esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis Results and Discussion of Coupled Multi-Body Dynamics
### Influence of Braking Parameters on the Vibration Characteristics of the Brake Disc
#### 4.3.2 Influence of the Change of Braking Speed on the Vibration Characteristics of the Brake Disc  
To further study the influence of the change of braking speed on the vibration characteristics of the brake disc, the finite element model of the brake was subjected to multi-physics coupling simulation at the initial braking speeds of 150 km/h, 200 km/h, and 250 km/h, respectively. After extracting the overall axial vibration displacement range and root mean square value of vibration velocity, the change results of the vibration characteristics of the brake disc under the change of the initial braking speed are shown in Figure 13.  
Figure 13a-c are, respectively, the brake disc vibration displacement-time curve, the axial vibration displacement range-braking speed bar graph, and the root mean square value of the axial vibration velocity graph. It can be seen from Figure 13a that the variation range of the axial vibration displacement of the brake disc increased with the increase of the braking speed; It can be seen from Figure 13b that when the braking initial velocity was 150 km/h, 200 km/h, and 250 km/h, the ranges of axial vibration displacement of the brake disc were \(4.32\times 10^{-5}\) m, \(5.04\times 10^{-5}\) m, and \(7.89\times 10^{-5}\) m, respectively, and the value increased with the increase of the braking initial velocity. It can be seen from Figure 13c that the axial vibration intensity of the brake disc increased gradually with the increase of the initial braking speed. From Figure 13a-c, it can be concluded that when the initial braking speed increased, the axial vibration amplitude of the brake disc increases and the vibration speed became faster, that is, the intensity of the axial vibration of the brake disc was intensified. The main reasons leading to the above phenomenon are as follows: When the braking speed increases, the friction contact scratching distance between the brake disc and the brake pad per unit time increases, and the work done by the friction force between the friction pairs increases. The effect of frictional work is to convert the mechanical energy of the brake disc and release it in the form of heat and vibration. Therefore, when the braking speed increases, the energy released by the brake disc through vibration increases, which leads to the intensification of its axial vibration. At the same time, the heat generated  
Figure 13: Vibration characteristics of brake disc under changing braking speed: (**a**) vibration displacement; (**b**) extreme difference in vibration displacement; (**c**) vibration intensity.  
by the friction pair increases due to the work done by friction, and the heat is transferred to the brake disc through the heat conduction of the material to increase the temperature and cause greater thermal warpage.
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
###### Abstract  
Numerical analysis, especially the finite volume method (FVM), is one of the primary approaches employed when evaluating a building environment. A complicated geometry can degrade the mesh quality, leading to numerical diffusions and errors. Thus, this study develops and evaluates an automatic building geometry simplification method based on integrating similar surfaces for the geometry of an indoor space. A regression model showed that the complexity of the simplified geometry and its similarity to the original geometry decreased linearly with the threshold of the method. The mesh quality was significantly improved by the simplification. In particular, the maximum skewness decreased exponentially with the threshold of the method. It is expected that the simplification method and regression model presented in this study can be used to quantitatively control the mesh quality.  
automation; building model; geometry simplification; finite volume method; mesh quality; model design Article
## 3 Satellite Precipitation Uncertainty Distribution Approximation
### Constellation Changes-Induced Uncertainties
The GMM model also stood out by being well suited to more unimodal and leptokurtic distributions over the entire precipitation range (i.e., the RE_C\({}_{04}\) case), despite slightly underestimating the density peaks. The Gaussian model poorly reproduced the uncertainties across the entire range of precipitation, underestimating the observed distribution peaks at about \(-5\)% RE. The RE_C\({}_{04}\) Gaussian distributions were negatively skewed rather than positively skewed.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 2 Materials and Methods
### Training Evaluation Using Total Reward and Q-Value Loss  
The effectiveness of the agent's learning process can be evaluated by calculating the total reward per training episode. The total reward refers to the cumulative number of reward units that the agent accumulates during a single episode of training. Formally,  
\[R_{total}=\sum_{t=0}^{T}r_{t} \tag{7}\]  
\(r_{t}\) is the reward received at step \(t\), and \(T\) is the total number of steps in the episode. This metric reflects the agent's ability to generate efficient sequences that are rewarded positively under the defined reward function. It also helps identify stagnation or regressions in learning dynamics, especially in later epochs. This metric is widely used to evaluate the effectiveness of an agent's policy in reinforcement learning [31]. To monitor the quality of the agent's training, we use a loss function. The loss is defined as the mean squared Bellman error (Mean Squared Loss, MSL):  
\[\mathcal{L}(Q)=\frac{1}{2}\bigg{(}Q(s_{t},a_{t})-\bigg{[}r_{t}+\gamma\max_{a^ {\prime}\in\mathcal{A}(s_{t+1})}Q\big{(}s_{t+1},a^{\prime}\big{)}\bigg{]} \bigg{)}^{2} \tag{8}\]  
The loss function is computed immediately after the Q-function update Equation (1) and indicates how far the current estimate \(Q(s_{t},a_{t})\) deviates from the expected target which consists of the immediate reward and the estimated maximum future return. This function is standard in TD learning and is widely used in both classical Q-learning and DQN-based approaches [35; 36]. The loss function serves as a quantitative indicator of the following:  
* How quickly the agent's behavior stabilizes, which is reflected by the reduction of fluctuations in the loss curve.
* Simultaneous reduction in the loss function and increase in the total return \(R_{total}\) indicates convergence toward an effective Q-value.  
Unlike typical reinforcement learning problems where agents often use deep neural networks, in our case, the Q-table is discrete, and the loss function is used solely as an indicator of stability, not as a target function for optimization. Nevertheless, it is used for tuning training parameters and determining whether training was successful. After training is complete, the agent can be used to synthesize new TC. In the next section, we describe the algorithm for TC synthesis based on the knowledge accumulated in the Q-table.
## 3 The Optimum Shape of a Reciprocating Oscillator for Self-Induced Oscillation
### The Pre-Test in 2D
#### 3.1.4 3D Fluid-Structure Coupled Analysis  
The simulation conditions for the coupled problem are listed in Table 3. Based on the previously discussed results, the authors consider that model 2 (ref. to Table 2) may be optimal, as it produces higher \(Cl\) and is relatively stable. Model 1, a semicircle, was analyzed for comparison. Model 3 was an elongated version of model 2. In order to compare their maximum \(Cl\), the weight of the three models with varying lengths in the \(x\) direction are the same, as is the support constraint. (Elastic base setting to move 0.01 m under a force of 1.92 N). The mechanics are shown in Figure 14, in which the reciprocating motion in the \(y\) direction from the oscillator parallel to \(z\) direction will be transmitted to a shaft, then the motion will be converted into rotation by an appropriate mechanism in order to generate electricity. Care was required in order to avoid negative meshes, the mesh type and its size, together with the total number of meshes, including the scale of the fluid domain, were iterated, as dynamic mesh is required for three-dimensional simulation; furthermore, an academic version of ANSYS was used, which involves some limitations. The convergence of the calculation was acceptable. However, the residues were a little higher than for the 2D case, indicating the presence of some error. The computing precision is currently limited by the use of a regular PC for simulation; in the future, this will be addressed by an upgrade of the processing environment.  
The simulation results are shown in Table 4. From the results, it can be concluded that \(Cl\) increases with elongation in the \(x\) direction, as was observed in the 2D model. However, the maximum displacement of model 3 is less than that of model 2. Based on the previously observed data, this reduction of output will be due to instability, resulting in an inability to meet the conditions required to move the object (oscillate). It can be concluded that model 2 is the optimum shape from among those  
Figure 13: Distribution of velocity for an extended semicircle (model 2) at \(t=23.75\) s. The reattached region is moving to the rear on the circular side of the object.  
Model 6 in Table 2 may also be optimal based on its stability and high \(Cl\). However, the coupled simulation result cannot be compared with those in Table 4, as the simulation failed under these specific conditions; the simulation, however, was successful with different weight and support parameters. Clearly the relationships between \(Cl\), model weight, supporting conditions, and optimum length require further research.  
The square-headed model has a higher \(Cl\) compared to the circular model. However, a very important aspect regarding installation of this mechanism is that, if it is not perpendicularly installed to the flow, reciprocation will not occur.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Overview and Terminology
Further mechanisms allow the resulting initial cluster constellation to respond to connectivity problems and moderate topology changes, such as subsequent installation, relocation, or removal of individual nodes in the course of maintenance activities. In this case, required incremental cluster adjustments are automatically handled by the CHs and CMs, respectively, while new nodes can find and join the existing clusters. However, in the case of detection of severe changes in the network structure and number of participants, it may be necessary to initiate a new cluster formation. The associated concepts beyond the initial phase sequence are discussed in Section 4.4. The coordination of the phase sequence is taken over by a Master Cluster Head (MCH) elected by all nodes in phase 0. Preferably, the MCH has a central position in the network and thus a small average distance to the other nodes and initiates the phase transitions via control messages. After reaching phase 7, this coordinating task of the MCH expires, and it remains in the network as conventional CH. The design decision for a phase sequence controlled by messages offers the advantage of loosely synchronizing the timing of the mesh nodes without requiring a precise common time base. This would be necessary, e.g., for frequency hopping approaches, in which nodes must meet at fixed time intervals on rendezvous channels to negotiate channel sequences [12; 13; 14]. In particular, phase-based synchronization allows stabilization of the topology information and metrics obtained during each phase duration, on the basis of which cluster formation and channel selection decisions are made. In contrast to completely asynchronous methods, the phase-based approach can also be easily reproduced and analyzed in practice. Figure 3: Phase sequence for initial clustering and channel assignment. Reprinted from ref. [18].
## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Ris  
RISs are meta-surfaces that use electronic circuits (usually, varactor diodes or other micro-electromechanical systems) to manipulate impinging electromagnetic signals in a desired way. By intelligently manipulating each reflecting surface, RISs can change the amplitude, phase, or frequency of each incoming signal and redirect them to a desired location without using power amplifiers. In an RIS, each meta-surface is capable of independently steering an impinging electromagnetic ray to a desired angle of reflection, and manipulating the phase of the reflected ray so that it aligns with the angle and phase of other rays from other meta-surfaces in the RIS [271], thereby ensuring that all the reflected rays are received with the same angle and phase (these are usually designed to align with the angle and phase of the line-of-sight signal (LOS)) at the receiver. By acting in such a manner as a low complexity (re-) transmitter, RISs can forward signals to a receiver and have been shown to largely outperform other amplify-and-forward-based relaying techniques in terms of received power (mostly due to their large geometry of the RIS antennas) [271] as well as EE in wireless communication systems [272], since they do not require active elements. They are mostly passive and do not require external power supply. However, they do not amplify the RF signals impinging on their surface [271], which is also an advantage since they do not amplify the noise.  
RISs can be combined with UAVs to improve system throughput [273], network coverage and reliability [274; 275], or EE [276; 277; 278]. By acting as relays for signals without the need for additional external power supply, RISs achieve high EE when combined with UAVs in cellular networks. RISs are deployed between the UAV and ground-based user nodes. As a result, the UAV can transmit at a much lower power to reach the RISs [271], leading to significant power savings. In addition, since received power is proportional to the effective area of the receiving antenna, RISs can capture more of the transmitted RF signal compared to user devices that have much smaller antennas. Again, this enables the UAV to lower its transmit power due to the efficiency of the RISs, which helps the UAV to conserve its power and prolong its operational lifetime.  
UAVs often need to move to maintain LOS connection with mobile ground terminals. With RIS, the UAV simply beams its signal on the reflecting surface, whose reflecting angle and phase are then manipulated to focus the signal in the direction of the mobile users. This has been shown to significantly lower UAV energy consumption since up to 95% of the energy consumed by rotary-wing UAVs is spent on flying [276]. It was also shown in [280] that even in the presence of a direct LOS signal between the UAV and internet-connected vehicles, RIS significantly improves the bit error rate, coverage probability, and throughput. These metrics imply not only less transmission power from the UAV but also fewer retransmissions.
###### Abstract  
The separation process between 5-hydroxymethylfurfural (HMF) and trace glucose in glucose conversion is important in the biphasic system (aqueous-organic phase), due to the partial solubility property of HMF in water. In addition, the yield of HMF via the dehydration reaction of glucose in water is low (under 50%) with the use of Bronsted acid as a catalyst. Therefore, this study was conducted to optimize the production and separation of products by using a new hydrophobic ionic liquid (IL), which is more selective than water. The new IL (1,3-dibutyl-2-(2-butoxyphenyl)-4,5-diphenyl imidazolium iodide) [DBDIm]I was used as a solvent and was optimized for the dehydration reaction of glucose to make a more selective separation of HMF, levulinic acid (LA), and formic acid (FA). [DBDIm]I showed high performance as a solvent for glucose conversion at 100 \({}^{\circ}\)C for 120 min, with a yield of 82.2% HMF, 14.9% LA, and 2.9% FA in the presence of sulfuric acid as the Bronsted acid catalyst.  
imidazolium-based IL; glucose conversion; hydroxymethylfurfural (HMF); levulinic acid (LA); formic acid (FA)
## 3 Results and Discussions
### Formulation of Liposomes with Mulberry Extract and Their Characterization
#### 3.3.2 Characterization of the Size and the Electric Surface Potential of the Liposomes by DLS  
The size distribution of the liposomes and the electrical surface charge (Zeta Potential) of the liposomes were further determined by DLS analysis. The analysis of these param  
Figure 2: Microscopic images obtained with an Olympus CX40 inverted light microscope, through a 40\(\times\) objective in phase contrast mode, captured by a Hitachi CCD camera. (**a**) L—empty liposomes, (**b**) MnL—mulberry-loaded liposomes. The white arrows highlight in both cases the spherical, round, dispersed liposomes.  
eters for liposomes is of major importance because it can provides information on their stability [63; 64; 65].  
The size distribution histograms of empty liposomes (L) and mulberry-loaded liposomes (MnL) are shown in Figure S2A and Figure S2B respectively.  
According to Figure S2A, the sizes of L sample are between 37 and 955 nm, of which 87.12% have a size up to 500 nm, while MnL sample showed 78.68% size distribution under 500 nm (Table 3).  
Liposomes of a large size are considered to be highly effective for penetration into the cell and implicitly for transporting the hydrophilic extract included in the vesicle through the membrane of the cell. Liposomes with a size of up to 500 nm are considered to have a high biocompatibility with the cell membranes of fibroblasts [61; 66; 67].  
The electrical surface charge of the L and MnL samples were also determined. For L and MnL samples, the electric surface charge was \(-\)2.80 mV and \(-\)2.5 mV, respectively which indicate a good stability of [24; 68; 69].  
Our results demonstrated that the black mulberry extract did not influence physico-chemical properties of liposomes. These results are consistent with the literature that used the lipid film hydration method for the encapsulation of various medicinal substances or plant extracts [23; 25].
## 3 Results
### Model Application to Satellite Data  
We could produce hourly \(n_{\mathrm{p}}\) products by applying the developed model into the hourly GOCI \(R_{\mathrm{PS}}(\lambda)\) data. Those hourly products were then synthesized into daily, monthly, seasonally, and annually averaged products. As shown in Figure 10, the general distribution pattern for 2015 is available. High \(n_{\mathrm{p}}\) values generally dominated nearshore waters, while low values were mainly distributed in the offshore waters. The whole BS region usually had relatively high refraction values of approximately \(>\)1.10, whereas most areas in the YS, except coastal regions, showed low values (roughly \(<\)1.05). Note that a large water region that includes Subei shoal and the Yangtze River estuary appeared to have very high refraction, which even exceeded 1.15 for some areas.  
The seasonal and monthly variations in \(n_{\mathrm{p}}\) in this study water areas were further generated as shown in Figure 11. On the whole, the \(n_{\mathrm{p}}\) during the period from December 2014 to April 2015, as well as November 2015, appeared to have the most high-valued distribution throughout the entirety of the  
Figure 10: Annual distribution pattern of \(n_{\mathrm{p}}\) derived by our developed model, based on GOCI data of the whole year of 2015.  
Figure 9: Scatter plots show the in situ \(n_{\mathrm{p}}\) values versus those values derived from the developed model by means of in situ observed \(R_{\mathrm{rs}}\) data (**A**) and GOCI satellite-derived \(n_{\mathrm{p}}\) values (**B**). The solid red lines refer to the 1:1 lines, and the dotted lines indicate the \(\pm\)5% ranges of deviation relative to the central line. The \(n_{\mathrm{p}}\) spatial patterns derived from the in situ measurements (**C**) and the GOCI satellite retrievals (**D**) in January 2017 were also compared.  
BS and YS regions, whereas relatively low values showed up between May and October 2015. In detail, in several particular water regions, including the Yellow River estuary, Yangtze River estuary, Subei shoul, and very nearshore areas, the \(n_{\rm P}\) values remained almost static over time and were always high (\(>\)1.15) throughout the entire year. In most regions of the BS, the \(n_{\rm P}\) values showed distinct seasonal variations, namely, high values in winter, early spring, and late autumn, whereas the values in summer were low. Similar temporal trends in \(n_{\rm P}\) variation also appeared in the YS. However, the difference with that in the BS was that there existed generally lower \(n_{\rm P}\) values in the YS for the same month.  
Figure 11: Seasonal distribution patterns of the \(n_{\rm P}\) estimated by our developed model using GOCI satellite observations during 2015 using the \(n_{\rm P}\) model developed in this study.
## 4 Characterizing Spectral Efficiency Dynamics  
Observe that the RRM Relaxation algorithm allows UE and BS to skip synchronization cycles, potentially leading to beam misalignment, as well as change in the distance to the BS when UE moves. To characterize this effect, we determine the gains at the BS and UE, time \(t\) after the synchronization time instant, \(G_{A}(t)\) and \(G_{U}(t)\).
## 2 Network Architecture and Model
It is worth noting that the load on the satellite is limited; the satellite cannot process services so that the inter-satellite link can be regarded as a channel, and the intermediate node receives the signal without processing it so that the entire wavelength routing path can be viewed as a \"channel\" composed of the source and destination node. Figure 2: Transmission mechanism. The set of requests in optical satellite networks is represented by \(R\), and each request \(reR\) can be described by tuple \(r=(s_{r},d_{r},t_{br},t_{sr},t_{cr})\). Among them, \(s_{r}\) and \(d_{r}\) express the source and destination node of \(r\) in EOSNs, \(t_{br}\) is the arrival time, \(t_{sr}\) is the duration of the request, \(t_{cr}\) is the end time of the request. For AR, due to the elastic time, the actual start transmission time \(t_{abr}\), actual transmission end time \(t_{aar}\), and latest start transmission time \(t_{lsr}\) of the request should also be considered. The relationship is expressed as \(t_{br}\leq t_{abr}<t_{er}\), \(t_{abr}+t_{sr}-1=t_{aar}<t_{er}\) and \(t_{lsr}=t_{er}-t_{sr}+1\). Each request has an elastic time window with a start transmission time \(t_{urr}=[t_{br},t_{lsr}]\), as long as the request starts transmitting within this time range. Figure 3 shows the correlation between the time points of request \(r\). \(t_{br}=1\), \(t_{er}=10\), so other time points range from [1; 10]. The allocation of AR services exhibits flexibility within the time domain, ensuring that services transmitted within the designated elastic time window, denoted as \(t_{urr}\), remain valid. Consequently, in EOSNs, time slots can be allocated judiciously for requests falling within \(t_{urr}\). This approach conserves time slots for subsequent requests and enhances the overall efficiency of wavelength resource utilization. A two-dimensional resource matrix represents the resource occupancy of optical satellite networks. The resource usage on each link has two dimensions: wavelength \(W=\{w_{1},w_{2},\cdots,w_{max}\}\) and time slot \(TS=\{ts_{1},ts_{2},\cdots ts_{L}\}\). Each element of the resource matrix refers to the state of the wavelength \(w\) of link \(e\) in time slot \(ts\), using a binary variable \(u^{e}_{w,ts}\) represents, as shown in Equation (1). \[u^{e}_{w,ts}= \begin{cases}0&,\quad ts\text{ of }w\text{ at }t\text{ on }e\text{ is not free}\\ 1&,\quad\quad ts\text{ of }w\text{ at }t\text{ on }e\text{ is free}\end{cases}  \tag{1}\]