indicated that injection molding is economically viable only when production exceeds 735 pieces, while FDM becomes more attractive for smaller batches due to its low initial cost. A multi-criteria decision-making analysis using the TOPSIS method was conducted to integrate technical and economic factors. Results showed that injection molding is preferable for mass production, whereas PLA-FDM is more suitable for low-quantity, cost-sensitive scenarios.  
3D printing; FDM--fused deposition modeling; ABS; PLA; compression test; surface roughness; dimensional accuracy; SEM--scanning electron microscopy; toy manufacturing; break-even cost; TOPSIS Article
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions  
In this section, we assess the performance efficiency of RE-AEDG in comparison of other two protocols; U-LVRP and AEDG. LVRP is a protocol designed for terrestrial WSNs. It is chosen becauseits layering layout resembles our schematic in many aspects. We have simulated LVRP in underwater environment (U-LVRP). In the simulations we considered a 500 m \(\times\) 500 m network field. The other selected simulation parameters are depicted in Table 3.  
We chose the following performance matrices.  
* **Network lifetime**: Time in which network is considered to be operational. i.e., from network configuration till the death of last node. It is calculated in seconds.
* **Stability period**: Time (in second) from network establishment till the death of last node.
* **PAR**: It is the ratio of total number of packets reached at the sink to the number of packets being sent by sink in specified interval.
* **Throughput rate**: Throughput is defined as the number of packets successfully reached the water surface sink. It is measured in packets per second.
* **E2E delay**: It is the time taken for a packet to be transmitted across a network from source to destination.
# Numerical Design and Optimisation of Self-Compacting High Early-Strength Cement-Based Mortars
###### Abstract  
The use of SCC in Europe began in the 1990s and was mainly promoted by the precast industry. Precast companies generally prefer high early-strength concrete mixtures to accelerate their production rate, reducing the demoulding time. From a materials science point of view, self-compacting and high early-strength concrete mixes may be challenging because they present contradicting mixture design requirements. For example, a low water/binder ratio (w/b) is key to achieving high early strength. However, it may impact the self-compacting ability, which is very sensitive to Vw/Vp. As such, the mixture design can be complex. The design of the experimental approach is a powerful tool for designing, predicting, and optimising advanced cement-based materials when several constituent materials are employed and multi-performance requirements are targeted. The current work aimed at fitting models to mathematically describe the flow ability, viscosity, and mechanical strength properties of high-performance self-compacting cement-based mortars based on a central composite design. The statistical fitted models revealed that Vs/Vm exhibited the strongest (negative) effect on the slump-flow diameter and T-funnel time. Vw/Vp showed the most significant effect on mechanical strength. Models were then used for mortar optimisation. The proposed optimal mixture represents the best compromise between self-compacting ability--a flow diameter of 250 mm and funnel time equal to 10 s--and compressive strength higher than 50 MPa at 24 h without any special curing treatment.  
self-compacting concrete; high early strength; mixture design; design of experiments; response model +
Footnote †: journal: Applied Sciences
## 3 Simulation
### Simulation on the Impacts of Disturbance on Traffic Flow  
In the previous section, the traffic flow affected by the GPV information in the V2X environment was simulated, and the operating characteristics of the traffic flow were explored. With the simulation, one can obtain that the traffic flow affected by the GPV information could maintain the free-flow state with higher density and operate with higher velocity when it breaks away from the free-flow state. To reveal the operating characteristics of traffic flow affected by the GPV information in the V2X environment, the disturbance was not considered in the previous simulation. However, the disturbance is not rare in the realistic traffic system. For instance, when a vehicle in the system changes its velocity, the headway between the vehicle and its preceding as well as its rear vehicle, a disturbance emerges in the system and will affect the operating of traffic flow. Based on this, the impacts of disturbance on the traffic flow affected by the GPV information in the V2X environment are simulated and explored in this section. Parameters of the simulation scenario are set as: \(p=0.769\), \(L=1000\) m and \(N=50\). When \(t=1000\) s, a disturbance with the scale of 4 m/s is exerted in the traffic flow, and the simulation continues. The FVD model is employed to represent the unconnected environment for comparison. The propagation of disturbance has been discussed in our previous work [33], and the attention is attached to the impacts of the disturbance on traffic flow in this research. The simulation results are as shown in Figures 9 and 10.  
Figure 9: The operation of traffic flow affected by the generalized preceding vehicles (GPV) information in the vehicles to everything (V2X) environment: (**a**) Before exerting the disturbance; (**b**) After exerting the disturbance.  
As shown in Figures 9a and 10a, the traffic flow in the two kinds of environment are all at a free-flow and equilibrium state before exerting the disturbance. Compared with the unconnected environment, the traffic flow affected by the GPV information is operating with the velocity 30.73 km\(\cdot\)h\({}^{-1}\) in the V2X environment. By contrast, the traffic flow is operating with the velocity 28.47 km\(\cdot\)h\({}^{-1}\) in the unconnected environment.  
From Figures 9b and 10b, one can obtain that the time for the traffic flow in the unconnected environment returns to an equilibrium and stable state is 540 s, and that for the traffic flow affected by the GPV information in the V2X environment it is 300 s. The improvement in the time for traffic flow to return to an equilibrium and stable state is up to 44.44%. Besides, the deviation of operation of the traffic flow affected by the GPV information from the state before the exerting of disturbance is slighter than that of the traffic flow in the unconnected environment. The deviation in the V2X environment is 6.38%, which is represented by the velocity deviation, and the improvement on the deviation is up to 25.27%.
# Modulation Decoding Based on K-Means Algorithm for Bit-Patterned Media Recording
## 3 Proposed Modulation Decoding Scheme  
The supervised learning that is used for classification and prediction requires a time-consuming and expensive training process and labeling data models. Additionally, models based on supervised learning should be redesigned or retrained when features of the data are changed. However, unsupervised learning exploits similarities between inputs and finds clusters of similar inputs [22]. Clustering is an unsupervised learning process in which similar data can be identified and assigned to a cluster [23; 24; 25]. It is widely exploited for customer segmentation, anomaly detection, and pattern recognition. Thus, we do not need to feed labeled data into models based on unsupervised learning. The K-means algorithm is capable of clustering unlabeled data in just a few iterations, which allocates \(n\) input data sequences into \(k\) categories. In this study, we use the K-means algorithm for modulation decoding to improve decoding capabilities.
## 4 Experiments and Discussion
### Discussion  
#### 4.6.1 Ablation Experiments and Analysis  
To verify the effectiveness of each module, the following models are established: a reinforcement learning combined prediction model considering multiple factors (T-Q (E_L_LA)), a reinforcement learning combined prediction model with second-level decomposition (D-Q (E_L_LA)), a combined prediction model with second-level decomposition considering multiple factors with equal weights (D-T-E (E_L_LA)), and the prediction effects of the above models are compared with the model in this paper (D-T-Q (E_L_LA)).  
From the error analysis in Figure 11, it can be seen that the decomposition module in the model plays an important role in the prediction of complex traffic flow, which can effectively reduce the unsteadiness of traffic flow. Before and after decomposition, \(E_{RMSE}\) is reduced by 71.39% and \(E_{MAE}\) is reduced by 71.67%; when the interference of external factors on traffic flow is considered, \(E_{RMSE}\) is reduced by 10.61% and \(E_{MAE}\) is reduced by 9.56%, indicating that fully considering external factors can improve. For the model weight assignment, compared with the equal weight assignment, the Q-learning assignment reduces the model error \(E_{RMSE}\) by 3.74% and \(E_{MAE}\) by 3.35%, which improves the prediction accuracy.
## 2 System Model
### Channel Model  
In the optical line of sight path shown in Figure 1a, the channel gain between the \(i\)-th terminal and the \(j\)-th AP is given by [23]:  
\[h_{i,j}=\left\{\begin{array}{l}\frac{(m+1)A}{2\pi d_{i,j}^{2}}\cos^{m}(\phi)T_ {S}(\varphi)g(\varphi)\cos(\varphi)\,0\leq\varphi\leq\varphi_{FOV}\\ 0,\varphi>\varphi_{FOV}\end{array}\right. \tag{1}\]  
where \(m=-\ln 2/(\ln(\cos\phi_{1/2}))\) is the Lambertian emission order; \(\phi_{1/2}\) is the half-intensity radiation angle of APs; \(A\) is the physical area of the receivers of terminals; \(d_{i,j}\) is the distance between the \(i\)-th terminal and the \(j\)-th AP; \(\phi\) is the angle of irradiance of terminals; \(\varphi\) is the angle of incidence of terminals; \(\varphi_{FOV}\) is the field of view (FOV) of terminals; \(T_{S}(\varphi)\) is the gain of the optical filter; and \(g(\varphi)\) denotes the optical concentrator gain, which is formulated as:  
\[g(\varphi)=\left\{\begin{array}{l}\frac{n^{2}}{\sin^{2}\varphi_{FOV}},\quad 0 \leq\varphi\leq\varphi_{FOV}\\ 0,\quad\quad\quad\varphi>\varphi_{FOV}\end{array}\right. \tag{2}\]  
where \(n\) is the refractive index. It is assumed that each terminal is served by at most one AP. If the \(i\)-th terminal is associated with the AP \(j_{a}\), the signal to interference plus noise ratio (SINR) is expressed as follows [24]:  
\[\gamma_{i}=\frac{\left(rP_{l}h_{i,j_{a}}\right)^{2}}{\sum_{j\
eq i_{a}}^{N_{A} }\left(rP_{l}h_{i,j}\right)^{2}+n_{0}B_{i,j_{a}}} \tag{3}\]  
where \(r\) is the responsivity of receivers; \(P_{l}\) is the transmitted optical power of APs; \(n_{0}\) is the power spectral density of noise; \(B_{i,j_{a}}\) is the bandwidth resource that the AP \(j_{a}\) allocates to the \(i\)-th terminal. The LoS link of the \(i\)-th terminal might be interrupted by multiple factors, such as blocking. It is assumed that the blocking event random variable obeys the Bernoulli distribution and the probability mass function is given by [25]:  
\[\left\{\begin{array}{l}P(X_{i}=1)=1-p_{i}\\ P(X_{i}=0)=p_{i}\end{array}\right. \tag{4}\]  
where \(X_{i}\) denotes the blocking event random variable of the \(i\)-th terminal; \(X_{i}=1\) indicates that the channel of the \(i\)-th terminal is not blocked; \(X_{i}=0\) indicates that the channel of the \(i\)-th terminal is blocked; \(p_{i}\) denotes the blocking probability of the \(i\)-th terminal. To reflect the differences among  
Figure 1: The visible light communication ultra-dense networks model: (**a**) the coverage of a single AP and (**b**) the overlapping areas of a local network.  
terminal channels, it is assumed that the random variable \(p_{i}\) obeys the Beta distribution [26]. The achievable data rate of the \(i\)-th terminal is expressed as follows [27]:  
\[C_{i}=(1-p_{i})B_{i,j_{a}}\log_{2}(1+\gamma_{i}) \tag{5}\]  
The required data rate of the \(i\)-th terminal, denoted by \(R_{i}\), obeys the Gamma distribution [28]. The mathematical expectations of terminal's blocking probability and required data rate are denoted by \(\overline{p}\) and \(\overline{R}\), respectively.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Security and Privacy
#### 4.5.4 Firewall  
A firewall can be recognized as a type of protection system that keeps track of and manages traffic from and to the network in accordance with established security guidelines. A program that enforces rules to limit or permit network traffic can be used as a firewall.  
Blockchain security is provided by a program called ChainGuard, which filters traffic to ensure that the source of the traffic is authentic. In ChainGuard, non-legal traffic is caught by a firewall in an intelligent KDN IoT transport network, which can thereby prevent flooding attacks and offer restricting features using blockchain for authenticating nodes, while a fuzzy neural network is leveraged to diagnose malicious content, allowing traffic filtering [339]. Moreover, another blockchain-based framework (FL-FW) predicts traffic flow using federated learning, where blockchain is utilized for secure rule sharing and validation for distributed network monitoring, while blockchain consensus is realized with the help of pre-known strategies to filter malicious traffic, acting as a firewall [340]. Alternatively, fuzzy logic and blockchain have been leveraged to filter fake and anomalous data using automobile rules and behaviors in Intelligent Vehicular Networks (IVNs), acting as a firewall [341].
## 5 Results and Discussion
### Condensation and Re-Vaporization Sub-Models
#### Condensation:
To investigate if the condensation is adequately modeled, the result of the case with condensation-only is compared in Table 8 with the counterpart of a base case (referring to the reaction described in above) without including condensation and recycling of the unreacted char. The comparison shows that incorporating the volatiles condensation sub-model results in a formation of about 6.47% condensed liquid volatiles and an increase of about 135 K in exit temperature, due to the release of latent heat. The above comparison indicates that the proposed sub-model successfully captures the phenomenon of the volatiles' condensation. Moreover, the result of the subsequent implementation of the re-vaporization sub-model is also shown in Table 8. The liquid volatiles from previous condensation are reduced to zero due to re-vaporization mechanism. The exit temperature also drops to the level closer to the base case (without char recycle and both sub-models), due to energy supply for the evaporation latent heat. When both the condensation and re-vaporization sub-models are included, the result returns to the status similar to that of the base case (without char recycle and both sub-models). These two sub-models are considered to be successfully developed and implemented. Both sub-models can also be improved to incorporate more complex physics or be calibrated and fine-tuned to benchmark experimental data, when the data become available in the future.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 3 Proposed Methodology
### Overview  
Our proposed methodology comprises 4 steps for 1D holograms and an additional step when processing 1D slices of 2D holograms. First, considering a 1D hologram obtained from a 2D scene, the hologram phase space representation is computed using the _short-term Fourier transform_ (STFT), producing for each color channel a 2D matrix of size \(N\times M\) corresponding to the axis \((x,\xi)\). This matrix represents the decomposition of the hologram as local frequencies \(\xi\) around the specified position \(x\). Since frequencies are related to light propagation through the grating equation, each couple \((x,\xi)\) represents a light beam passing through \(x\) and oriented according to \(\xi\). The phase space transform is a compact representation of the hologram and contains a sufficient amount of information to fully describe it.  
Second, a neural network is used to estimate the support of the hologram phase space representation, resulting in a set of binary masks. The phase space segmentation makes it possible to consider only the shape of the representation created by the scene in phase space without taking into account the values of the transform, which are subject to variation from one hologram to another. Furthermore, by using this segmentation step, a unique thresholding value that is not affected by the color intensity or phase value of the input hologram can be defined.  
Third, the predicted masks are used to detect regions of interest containing the scene objects. The probability for each 2D position to belong to the scene is estimated based on the binary masks, and points exceeding a given threshold define the region of interest.  
Finally, the initial regions of interest are refined using an iterative method to reduce their depth extent and obtain a set of straight lines that represent the final depth estimate of the scene.  
In the case of 1D slices of the 2D hologram, a fixed-size ROI centered on the estimated straight lines is defined and used with the DFF method to further recover the curvature of the extracted lines.  
Figure 1: An illustration of the different components of the proposed method. First, the phase space representation of the input hologram is computed. Then, a neural network is used to predict phase space masks, which will be back-projected to extract regions of interest. Finally, the proposed refinement algorithm is used to extract the final depth estimate.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 4 High-Speed Train Noise Control Methods
### Acoustic Metamaterials Applied to High-Speed Train Noise Control
#### 4.2.1 Definition and Development History of Acoustic Metamaterials  
The term \"metamaterial\" is commonly used to describe artificial composites consisting of periodic or randomly arranged artificial subwavelength structures, a concept first introduced by [PERSON] in 1968 in the field of electromagnetism [92]. The emergence of electromagnetic metamaterials sharpens researchers' understanding of metamaterial theory, and the concept of metamaterials was also introduced in the fields of optics, mechanics, and heat transfer. The concept of acoustic metamaterials can be traced back to [PERSON] [93], who first discovered that periodic structures could be used to control high-frequency phonon propagation but did not refer to such periodic structured materials as acoustic metamaterials. In 2000, [PERSON] et al. [94] designed a small ball made of high-density lead wrapped in rubber and then proposed the theory of locally resonant phonon crystals using this model, opening the door to the study of acoustic metamaterials. After this, acoustic metamaterials have been further developed by artificially designing microstructures to allow materials to present limitations beyond their original natural laws. This can realize a series of idiosyncratic material functions, including acoustic stealth [95], acoustic directional transmission [96], acoustic negative refraction [97], acoustic focusing [98], low-frequency sound absorption [99], and so on. At present, acoustic metamaterials have been applied to solve engineering problems such as aircraft cabin noise reduction, automotive NVH, and building facades and have achieved better vibration and noise suppression effects [100].  
Metamaterials often have different properties from those of traditional materials. For natural materials, material parameters such as mass density, Young's modulus, and Poisson's ratio are positive in the natural case, while for artificial structures, effective material parameters may become negative within a specific frequency range. Some mechanical metamaterials have been applied in the field of energy-absorbing structures and body structures of high-speed trains [101; 102], and the application of metamaterials to reduce railroad noise will be one of the important means of dealing with the noise problem associated with future high-speed trains.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 2 Satellites Sensors and Data  
### Brightness Temperature Datasets  
#### 2.1.1 Gmi  
The GPM Core Observatory, jointly developed by the Japan Aerospace Exploration Agency (JAXA) and National Aeronautics and Space Administration (NASA), launched on 27 February 2014. The design of this satellite is an extension of TRMM and it carries the first Ku/Ka-band dual-frequency precipitation radar (DPR) along with multi-channel GMI. It is deployed in a non-sun-synchronous orbit at a 65\({}^{\circ}\) inclination and a mean altitude of 407 km, giving it the effective latitudinal range of 68\({}^{\circ}\)N-68\({}^{\circ}\)S and swath width of 885 km. GMI is a conically scanning PMW radiometer onboard the GPM Core Observatory, which operates at thirteen microwave channels ranging in frequency from 10.65 to 183 \(\pm\) 7 GHz. The spatial resolution of GMI on the Earth's surface ranging from 25 km at 10 GHz to 6 km at 183 GHz. It views the Earth at an average incidence angle of 52.8\({}^{\circ}\) for frequencies from 10.65 to 89.0 GHz, and at 49.1\({}^{\circ}\) for higher frequency channels [23; 24]. The GMI TB dataset used in this study is the Level 1C version 05 product, obtained from the NASA website at [[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)) (accessed on 12 January 2021). Level 1C generates common calibrated PMW TB products for the GPM Core and constellation satellites using GMI as the reference standard [20; 21]. The intercalibration process involves two major steps: (a) prescreening the sensor TB to identify and correct for calibration biases across the scan or along the orbit path, and (b) to adjust the calibrations of the constellation passive microwave radiometers to be consistent with GMI [20].