esa-sceva/satcom-chunk-collection huggingface dataset:
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology
### Developed Algorithms  
This section presents the development of a novel algorithm named (BOCEDS-TSHC). It has the core of BOCEDS with an addition algorithm named tempo spatial hyper cube (TSHC). It is developed as an extension that augments the existing BOCEDS (or any similar density-based online stream clustering) to provide high cluster efficiency within evolving  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **BOCEDS-** & \multirow{2}{*}{**BOCEDS**} & **BOCEDS** & \multirow{2}{*}{**CEDAS**} & **MuDi-Stream** \\  & **TSHC** & & & & \\ \hline cellThresh & 5 & - & - & - & - \\ outlierThresh & 2 & - & - & - & - \\ incStep for & & & & & \\ quantization & 0.02 & - & - & - & - \\ \(q_{0}\) & 0.01 & - & - & - & - \\ Decay & 50 & 50 & 50 & 50 & - \\ \(R_{min}\)Ratio & 2 & - & - & - & - \\ \(R_{min}\)Ratio & 0.25 & - & - & - & - \\ CMTresh & 10 & 10 & 10 & - & - \\ \(R_{max}\) & - & 0.1 & 0.1 & - & - \\ \(R_{min}\) & - & 0.05 & 0.05 & - & - \\ Radius & - & - & - & 0.01 & - \\ minThreshold & - & - & - & 3 & - \\ Stream speed & - & - & - & - & 50 \\ \(C_{m}\) & - & - & - & - & 1 \\ \(C_{l}\) & - & - & - & - & 0.5 \\ Lamda & - & - & - & - & 0.998 \\ gridGranularity & - & - & - & - & 10 \\ MinPts & - & - & - & - & 3 \\ Horizon & - & - & - & - & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters Setting.  
Figure 3: Online Clustering Algorithm developed.  
data streams respectively. The developed TSHC algorithm is an add-on to a single-phase clustering algorithm that assists in generating clusters from evolving data streams in a fully online manner. It can work independently or jointly with an existing clustering algorithm such as BOCEDS. Again, using them with the BOCEDS core can be regarded as a new clustering algorithm designated as BOCEDS-TSHC. We present the developed TSHC in the subsequent section.
# Dynamic Response of Non-Yielding Wall Supporting Over-Consolidated Sand
## 1 Introduction  
Retaining walls are important geotechnical structures used in numerous civil engineering projects. Such applications include basement walls, highway bridges, culverts, and temporary soil retention structures used during excavations. Retaining walls may be classified into yielding and non-yielding structures. In a yielding structure, the wall experiences sufficient displacement, and the soil's shear strength is mobilized so that active and passive lateral earth pressures can be achieved. In a non-yielding structure, the wall is restrained against lateral motion at its top and bottom. Thus, it does not displace laterally or rotate about its toe; hence, active and passive earth pressures do not develop. Non-yieldingwalls appear in bridge abutments, tunnels, and basement walls among other applications. Such walls are typically designed assuming at-rest conditions and a triangular lateral earth pressure distribution. However, this assumption is no longer valid when the soil is subjected to compaction-induced stresses.  
Compaction-induced stresses behind non-yielding walls have been thoroughly investigated in previous studies [1; 2; 3; 4; 5; 6]. However, the estimation of design forces becomes more complex when seismic loads are introduced. Since non-yielding walls do not allow active conditions to develop, seismic earth pressures are expected to be larger in non-yielding walls than in yielding walls. In the literature, several solutions have been proposed to estimate the seismic pressure or forces behind non-yielding walls; however, these solutions often yield conflicting results. Moreover, they generally focus on the maximum response of the soil-wall system, although the residual state of the wall is also an important aspect of the system's seismic performance.  
This study builds on the earlier work of [PERSON] [6], which focused on the static behavior of a prototype non-yielding wall supporting over-consolidated sand, by addressing the dynamic performance of the soil-wall system. It also extends the preliminary dynamic study of [PERSON] and [PERSON] [7] on the soil-structure interaction system. The research utilizes a finite difference model to conduct a parametric study on the seismic response of non-yielding walls. The parameters examined are the backfill soil friction angle, backfill compaction level, and wall elastic modulus. The seismic behavior of the considered model wall is discussed in terms of the lateral wall deflection, lateral earth pressure distribution, and lateral earth force magnitude and location. Both maximum and residual values are presented and discussed.
## 2 Methods  
### Design-of-Experiments  
The full-factorial Design-of-Experiments (DoE) [32] method is implemented in order to study the effect of the levels of process parameters or factors necessary to facilitate PVD-based graphene growth on copper (Cu) using amorphous carbon (a-C) as the solid carbon source. There are many factors that can affect the growth of graphene on Cu in this process, namely the annealing temperature, annealing time, a-C and Cu layer thicknesses, gas mixture, gas flow rate and pressure values.  
From our previous experiences with the process, we found that varying some parameters, such as gas mixture, gas pressure, flow rate and Cu thickness, will affect the possibility of graphene growth, and thus, they are held constant to values that can facilitate graphene growth in this work. This is because our focus here is on the values of parameters (levels of factors) that will affect the number of layers (single, few or multi-layered) of graphene and the quality (corresponding to the amount of defects) of graphene growth. Thus, we limit our scope to the study of only three parameters in this work, namely annealing temperature, a-C layer thickness and annealing time.  
The thickness of the a-C layer is further determined by the combination of various parameters in the sputtering process of a-C layer, including the substrate temperature, the argon (Ar) flow rate, base pressure values, RF/DC (Radio-Frequency/Direct Current) power and deposition time. In order to achieve variable a-C layer thickness, deposition time is varied, while the other parameters involved in the sputtering process are held constant.  
Each of the parameters or factors examined have two levels, as shown in Table 1. The factors are named as \"A\", \"B\" and \"C\", and the levels are coded as \"\(-1\)\" or low level and \"\(+1\)\" or high level.
## 5 Analysis of Gain Characteristics in DC-DC Stage with Series-Series (S-S) Compensation Circuit
In the contactless coupler used for a three-phase single-stage AC-DC converter in an IPT-based small wind power generation system, the primary and secondary sides of the coupler have a fixed air gap (10 mm) and rotate during coupling. Therefore, the parameters of the primary and secondary sides of the coupler remain almost unchanged and constant. Therefore, in this paper, based on the contactless coupler parameters shown in Table 1 and resonant capacitors \(\mathrm{C_{r1}}\): 203 nF and \(\mathrm{C_{r2}}\): 167 nF, the switching frequency (\(\mathrm{f_{s}}\)) was set around the second resonant frequency \(\mathrm{f_{r}}\) (80 kHz) to ensure that the gain remains constant (\(\mathrm{G_{V}}=1\)) despite load changes. At a fixed switching frequency (fs: 85 kHz), the link voltage (\(\mathrm{V_{Link}}\)) can be controlled through phase control (\(\mathrm{D_{1}T}\)). As shown in Figure 5, the boost inductor value (60 uH) was set to allow the link voltage (\(\mathrm{V_{Link}}\)) to be controlled up to a maximum range of 600 V to regulate the output voltage of 400 V at rated load (1 kW). Figure 11: Operation characteristics due to the phase shift modulation (\(\mathrm{D_{1}}\)) under load variation: (**a**) link voltage (\(\mathrm{V_{Link}}\)) of the PFC stage, and (**b**) gain of DC-DC stage with S-S compensation circuit. Figure 10: Equivalent circuit of the proposed converter with series–series compensation circuit. The gain characteristics with load changes (500 W\(\sim\)1.5 kW) and phase control (D\({}_{1}\)T) are shown in Figure 11a. Therefore, as shown in Figure 11b, at a fixed switching frequency (f\({}_{\rm S}\)) near the resonant frequency (f\({}_{\rm r}\)), the input-output voltage gain remains constant and is unaffected by changes in the equivalent load resistance (R\({}_{\rm ac}\)). Under these conditions, without feedback through wireless communication, the secondary-side output voltage (V\({}_{\rm o}\)) can be controlled within a certain range solely by sensing the primary-side link voltage (V\({}_{\rm Link}\)) and using phase control (D\({}_{1}\)T). \[\rm G_{v}=\frac{V_{S}}{V_{P}}=\left|\frac{s^{3}R_{ac}C_{r1}C_{r2}Nk\sqrt{L_{1} L_{2}}}{\left(1+s^{2}A\right)\left(1+s^{2}B\right)+sR_{ac}C_{r2}(1+s^{2}C)} \right|,\ k=\frac{L_{m}}{N\sqrt{L_{1}L_{2}}} \tag{26}\]  
where  
\[\rm A=C_{r2}L_{2}(Nk\sqrt{L_{1}L_{2}})\frac{1+s^{2}C_{r2}L_{r2}}{1+s^{2}C_{r1} L_{l1}} \tag{27}\]  
\[\rm B=C_{r1}L_{l1} \tag{28}\]  
\[\rm C=C_{r1}L_{l1} \tag{29}\]
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
### Review of Existing Air Traffic Simulation Environments
EUROCONTROL provides a suite of simulation and validation tools free of charge for its Member States. One such tool is NEST [19], a scenario-based platform available to registered users from ANSPs and the Network Manager. NEST allows users to access and analyze both historical and forecasted traffic data through filtering and visualization functionalities. While it primarily supports the design and capacity planning of European airspace, an enhanced version, R-NEST [20], extends these capabilities to support research and innovation related to future ATM concepts. R-NEST enables network-level simulations and integrates SESAR future solutions, including 4D trajectory management (based on BADA), and the Integrated Advanced Emission Model for environmental sustainability assessments. For example, R-NEST was employed in the SIMBAD [21] project, a SESAR Joint Undertaking initiative that explored how machine learning techniques could reduce the computational load associated with high-level performance analysis in macrosimulations of emerging ATM technologies. Another EUROCONTROL tool, eDEP [22], is a lightweight real-time simulation environment intended for rapid ATM prototyping. It is particularly well suited for small-scale studies, such as human factor assessments, and supports both tower and en-route control scenarios. eDEP also features automated workspaces and can interoperate with its heavier version, ESCAPE [23], which is a more comprehensive simulation platform designed for large-scale research, operational validation, and ATCO training. In addition, BADA [24], developed by EUROCONTROL, is an aircraft performance model widely used in ATM systems. BADA uses a kinetic modeling approach to represent aircraft dynamics across a wide range of types, making it highly suitable for trajectory simulation and prediction. The model consists of a set of ASCII files containingoperational performance parameters, airline procedures, and performance summary tables for over 300 aircraft types. Although access to BADA is license-restricted, it is provided free of charge. Many previously mentioned simulators--AirTrafficSim, BlueSky, DYNAMO, eDEP, and ESCAPE--utilize data from BADA for aircraft performance modeling. Table 1 provides a comparative overview of the simulation environments discussed.
## 5 Energy Efficiency and Sustainability
### Energy Efficiency in the Context of RIS and HAP Integration
#### 5.1.6 Enabling Green Communication Networks  
Integrating RISs into HAPs aligns with the broader objective of developing sustainable and green communication networks [126]. By reducing the energy requirements of HAPs, RISs contribute to lowering the overall carbon footprint of communication infrastructure. This is particularly pertinent as the demand for data and connectivity continues to grow exponentially, necessitating solutions that can scale without proportionally increasing energy consumption.  
Analyzing the energy efficiency of RIS-enhanced HAPs reveals their potential to optimize power consumption and improve overall system performance [127]. The strategic deployment of RIS technology can enhance the coverage and capacity of wireless networks while reducing the energy consumption of BSs [124]. Hence, the energy efficiency of RIS-enhanced HAPs is multifaceted, stemming from the ability to direct and manipulate signals efficiently, extend coverage with minimal energy increase, and reduce interference. The passive nature of RISs contributes significantly to lowering operational energy demands, making it a promising technology for sustainable and efficient high-altitude communication platforms. As technological advancements continue to unlock the potential of RISs, it is expected that HAPs will become even more energy-efficient, paving the way for an era of more sustainable communication and monitoring solutions in the stratosphere.
## 3 Theoretical Analysis
### Analysis of the Flow Resistance  
The vague phrases \"one branch\" and \"the other branch\" are used above because the fundamental flow constraints include not only the pressure generated by the volume changes of the aperture but also the inertial force and the capillary force. A slight volume change in the liquid chamber beneath the dispenser also causes both atomization and ejection directly. In the tapered aperture, the flow resistance in the diffuser element is different from the flow resistance in the nozzle element.  
In a single cycle, the micro-cone hole acts as a nozzle/diffuser element. The difference between the two flow resistances determines the flow rates of \"one branch\" and \"the other branch\" described above and allows one-way flows from the flared (the larger diameter of the micro-cone hole) side to the tapered side (the smaller diameter of the micro-cone hole).  
The atomizer uses AC voltage. Therefore, the angle of the micro-cone hole changes constantly. The flow resistance of the diffuser and nozzle also changes constantly. At a certain moment, \(\xi(t)_{Dd}\) is the diffuser element loss coefficient, and \(\xi(t)_{Dn}\) is the nozzle element loss coefficient.  
Therefore, in one cycle, the average of the diffuser element loss coefficient is:  
Figure 3: Illustrative diagram of the dynamic cone angle.  
\[\overline{\xi_{Dd}}=\frac{1}{T}\int_{0}^{T}\xi(t)_{Dd}dt=\frac{1}{T}\sum_{i=0}^{k} \xi(t)_{Dd}\Delta t_{i} \tag{1}\]  
where \(T\) is the length of one cycle.  
Similarly, the average of the nozzle element loss coefficient is:  
\[\overline{\xi_{Dn}}=\frac{1}{T}\int_{0}^{T}\xi(t)_{Dn}dt=\frac{1}{T}\sum_{i=0} ^{k}\xi(t)_{Dn}\Delta t_{i} \tag{2}\]  
where \(T\) is the length of one cycle.  
Figure 4 shows the empirical curves of the cone angle, the diffuser loss coefficient \(\xi_{Sd}\), and the nozzle loss coefficient \(\xi_{Sn}\)[44]. The flow resistance is also a function of the cone angle on a macroscopic level (only cone angles of more than \(40^{\circ}\) are discussed here). The flow resistance is higher in the diffuser element than in the nozzle element when the cone angle is more than approximately \(30^{\circ}\)[44]. The flow resistance increases as the angle increases.  
[PERSON] [45] compared the results obtained from the numerical calculation for conical flows and those mentioned in reference [46] and found that the flow resistances in the nozzle/diffuser elements at the micro-scale are similar to those at the macro-scale. Therefore, in this study, the flow resistances in the nozzle/diffuser elements at the micro-cone hole can use the empirical curves in Figure 4. From this figure, the diffuser/nozzle loss factors are as follows: \(\xi_{Sn}=0.49\), \(\xi_{Sd}=0.62\).
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 3 Simulation Analysis of Static and Dynamic Characteristics of Bus Body Frame
### Static Structural Mechanics Simulation Results
#### 3.1.4 Emergency Cornering Condition  
Based on Figure 9, under the emergency cornering condition, the maximum stress in the bus body frame is 328.9 MPa, and it occurs at the connection between the third vertical column of the right side frame and the roof frame. The material used for the bus roof frame is Q345, with a yield strength of 345 MPa. Although the maximum stress value is not exceeding the yield strength of this material, it is very close to the limit. This represents a critical condition for the bus body frame and requires a redesign.  
As shown in Figure 10, the maximum displacement in this condition is 13.408 mm, with significant deformation mainly concentrated in the upper part of the right side frame and the front-middle part of the roof frame. As this condition represents a critical scenario during actual operation when the bus makes an emergency right turn, the large deformation is due to significant lateral forces acting on the bus body. The observed deformation aligns with the expected behavior. The overall deformation of the bus does not exceed the expected target, thus meeting the stiffness requirements for design under this condition.  
Figure 8: Displacement contour plot under emergency braking condition.  
Figure 7: Stress contour plot under emergency braking condition.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 4 Research Progress of Flyer Morphology  
### Investigation of Flyer Morphology  
#### 4.1.1 Streak Photography  
A high-speed streak camera can capture the side image of the flyer during flight. The integrity and one-dimensional planarity of the flyer are qualitatively judged by analyzing the obtained image. Due to the problems of real-time auto-focusing and depth of field change, it is difficult to obtain frontal images of the moving flyer.  
As shown in Figure 20, [PERSON] et al. employed a Q-switched Nd: YAG laser to launch flyer plates and evaluated the integrity of the flyers based on threshold timings and distances prior to plasma penetration using streak photography and microscopy of the impacted flyers [81].  
Figure 20: Streak photographs of flyers taken by scanning camera [81].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Flyer Launch Mechanism** & **Pulse Energy Source** & **Parameters Related to the Flyer** & **Flight Environment** \\ \hline Explosive-driven flyer & Charge density and size & Flyer thickness and mass; submillimeter-sized gap and cushion & Barrel diameter \\ \hline Light gas gun & Initial air pressure; air chamber volume; adiabatic index & Projectile mass; virtual mass coefficient & Cross-sectional area of the gas gun barrel; barrel length \\ \hline Electrical exploding foil accelerator & Burst current density; charging voltage; bridge thickness, width and mass; Bridge wing structure & Flyer mass and density & - \\ \hline Magnetically accelerated flyer & Drive current; magnetic field Scale factor; magnetic permeability & Flyer material density; flyer thickness; Plate length & - \\ \hline Laser-driven flyer & Wavelength, pulse width, diameter of the focused spot and power density of the laser & Thickness, density, diameter and preparation process of the flyer; thickness and strength of the substrate & Barrel diameter and length \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence factors of flyer speed mentioned in the literature.
# Development of a Robotic Arm Based Hydrogel Additive Manufacturing System for In-Situ Printing
## 2 Robotic Arm Printing Platform  
### Hardware  
The robotic arm printing platform (as shown in Figure 1) consists of a robotic arm (Dobot version 1.0, Shenzhen Yuejiang Technology Co., Ltd., Shenzhen, China), an Arduino singlechip (UNO R3, Shanghai Langyi Electronics Technology Co., Ltd., Shanghai, China), a leeco micro-valve (INKX0514300A, Lee Company, Westbrook, CT, USA), a light source (UVXON, Shenzhen Youxi Technology Co., Ltd., Shenzhen, China), a relay, a computer and feeding equipments.  
In Figure 1a, the computer is connected to the Dobot controller (Shenzhen Yuejiang Technology Co., Ltd., Shenzhen, China) and Arduino singlechip via the USB interface. Arduino singlechip is connected to the light source controller (Shenzhen Youxi Technology Co., Ltd., Shenzhen, China) and nozzle controller (Lee Company, Westbrook, CT, USA). The relay is used to connect the Arduino singlechip and the light source controller.  
The robotic arm is composed of a base, a rear arm, a fore arm and an end-effector. It has three degrees of freedom and is driven by three stepper motors. The angles of the robotic arm are measured by the sensors SCA1000-D01 (Shenzhen Yuejiang Technology Co., Ltd., Shenzhen, China) mounted on the rear arm and fore arm. The end-effector can move within a circle with a radius of 320 mm and the positioning accuracy is \(\pm 0.1\:\mathrm{mm}\).
# Assessment of the RACPC Performance under Diffuse Radiation for Use in BIPV System
###### Abstract  
In the last four decades there has been a significant increase in solar photovoltaic (PV) capacity, which makes solar one of the most promising renewable energy sources. Following this trend, solar power would become the world's largest source of electricity by 2050. Building Integrated Photovoltaic (BIPV) systems, in which conventional materials can be replaced with PV panels that become an integral part of the building, can be enhanced with concentrating photovoltaic (CPV) systems. In order to increase the cost efficiency of a BIPV system, an optical concentrator can be used to replace expensive PV material with a lower cost option, whilst increasing the electrical output through the concentration of solar power. A concentrator called rotationally asymmetrical compound parabolic concentrator (RACPC) was analysed in this work under diffuse light conditions. Software simulations and experimental work were carried out to determine the optical concentration gain of the concentrator. Results from this work show that, under diffuse light, the RACPC has an optical concentration gain of 2.12. The experimental work showed a value of 2.20, which confirms the results with only a 3.8% difference.  
solar photovoltaic; building integrated photovoltaic systems; rotationally asymmetrical compound parabolic concentrator; diffuse radiation +
Footnote †: journal: Applied Sciences
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Open Source IoT Technology for Device Access
#### 2.1.3 Automatic Access of Devices  
At present, automatic access requirements for devices are mainly fulfilled through the device's auto-discovery mechanism. Open source technologies in this space include mDNS, Bonjour, uPnP, etc. mDNS uses multicast technology to implement the service discovery in the LAN and also supports P2P service discovery, which was released as a draft RFC in 2013. uPnP also uses multicast technology to implement communication between devices. It was developed earlier and is more mature which was released as the ISO/IEC 29341 standard in 2008 [24].