esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Metaverse Classroom: A Layered Architectural View
### Infrastructure Layer
#### 3.1.3 Hardware Integration  
Hardware integration in a Metaverse classroom ensures that all students can access and participate fully in the virtual environment, regardless of their available technology [92]. This compatibility allows access through various devices such as VR headsets, computers, tablets, and smartphones [93]. While VR headsets provide the most immersive experience, compatibility with desktops and mobile devices ensures flexibility and accessibility for students from different socioeconomic backgrounds [94]. For instance, students without VR headsets can still engage meaningfully using desktops or mobile devices, making learning more inclusive. Furthermore, VR headsets like the Oculus Rift, HTC Vive, orMeta Quest offer a fully immersive 3D experience, enabling students to explore complex concepts through hands-on simulations [95]. For example, a biology class can use VR to perform virtual dissections, allowing students to manipulate 3D models to better understand anatomy [55]. However, the Metaverse classroom should also support lower-end VR devices to avoid reliance on premium equipment [96], ensuring that all students can engage with the content, regardless of the hardware they own.  
Moreover, desktop and laptop compatibility is crucial for students who do not have access to VR headsets [97]. With traditional computer labs and home desktops, students can navigate 3D environments using keyboard and mouse inputs [98]. This setup allows participation in virtual activities such as science experiments, historical site explorations, and collaborative projects [99]. Many schools already have desktop infrastructure, making Metaverse implementation cost-effective and widely accessible [8]. Tablets and smartphones further expand accessibility by allowing students to access the Metaverse classroom through touch interfaces [18]. Tablets provide larger screens, clearer visuals and better engagement than smartphones [100]. Mobile devices are practical alternatives for students without computers or VR headsets, especially in regions where mobile technology is more prevalent [55]. Mobile compatibility enhances the portability of the Metaverse classroom, allowing students to continue learning outside traditional settings, such as during field trips or at home [16]. Finally, cross-platform synchronization is vital in ensuring students can transition smoothly between devices without losing progress or settings [101]. For example, a student might begin a lesson on a desktop at school and continue it on a tablet at home [102]. Cross-platform support ensures a consistent learning experience across devices [103], creating a cohesive and flexible virtual classroom environment. This continuity enables students to engage with content based on their needs and circumstances, making the Metaverse classroom more accessible and inclusive [104].
## 4 Initial Trajectory Generation
### Define Trajectory  
Let \(\Gamma\gets C\subset\mathbb{R}^{d}\) be an initial trajectory, which is parameterized as a function of time where \(d\) denotes the \(C\)'s dimension. Since \(\Gamma\) is a function, the objective of the trajectory generator is to determine the precise objective, which will eventually provide the optimal trajectory in a timely manner satisfying constraints and hypotheses that are imposed. Hence, optimal trajectory, namely \(\Gamma^{*}\), can be posed as a discrete or continuous optimal control problem (OCP) [73]:  
\[\begin{split}\Gamma^{*}=\underset{\mathbf{u}(\cdot)}{\text{min}} & J(\mathbf{x}(0),\mathbf{u}(\cdot))\\ \text{s.t.}&\mathbf{x}(0)=\mathbf{x}_{0},\ \mathbf{x}(t_{n})= \mathbf{x}_{n}\\ &\dot{\mathbf{x}}(t)=\mathbf{f}_{c}(\mathbf{x}(t),\mathbf{u}(t)) \\ &\mathbf{x}(t)\in C_{free},\ \mathbf{u}(t)\in U,\ t\in[t_{0},t_{n}], \end{split} \tag{11}\]  
where \(t_{0}\) and \(t_{n}\) denote the start and terminal time, respectively. Yet another challenging problem is to formulate the objective function, namely \(J\). In the following subsections, we discuss several approaches to address this problem.
###### Abstract  
In the present study, the incremental modal pushover analysis (IMPA), a pushover-based approach already proposed and applied to buildings by the same authors, was revised and proposed for bridges (IMPA\(\beta\)). Pushover analysis considers the effects of higher modes on the structural response. Bridges are structurally very different from multi-story buildings, where multimodal pushover (MPA) has been developed and is currently used. In bridges, consideration for higher modes is often necessary: The responses of some structural elements of the bridge (e.g., piers) influence the overall bridge response. Therefore, the failure of these elements can determine the failure of the whole structure, even if they give a small contribution total base shear. Incremental dynamic analysis (IDA) requires input accelerograms for high intensities, which are rare in the databases, while scaling of generated accelerograms with a simple increment of the scaling acceleration is not appropriate. This fact renders IDA, which is by its nature time-consuming, not straightforward. On the contrary, the change of input spectrum required by IMPA is simple. IMPA\(\beta\) also utilizes a simple complementary method coupled to MPA, to obtain bounds at very high seismic intensities. Finally, the two incremental methods based on static nonlinear and dynamic nonlinear analyses are compared.  
incremental analysis; nonlinear static (pushover) analysis; modal pushover; nonlinear time-history analysis; bridges +
Footnote †: journal: Applied Physics Letters  
_Article_  
IMPA\(\beta\): Incremental Modal Pushover Analysis  
for Bridges
# Design and Accuracy Analysis of a Micromachine Tool with a Co-Planar Driving Mechanism
## 4 Accuracy and Machinability Test  
The prototype machine was designed to have an X-, Y-, and Z-axis travel range of 60 \(\times\) 40 \(\times\) 40 mm, and the machine footprint is less than 60 \(\times\) 60 cm, as shown in Figure 16. A high-speed spindle with a maximum speed of 150,000 rpm was used. Three sets of tilt-servo mechanisms were used, and were installed on the base of the machine table. Since the machine is of the horizontal type, the X- and Z-axis driving mechanisms were responsible for translational motion, and the B-axis mechanism was designed for the rotation motion about the Y-axis. The fourth tilt-servo mechanism was installed on the machine column to translationally drive the spindle in the Y-axis direction. Due to the requirements of high rigidity, a double-column-type mechanism was designed for Y-axis driving unit. The machine table was supported by three supporting points. Ball bearings were installed on the supporting points to provide low-friction rotation freedom for the B-axis. A sliding connecting rod was installed under the bearing, so that the platform can move along the X- and Z-axis slide rails. The driving unit used for each translational axis was composed of a servo motor and a linear guideway module. The ASDA-A2 series servo motor produced by Delta Electronics Inc. with a high-precision encoder was used. The linear guideway module was model KK50 with a 2 mm ballscrew pitch and a 300 mm stroke produced by made by Hiwin Corp.  
Figure 16: Prototype of horizontal micromachine tool with co-planar multi-DOF.  
Figure 15: Ballscrew B rotation error.  
A HP5529 laser instrument was used to check the position accuracy and repeatability of the machine. The measurement of the laser instrument was applied for a long travel range, and the microcapacitance was used to check the accuracy of small movements. The measurements were taken at three locations for each axis. At each location, several small movements with 1-mm intervals were made. According to the calibration results, the positioning accuracy of the machine was less than 0.8 \(\upmu\)m. In addition, a vision measurement system was implemented to check the rotation accuracy. It showed that the machine was able to achieve a rotation accuracy of 0.008\({}^{\circ}\).  
To conform the four-axis machinability of the machine, two micromachining experiments were conducted. In the experiments, a microcutter with a diameter of 0.2 mm, a spindle speed of 150,000 rpm and a feedrate of 300 mm/min was used to cut a copper workpiece. Figure 17 shows the machined micro pattern, micro hole array, and micro characters. To verify the rotation degree of freedom of the B-axis, three micro holes were machined with tilting angles of 0\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\). Figure 18 shows the cutting results. The test results show that the machine was able to provide the expected cutting performance with
## 1 Introduction
### Research Gap, Novelty, and Objectives  
Due to an unanticipated increase in cutting temperature at the cutting interface during the hard turning operation of heat-treated AISI 52100 steels in a dry environment, this causes quick tool wear and degradation of the workpiece's surface quality. To resolve this problem, appropriate cooling and lubrication procedures should be used to lessen the cutting temperature, which will prevent rapid tool wear and power consumption and enhance the product's surface quality.  
MQL (with a single-nozzle arrangement) is one of the most well-known and extensively used cooling technologies for machining hardened steel. A dual-nozzle MQL configuration, a recently developed idea for enhancing the cooling performance of the current MQL, was employed in this investigation. Dual-nozzle MQL has not yet been documented in any literature studies for machining hardened AISI 52100 steel. The key novelty of this research is the employment of ZnO nanofluid with a dual-nozzle MQL setup in hard turning. Additionally, the employment of WASPS in hard turning performance optimization is a new effort in and of itself. Furthermore, very little research has been performed to explain how hard turning affects power consumption and sound levels, thus a thorough analysis is essential for hard turning success. Considering these novelties, the current research implemented ZnO nanoparticle-based green and sustainable cutting fluid through dual-nozzle MQL in turning heat-treated AISI 52100 steel (56 \(\pm\) 1 HRC) using PVD single-layer (AlTiN)-coated carbide inserts. The impact of process variables like cutting speed, feed, depth of cut, the flow rate of lubricant on response parameters like tool wear, surface roughness, cutting temperature, cutting sound, power consumption, and chip morphology has been thoroughly investigated during the experiment. In addition, a novel WASP-based MCDM (multi-criteria decision-making) optimization technique is utilized to obtain the best possible input parameter combination for increasing product cost by enhancing the product's surface finish, decreasing production cost by lowering cutting temperature and tool wear, and achieving noise reduction as well as lowering power consumption during hard turning operation for a sustainable industry application of AISI 52100 steel.
## 4 Design of the Controller
### Design of the Position Controller
Furthermore, the virtual control variable \(\alpha_{2}\) is given by Equation (28):  
\[\alpha_{2}=\frac{1}{c_{1}}(-k_{2}e_{2}-c_{2}(e_{2}+\alpha_{1})-\dot{\theta}_{ i2}-e_{1}-\delta_{i2}+\hat{\alpha}_{1}), \tag{28}\]  
where \(k_{2}\) is the controller gain, \(\hat{\alpha}_{1}\) is obtained using TD and \(\dot{\theta}_{i2}\) is the observed uncertainty. When the Lyapunov function is described as \(V_{2}=V_{1}+\frac{1}{2}e_{2}^{2}\), Equation (29) is obtained and shown as following:  
\[\dot{V}_{2}=-k_{1}e_{1}^{2}-k_{2}e_{2}^{2}+c_{1}e_{2}e_{3}+e_{2}\widetilde{ \theta}_{i2}-e_{2}\widetilde{\dot{\alpha}_{1}}. \tag{29}\]  
As long as \(e_{3}\), \(\widetilde{\theta}_{i2}\), and \(\hat{\alpha}_{1}\) converge to \(0\) and \(k_{2}>0\), it can be guaranteed that \(\dot{V}_{2}<0\). Therefore, step 3 is devised. **Step 3:**  
The second error surface \(e_{3}\) is defined using Equation (30):  
\[e_{3}=x_{i3}-\alpha_{2}. \tag{30}\]  
The derivative of \(e_{3}\) is given by Equation (31):  
\[\dot{e}_{3}=\dot{x}_{3}-\dot{\alpha}_{2}=c_{3}u_{1}+c_{4}(e_{2}+\alpha_{1})+ \theta_{i3}-\dot{\alpha}_{2}. \tag{31}\]  
The control law is obtained using Equation (32):  
\[u_{i}=\frac{1}{c_{3}}(-k_{3}e_{3}-c_{4}(e_{2}+\alpha_{1})-\dot{\theta}_{i3}+ \hat{\alpha}_{2}-c_{1}e_{2}), \tag{32}\]where \(k_{3}\) is the controller gain and \(k_{3}>0\). Furthermore, \(\hat{\vec{\alpha}}_{2}\) is obtained using the TD given in Equation (19). \(\hat{\theta}_{l3}\) is obtained using the NDO (Equation (33)), shown as follows:  
\[\left\{ \begin{array}{l}\hat{\theta}_{l3}=\sigma_{l3}+\eta_{l3}x_{l3}\\ \hat{\sigma}_{l3}=-\eta_{l3}\sigma_{l3}-\eta_{l3}(c_{3}x_{i4}+c_{4}x_{l2}+ \eta_{l3}x_{l3})\end{array} \right., \tag{33}\]  
where \(\eta_{l3}\) is the observer gain (\(\eta_{l3}>0\)), which is related to the convergence speed of the NDO, and \(\sigma_{l3}\) is the internal variable.
# Construction of a Chemical Kinetic Mechanism of Five-Component Surrogate Fuel for RP-3 Kerosene
## 3 Modification of Kinetic Parameters for Mechanism of Surrogate Fuel Mixture
### Modification of Dynamic Parameters
Among the reactions with larger negative sensitivity coefficients, the contributions of R36 and R37 are more prominent. Since both reactions are endothermic processes where HCO radicals generate small-molecule radicals, although they promote the generation of H radicals, they consume a relatively large amount of heat from the system, exerting a certain inhibitory effect on the laminar flame speed [31; 48]. Under three distinct operating conditions, the temperature sensitivity analyses all demonstrated that reactions such as R1 and R36, which involve small molecules like H radicals and OH radicals, have  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Reactions**} & \multirow{2}{*}{**Modification**} & \multicolumn{3}{c}{**Kinetic Parameters**} & \multirow{2}{*}{**Ref. **} \\ \cline{3-4} \cline{6-5}  & & **A** & **b** & **E (J/mol)** \\ \hline R1:H + O\({}_{2}\) = O + OH & Before & \(2.644\times 10^{16}\) & \(-0.671\) & \(1.7041\times 10^{4}\) \\  & After & \(1.04\times 10^{14}\) & 0.0 & \(1.5286\times 10^{4}\) \\ R12: H + O\({}_{2}\)(+  
a more significant influence on the laminar flame speed. This indicates that the laminar flame speed is closely associated with the concentrations of H and OH radicals. Based on the above analysis results, the kinetic parameters of some elementary reactions in the mechanism were modified. Except for R1, the main modified reactions are shown in Table 3.  
Figure 9 presents the comparison between the prediction outcomes before and after the modification of the mechanism kinetic parameters and the experimental data of RP-3 fuel. Regarding the ignition delay time, the optimized mechanism conspicuously reduces the predicted values in the medium- and low-temperature regions and elevates the predicted values in the high-temperature region, significantly enhancing the matching with the experimental values. With respect to the laminar flame speed, the optimization measures likewise lower the predicted values of the mechanism within \(\varphi\) values of 0.7 to 1.5, achieving better alignment with the experimental values. The optimized coupled mechanism encompasses 142 species and 504 elementary reactions (Supplementary Materials Tables S1 and S2) and still maintains a relatively small size. Figure 8: Temperature sensitivity at different equivalent ratios.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 2 Related Works  
The evolution of IoT deployments has raised the need for effective methods of updating device firmware. As a result, innovative solutions for remote firmware upgrades have attracted the attention of many researchers. Current research covers many aspects of the upgrade process, such as the security of performing the process, the importance of remote firmware upgrades, and different aspects of upgrade efficiency [9].  
Ref. [10] presents a model for LoRaWAN that can be used for firmware transfers over low data rate, constrained remote wide area networks (LoRaWAN). The proposed model ensures integrity and availability during firmware upgrades. However, it does not work very well for the transmission of larger firmware.  
Ref. [11] propose a blockchain-based OCF firmware upgrade solution for IoT devices. They introduce two types of firmware upgrade protocols, direct and peer-to-peer upgrades. In the direct scheme, devices can download a new firmware upgrade from a server via an IoT gateway. In the peer-to-peer scheme, devices can query for upgrades from a nearby gateway. The results show that they can complete firmware upgrades in a reasonable amount of time, with peer-to-peer taking less time.  
Secure and reliable firmware upgrade technology for vehicles is also being investigated [12]. It can be utilized to reduce upgrade maintenance costs. In their studies, the firmware is upgraded over Wi-Fi on a vehicle that already has the most recent firmware [13]. There, vehicle owners do not need to return to a dealership for a firmware upgrade, which costs around 150 dollar [14]. The cost of an upgrade utilizing the proposed system depends on the number of users. However, they only proposed a simple TCP triple handshake for secure connections between vehicles. The security is yet to be verified. [PERSON] a wireless upgrade system based on an IEEE 802.11 s mesh network and describes related high-level requirements for such a system [15]. However, its availability is currently insufficient. In addition, its interconnection with other current automotive networks is difficult to achieve.  
Marine engine room simulators can provide a highly realistic experience for seafarer training in the operation and maintenance of marine vessels. As it is a widely used simulation device, we have to consider an efficient and economical way of upgrading the firmware. We focus on the reliability and stability of the upgrade process to ensure the proper functioning of the equipment.
## 2 Materials and Methods
### Biodegradation Kinetics Modeling  
4-n-NP biodegradation plots were fitted to the optimal kinetic model using an Excel file supplied by the FOCUS working group [40] on degradation kinetics and the Solver tool (Microsoft statistical package). The optimization of the parameters was performed by adapting the recommendations of FOCUS to our biodegradation processes and applying the least-squares method. The biodegradation data were fitted to three kinetic models, namely a first-order simple (SFO) model, a first-order biphasic sequential model (hockey stick, HS), and a first-order multi-compartment (FOMC) model, using the equations below:  
\[\mathrm{M_{t}}=\mathrm{M_{0}}\ \mathrm{e^{-kt}}\] (SFO)  
\[\mathrm{Dt_{50}}=\mathrm{ln}\ 2/\mathrm{k}\] (SFO)\[\text{M}_{\text{t}}=\text{M}_{0}\;\text{e}^{-\text{k}\text{l}\text{t}\text{b}}\; \text{e}^{-\text{k}\text{2}(\text{t}\;-\;\text{t}\text{b})}\] (HS) \[\text{DT}_{50}=(\text{ln}\;100/100\;-\;50)/\text{k}_{1}\;(\text{if }\text{DT}_{50}\leq\text{t}_{\text{b}})\] (HS) \[\text{DT}_{50}=\text{t}_{\text{b}}+(\text{ln}\;(100/100\;-\;50)- \text{k}_{1}\text{t}_{\text{b}})/\text{k}_{2}\;(\text{if }\text{DT}_{50}>\text{t}_{\text{b}})\] (HS) \[\text{M}_{\text{t}}=\text{M}_{0}/((t/\beta)+1)^{\alpha}\] (FOMC) \[\text{DT}_{50}=\beta\;(2^{(1/\alpha)}-1)\] (FOMC)  
SFO is a function describing exponential decline from a defined starting value, where Mt and M0 are the remaining concentrations of 4-n-NP (mg kg\({}^{-1}\)) at time t and just after spiking the sample, respectively, whereas k is the degradation rate constant (day\({}^{-1}\)). The HS model describes two exponential decline functions with a breakpoint between them, where k1 and k2 are the rate constants of degradation for the fast and slow degradation fractions, respectively, and t\({}_{\text{b}}\) is the time at which the rate constant changes. In the FOMC model, the function describes an exponential decline from a defined starting value, with a decreasing rate constant; \(\alpha\) is a shape parameter determined by the coefficient of variation in k values, and \(\beta\) is a location parameter. The DT\({}_{50}\) is the time required for the pollutant concentration to decline to half of its initial value. The chi-square (x\({}^{2}\)) variable was used to estimate the appropriateness of the model and to assess the accuracy of each resulting fit.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 2 Materials and Methods
### Three-Dimensional Point Cloud Momentum Contrast Learning Framework
#### 2.1.1 Momentum Contrast Learning Algorithm  
Algorithm 1 provides pseudocode for the momentum contrast learning algorithm. First, the self-attention point cloud capsule networks \(f(\cdot)\) and \(g(\cdot)\) are initialized. A mini patch size point cloud data is loaded, and multiple data augmentation hybrid methods (Section 2.1.2) are used to generate positive sample pairs. The positive samples obtain the features of the input networks \(f(\cdot)\) and \(g(\cdot)\) and compress them (Section 2.1.4), and the gradient back-propagation of the network \(g(\cdot)\) is stopped. Then, the Jaccard contrast loss (Section 2.1.5) is calculated for the feature, the feature is stored in the feature queue, and the contrastive learning is carried out according to the loss. The parameters of network \(f(\cdot)\) are updated by the Adam update method, and the parameters of network \(g(\cdot)\) are updated by the momentum update method (Section 2.1.3). Finally, to update the queue, the current feature is put into the queue and the earliest feature is taken out of the queue.  
Figure 1: Contrastive learning framework.  
```
f, g: Self-attention PointCapsNet \(f(\cdot)\) and \(g(\cdot)\) # queue: dynamic queue # m: momentum # t: temperature f.params = g.params # Initialize for x in loader: # Load a minibatch x with N samples #Using multiple data augment hybrid methods to generate positive sample pairs x_f = MulAug(x) x_g = MulAug(x) # Input to network r_f = forward(x_f) s_f = compress(f_f) # Compressed digital capsule layer r_g = g.forward(x_g) s_g = compress(r_g) r_g = r_g_detach() # No gradient to r_g # Jaccard Contrast Loss J_pos = Jaccard(s_f, s_g) # Calculate Jaccard similarity coefficient # Summation of Jaccard similarity coefficients for negative samples J_neg = 0 for neg in queue: J_neg+ = Jaccard(s_f, neg) loss = \(-1*\)log(J_pos/J_neg) # Adam update: \(f(\cdot)\) loss.backward() update(f.params) # Momentum update : \(g(\cdot)\) g.params = m * g.params + (1 - m) * f.params # Update queue enqueue(queue, s_g) # Enqueue the current queue  dequeue(queue) # Dequeue the earliest minibatch
```  
**Algorithm 1** Pseudocode in Momentum Contrast Learning in a PyTorch-like style