esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 V2H and V2G  
### V2H and V2G Technologies  
The V2H system allows the use of electricity already stored in the EV to power the household, whereas the V2G system supports selling the electricity already stored in the EVs to the grid and purchasing electricity from the grid, charging the EV [60]. The V2H and V2G systems mainly consist of battery management software, hardware that supports bidirectional electricity transmission, and communication modules between the vehicle terminal and the grid operator [61]. Intelligent algorithms monitor the operating status of the grid in real-time to determine whether a vehicle can purchase electricity from or sell electricity to the grid at the moment and whether a vehicle is available to reach the corresponding agreement.
## 2 Convective Heat Transfer Coefficients
### Heat Transfer on the Combustor Annular Passage  
The heat load to estimate the temperature distribution on the combustor is computed according to [PERSON] (1986). Radiation and convection from the combustion gases are in equilibrium with the radiation to the engine casing and convection to the air flow on the annular passage between combustor wall and casing. The radiation takes into account the combustion gases temperature and the gas emissivity. The convection on the annular passage inside the combustor and on the combustor/casing annular passage is computed according to the correlation [PERSON] (1986):  
\[h_{an}=0.02\bigg{(}\frac{k_{air}Nu}{D_{an}^{0.2}}\bigg{)}\bigg{(}\frac{\dot{m} _{air}}{A_{an}\mu_{air}}\bigg{)}^{0.8}, \tag{25}\]  
\[D_{an}=2H_{an}\text{ and }A_{an}=\frac{\pi}{4}\big{(}D_{ex}^{2}-D_{in}^{2} \big{)}, \tag{26}\]where, \(h_{an}\) is the convection heat transfer coefficient, \(k_{air}\) and \(\mu_{air}\) are the conductivity and viscosity of the air, \(D_{an}\) is the hydraulic diameter of the annular passage, \(\dot{m}_{air}\) is the air mass flow rate and \(A_{an}\) is the area of the annular passage. \(D_{ex}\) and \(D_{in}\) are the external and internal annular passage diameters. \(H_{an}\) is the annulus height.
## 3 Methods
### Estimation of Drosophila Orientation  
Accurate estimation of the orientation of drosophilas is a critical component of our tracking method. This estimation is particularly challenging due to the small size of the targets and their rapid, erratic movements. Our approach leverages the capabilities of stereoscopic camera views to construct a reliable estimate of each drosophila's orientation in the three-dimensional space.  
In each camera view, we model the body of the drosophila as a 2D ellipse. This ellipse fitting is achieved through image segmentation techniques, where the major and minor axes of the ellipse provide a basis for estimating the orientation in each view. The orientation of the ellipse is defined by the angle \(\theta\) of the major axis for a fixed axis in the image plane. An estimated 2D orientation of a drosophila object is shown in Figure 4.  
Figure 3: This figure shows a series of drosophila objects detected by our method.  
The orientations estimated from each camera view are combined to infer the 3D orientation of the drosophila. This process involves using the known geometrical configuration of the cameras and applying principles of epipolar geometry. The 3D orientation is represented by a vector \(\overset{\rightarrow}{o}\) in the 3D space, which is derived from the 2D orientation angles \(\theta_{1}\) and \(\theta_{2}\) obtained from the two camera views.  
The 3D orientation vector \(\overset{\rightarrow}{o}\) is calculated using the following Formula (4):  
\[\overset{\rightarrow}{o}=R\cdot\begin{pmatrix}\cos(\theta_{1})\\ \sin(\theta_{1})\\ \cos(\theta_{2})\end{pmatrix} \tag{4}\]  
where \(R\) is the rotation matrix derived from the camera calibration process [16].  
The estimated 3D orientation of each drosophila is incorporated into the state representation of each particle in the particle filter. This orientation information is crucial as it significantly influences the movement dynamics of the drosophilas, which in turn affects the prediction and update steps of the particle filter.  
By accurately estimating the 3D orientation of the drosophilas, our method can more precisely predict their future positions and movements. This accuracy is particularly beneficial in complex scenarios involving rapid changes in direction, occlusions, and interactions among multiple drosophilas.
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
## 1 Introduction
Once we understand the requirements of the proposed algorithms, we simulate a complete U-WSN on a prototypical scenario and study the influence of different parameters. In particular, we consider alternative MAC protocols (unicast with ACK and synchronous and asynchronous broadcast) for node-to-node communications (as required for the proposed localization algorithms) and a standard routing protocol for external monitoring. The network simulations are realistic, in the sense that they include multiple effects that affect the overall performance of the network, including propagation impairments (e.g., noise, interference), radio parameters (e.g., modulation scheme, bandwidth, transmit power) and hardware limitations (e.g., clock drift, transmission buffer). We perform the network simulations in Castalia [13]. Castalia is a simulator for wireless sensor networks based on the OMNeT++ platform [14] that offers realistic wireless channel and radio models and realistic node behavior. The main reasons for choosing Castalia are its level of realism, speed and flexibility. The speed is achieved because all of the modules are written in C++. The flexibility is at the cost of having no graphical user interface. Indeed, Castalia is a command line simulator, where scenarios and settings are defined through external configuration files. The results are also given in text files, but they can be accessed with convenient parser scripts. The paper is structured as follows. In Section 2, we propose a parametric model of the underwater channel characterized in Part I of this paper [9], so that it can be efficiently simulated. In Section 3, we introduce two algorithms for localization applications and discuss the communication requirements for the network in these scenarios. In particular, in Section 3.1, a self-positioning algorithm is proposed; while Section 3.2 presents a target localization method for navigation aid. In Section 4, we describe the simulations of a complete U-WSN and obtain some guidelines on the communication schemes for the deployment of a network that addresses the proposed applications.
## 3 Results and Discussion
### Source of As
#### 3.3.1 Correlation Analysis and Cluster Analysis  
A correlation analysis of elements offers insights into their origins and migration pathways. Strong positive correlations indicate similar sources [15; 44]. Following [PERSON]'s correlation grading [45], strong correlations were observed among Cd, Cr, Ni, Mo, and Cu (Figure 5a, r > 0.71, \(p\leq 0.05\)), suggesting a common origin. They may have the exact same origin. Pb and As also showed a significant positive correlation (Figure 5a, r = 0.97, \(p\leq 0.05\)). Cluster analysis further classifies trace elements based on their similarity in concentration and assesses the correlation between elements by constructing a tree diagram to help determine the source of the elements [37; 38]. The Pearson clustering method clusters the elements; the results are shown in Figure 3b. The distance indicates the degree of correlation between elements. The lower the value on the distance cluster, the more significant the correlation. According to the cluster analysis diagram, the seven elements can be classified into two categories (Figure 5b) Pb and As forming a distinct cluster at a distance of approximately 0.02; and Cd, Cr, Ni, Mo and Cu forming a distinct cluster at a distance of approximately 0.25. Most importantly, As and Pb may have a common source. Meanwhile, many studies have reported that IAREEMs may be a significant source of Pb contamination in the surrounding areas [4; 11]. Based on previous findings, As is predominantly distributed around mines containing heavy rare earth elements in Longnan county (Figure 2), suggesting that these mines may be the principal source of As in atmospheric deposition in the area.  
Figure 5: (a) Correlation for the concentration of PTEs in atmospheric deposition (\(n=25\)). \(\times\) indicates no significant correlation. (b) Cluster for the concentration of PTEs in atmospheric deposition (\(n=25\)). Distance reflects the degree of correlation between different elements.
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 3 Alternative Processes for Increased H\({}_{2}\)/CO Ratio
### Steam Reforming of Methane (SRM)
[119] investigated the use of a Pd composite membrane reactor packed with Ru/Al\({}_{2}\)O\({}_{3}\) catalyst for CH\({}_{4}\) reforming at mild conditions (T = 500 \({}^{\circ}\)C, H\({}_{2}\)O:CH\({}_{4}\) = 3). Despite the low operating temperature, CH\({}_{4}\) conversion reached 79.5% when high transmembrane pressure difference (5 bar) was applied to the system. Moreover, the purity of the H\({}_{2}\) permeate stream remained above 97% on 145 h long tests and accounted for 99% hydrogen recovery. [PERSON] et al. [120] reported using Ni-based hollow fiber membrane catalysts for SRM and reaching 99% CH\({}_{4}\) conversion at 800 \({}^{\circ}\)C with hydrogen purity above 95%. In both cases, the authors stated that the transmembrane pressure difference proved to be a very important parameter influencing CH\({}_{4}\) conversion for promoting solution and diffusion mechanisms of H\({}_{2}\) through the membrane structure.
###### Abstract  
The development of novel methods for industrial production of metal-matrix composites with improved properties is extremely important. An aluminum matrix reinforced by \"in situ\" \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) nanoparticles was fabricated via direct chemical reaction between molten aluminum and rutile TiO\({}_{2}\) nanopowder under the layer of molten salts at 700-800 \({}^{\circ}\)C in air atmosphere. Morphology, size, and distribution of the in situ particles, as well as the microstructure and mechanical properties of the composites were investigated by XRD, SEM, Raman spectra, and hardness and tensile tests. Synthesized aluminum-alumina composites with Al\({}_{2}\)O\({}_{3}\) concentration up to 19 wt.% had a characteristic metallic luster, their surfaces were smooth without any cracks and porosity. The obtained results indicate that the \"in situ\" particles were mainly cube-shaped on the nanometer scale and uniform matrix distribution. The concentration of Al\({}_{2}\)O\({}_{3}\) nanoparticles depended on the exposure time and initial precursor concentration, rather than on the synthesis temperature. The influence of the structure of the studied materials on their ultimate strength, yield strength, and plasticity under static loads was established. It is shown that under static uniaxial tension, the cast aluminum composites containing aluminum oxide nanoparticles demonstrated significantly increased tensile strength, yield strength, and ductility. The microhardness and tensile strength of the composite material were by 20-30% higher than those of the metallic aluminum. The related elongation increased three times after the addition of nano-\(\alpha\) Al\({}_{2}\)O\({}_{3}\) into the aluminum matrix. Composite materials of the Al-Al\({}_{2}\)O\({}_{3}\) system could be easily rolled into thin and ductile foils and wires. They could be re-melted for the repeated application.  
metal-matrix composite; aluminum; nano-alumina; molten salt synthesis; mechanical properties +
Footnote †: journal: Applied Sciences
## 2 Measurement Approach
### Wedge Detection Algorithm
#### 2.3.1 Wedge Template  
Template matching uses a template whose shape is similar to the desired feature that is to be found in the image. Therefore, the template has a triangular shape with the three parameters height \(h^{\prime}\), width \(w^{\prime}\) and angle \(\alpha^{\prime}\)--see Figure 3. The angle \(\alpha^{\prime}\) is used to set the position of the template's tip \((x_{\text{tip}},y_{\text{tip}})\).  
Figure 2: Simulated thermogram (**a**) and normalized gradient (**b**) for two chord-wise positions. The blue line intersects a turbulence wedge, which results in less pronounced maxima of the gradient below the threshold of \(0.1\,\text{pixel}^{-1}\), marked with a dashed gray line. (**a**) Simulated thermogram with one turbulence wedge. The trailing edge is labeled with a green dashed line, the leading edge with a blue dash-dotted line. The natural transition is marked with a red dashed line, excluding premature transitions. The two vertical lines mark the positions for the intensity gradients, see (**b**). (**b**) Intensity gradients normalized by their respective maximums for the two lines at \(x_{\text{orange}}=20\,\text{pixel (**top**)}\) and \(x_{\text{blue}}=38\,\text{pixel (**bottom**)}\). Local maxima are marked with dots and correspond to the trailing edge (TE), the natural transition and the leading edge (LE).  
In the coordinate system of the template, the tip's \(x\)-position can be calculated by using the equation  
\[x_{\text{tip}}=h^{\prime}\cdot\cos(90-\alpha^{\prime}), \tag{2}\]  
where the \(y\)-position of the tip equals the wedge height, i.e.,  
\[y_{\text{tip}}=h^{\prime}. \tag{3}\]  
Note that the width of the wedge at the base is not changed when the position of the tip is adjusted to tailor the template to the turbulence wedge in the thermogram. Currently, the adjustment of the angle \(\alpha^{\prime}\) is carried out manually, because \(\alpha^{\prime}\) can be kept constant for all turbulence wedges in the studied thermographic image.  
Each template pixel value \(v\) inside the wedge was set to \(v_{\text{wedge}}=1\), while the surrounding parts were set to \(-1\)--see Figure 3. The objective of doing so is to penalize all image pixels that do not fit the wedge shape. Note that the height and the width values of the template are positive integer values, because a subpixel interpolation has not been considered yet. The described template is subsequently utilized in the wedge detection algorithm to determine the positions of the turbulence wedges as well as the areas of the turbulence wedges.
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
###### Abstract  
A compact circularly polarized non-resonant slotted waveguide antenna array is proposed with the aim of achieving wide-angle scanning, circular polarization, and low side-lobe levels. The designed antenna demonstrates a scanning range of +11\({}^{\circ}\) to +13\({}^{\circ}\) in the frequency domain and a beam scanning range of \(-45^{\circ}\) to +45\({}^{\circ}\) in the phase domain. This design exhibits significant advantages for low-cost two-dimensional electronic scanning circularly polarized arrays. It employs a compact element that reduces the aperture area by 50% compared to traditional circular polarization cavities. Additionally, the staggered array method is employed to achieve an element spacing of 0.57\(\lambda\) within the azimuth plane. Isolation gaps were introduced into the array to enhance the circular polarization performance of non-resonant arrays. The Taylor synthesis method was employed to reduce the side-lobe levels. A prototype was designed, fabricated, and measured. The results indicate superior radiation efficiency, favorable VSWR levels, and an axis ratio maintenance below 3 dB across the scanning range. The proposed antenna and methodology effectively broaden the beam scanning angle of circularly polarized slotted waveguide array antennas.  
slotted waveguide array; circular polarization; wide-angle scanning; non-resonant array; phased array antenna; radar sensor +
Footnote †: journal: Sensors  
0000-0002-4243-0800][PERSON]  
0000-0002-4243-0800][PERSON]
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Traffic Characteristics  
The traffic traces were collected in Ireland by a large telecommunication operator. They include static and mobility scenarios and total roughly 21 h. We divided them into four sets: 5G, 4G, static and mobile, as listed in Table 1. A more detailed description of the traffic generation, capture methodology and properties of the traces can be found in [30] for 4G and in [31] for the 5G traffic.  
Table 1 shows that the average rates of the 5G traces are about 50% higher than 4G traces for the static scenario. Furthermore, the 5G traces achieve nearly three times higher variation rates, reaching about 25 MB/s, compared to 4G traces, which obtain 9.6 MB/s. In the mobile scenario, the differences between 5G and 4G are lower--the average throughput of 5G traces is only 27% higher than the 4G traces. The authors of [30] explain these differences by the lack of 5G base stations across the driving routes where the traffic was  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Trace** & **Activity** & **Length** & **Average** & **Var. Range** & **Source** \\  & & **(min)** & **(MB/s)** & **(MB/s)** & **(MB/s)** & **Source** \\ \hline S4G & Static 4G & 254 & 5.2 & 2.6-9.6 & [30] \\ M4G & Mobile 4G & 290 & 2.7 & 0.3-6.1 & [30] \\ S5G & Static 5G & 260 & 8.3 & 2.7-25 & [31] \\ M5G & Mobile 5G & 459 & 3.5 & 0.4-11 & [31] \\ \hline \end{tabular}
\end{table}
Table 1: Network capacity traces used in the experiments.  
Figure 5: Bidirectional architecture of an LSTM network. BdLSTMs apply both forward and backward dependencies.  
captured. Despite this restriction, the upper limit for the variation range is still almost two times higher for 5G than 4G.  
We use \(r_{i}\) (1) to denote an average amount of traffic registered within a specified time. The length of a video segment is \(L\), as stated in Section 3.1. A video player downloads segments at the rate \(q_{i}\approx\tilde{q_{i}}\); hence, the player measures network throughput roughly every \(L\) seconds. In our model, we assumed \(L=4\) s.
## 1 Introduction
The most common approach to solving classification problems involving more than two classes, namely multiclass problems, involves decomposing the problem into several binary subproblems. Simultaneously predicting multiple classes can be accomplished by disposing of the binary classifiers in a tree-structured hierarchical way, referring to that general discrimination is performed first and becomes successively refined until the obtainment of the final classification. In general, the introduction of a hierarchy in a multiclass application may reduce the complexity involved in its solution [22]. For example, for the five-class classification problem in this study, the directed binary trees only need to train four predictors; this is the lowest number among the main decomposition strategies such as five predictors of one-versus-all and 10 predictors of one-versus-one. In addition, the nodes from the deepest levels involve less training data for the corresponding binary classifiers each time. Another important reason why it is preferred to use the decomposition strategy to deal with multiclass problems, even when they can also be directly handled by a single multiclass classifier, is that the former one is more robust and offers better behavior when significant differences are not always found [23]. The aims of this study were two-fold. One aim was to evaluate the potential of SK-NIRS analysis for identifying and interpreting the distinctions in cucumber (_Cucunis sativus_ L.) seeds harvested at five different periods combined with the analytical results of relative chemical concentration; the other was to propose a hierarchical classification strategy to predict these five maturation stages simultaneously. In the proposed hierarchical model, a set of sequential subclassifiers was generated using the chemometrics methods of the SIMCA and PLS-DA. Together with the exploratory data analysis such as PCA, this hierarchical multiclass classification scheme was able to provide both an intuitive classification process and precise predictive results. In order to better evaluate the effectiveness of this hierarchical classification strategy, a direct SIMCA and PLS-DA model for multiclass classification had been also developed on their own to compare the predictive performances based on a set of classification metrics.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 2 Process of Chemical Looping Gasification of Biomass
### Process Classification
#### System Complexity  
Biomass owns intrinsic characteristics, such as high moisture content, low energy density, various geometrical shapes, ash content, etc. Furthermore, different types of biomass have different properties [47,48]. The bulk density of lignocellulosic biomass materials is relatively low (80-150 kg/m\({}^{3}\) for grass biomass and 160-220 kg/m\({}^{3}\) for woody biomass), which can cause low efficiency in transportation and storage. Therefore, pre-treatment processes are required to homogenize its properties and meet the requirements of chemical looping gasification. These processes possibly include drying, densification, or grinding, etc. These processes require energy and cost resulting in reducing process efficiency.  
The BCLG generally operates at high temperatures (over 800 \({}^{\circ}\)C) for the production of high calorific value syngas. The process requires a large heat transfer area in a gasifier for a sufficient heat supply for biomass gasification, which increases the operating complexity of the system.  
During biomass gasification, biomass ash is produced, which would result in reducing the performance of oxygen carriers. The removal of looping materials from biomass ash (including unreacted carbon) is a considerable challenge to minimize the loss of the looping materials. Additionally, interactions between the ash and looping materials may contribute to a reduction in separation efficiency [9]. Moreover, the syngas produced from the gasification system also contains unwanted substances such as dust, fly ash, tar, alkali metals, nitrogen compounds, sulfur compounds, chlorine, and trace elements. Thus, the CLG system should be integrated with the syngas cleaning system to remove these impurities from syngas before further applications. The required level of syngas cleaning significantly depends on the end-use technology and/or emission standards [49]. There are several methods to remove contaminants which are conveniently categorized according to the process temperature range as hot gas cleanup (HGC) (>300 \({}^{\circ}\)C), cold gas cleanup (CGC) (<100 \({}^{\circ}\)C), and warm gas cleanup (WGC). The majority of these methods are developed based on using wet scrubbers [49,50].  
Typically, biomass gasification system efficiency for power generation would increase corresponding to an increase in unit capacity. However, most of the power plants from biomass are small-scaled and decentralized due to difficulty in the biomass supply chain [51].  
The stable load on the chemical plant and long-term operation are among the challenges. Load variations or frequent start-ups and shut-downs could result in damage to the equipment and a reduction in productivity. Moreover, it could cause an increase in cost due to excessive fuel consumption during the start-up and shut-down.