esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biodiesel Production by Enzymatic Transesterification (First Stage)
For reaction time of 16 h, this minimum (396.08 g biodiesel) turned into a maximum (408.82 g biodiesel) because of the breaking of such dimers and higher accessibility of reactants to the active sites. The reaction time used in the transesterification process is a relevant variable affecting the level of target conversion. This operating factor significantly affects process performance and limits reaction mechanism. [PERSON] et al. [55] reported that it is feasible to achieve high FAMEs yields using low enzyme concentrations; however, these reactions would take longer than chemical method. As shown in Figure 2, biodiesel yield decreased when increasing reaction time for enzyme concentration of 14% and 18%, which is explained by the presence of more unreacted methanol over time interfering separation of glycerol from methyl ester and increasing its solubility. When glycerol remained in solution it contributes to drive the equilibrium back to the left, lowering the yield of biodiesel [51; 56]. For enzyme concentration of 16%, biodiesel production increased during the course of the reaction, which suggested that the equilibrium was driven to the right for all the reaction times (products side). Based on the above, the low presence of unreacted methanol in E4, E5 and E6 experiments suggested that the separation of glycerol and esters was not significantly interfered. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enzyme Concentration** & **8 h** & **12 h** & **16 h** \\ \hline
14\% & E1 & E2 & E3 \\
16\% & E4 & E5 & E6 \\
18\% & E7 & E8 & E9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nomenclature of experiments in the first stage. The analysis of variance (ANOVA) was performed in nested one-way to study the interactions of reaction time and enzyme concentration on biodiesel yield. The \(p\)-value reached 0.49 that is higher than  
Figure 3: Schematic representation of reaction mechanism during enzymatic transesterification  
Figure 2: Biodiesel production per experiment: (**a**) yield (%) and (**b**) weight (g). Values are mean +/- standard deviation (n = 2). 0.05 showing no statistically significant effect of the treatments for enzyme concentration and reaction time. After transesterification process, the enzyme is difficult to retrieve because it was free dissolved in the bioreactor and the complexity of waste frying oil also affects its reusability. Studies have shown that using supporting materials such as nanoparticles to immobilize the enzyme allow to maintain and even increase the activity and stability of the enzyme for further recoveries [57; 58]. The implementation of enzymatic immobilization during the enzymatic transesterification of waste frying oils still requires extensive research associated with the synthesis of novel materials, which has motivated the search for low-cost and efficient immobilization techniques.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Link Budget
#### 5.1.2 Effect of Altitude of UAV on Link Parameters at a Given Speed  
As discussed in the introduction, UAM requires communication systems at different altitudes. The link parameters and quality of service vary with changes in altitude. The analysis below was done while considering the power received via the UAV after rain attenuation. The link parameters vs. the distance curves at various altitudes with the UAV traveling at 45 m/s are shown in the figures below. The Doppler effect was considered when evaluating the drone's relative velocity, considering the satellite's orbit velocity.  
Figure 10 shows the variation in the received power as we increase the altitude. It is evident from the figure that, as we increase the altitude, more power is received. This is due to fewer losses along the path, as the path becomes shorter. It can be noted that, beyond the altitude of 2000 m (above the cloud altitude), the change in the received signal strength is much less. This happens since rain attenuation is no longer a factor beyond 2000 m in altitude.  
Figure 11 shows the variation in FSPL as we increase the altitude. From the result shown in the figure, we can infer that the free path loss decreases with an increase in altitude, while it increases as the UAV moves laterally away from the satellite at a constant altitude. This is because the distance between the satellite and the UAV decreases. Naturally, this trend is the opposite of what was observed for ground base stations in [26].  
Figure 10: Received power vs. horizontal distance at different altitudes.  
Figure 9: FSPL vs. horizontal distance.  
The carrier-to-noise ratio (SNR) and latency were calculated and plotted against the horizontal distance for different altitudes. The results are shown in Figure 12 and Figure 13, respectively. The SNR would decrease as the UAV moved closer to the satellite, and there would be less noise as you moved closer to the source of the signal. Latency drops as the UAV moves to a higher altitude in the case of satellites as the distance between the UAV and the satellite is reduced. These results show that the link budget parameters for satellite communications exhibit opposite trends when compared to ground base stations [26]. This is solely due to the direction of the source of the signal. When the altitude of the UAV is increased, it moves closer to the satellite but away from ground base stations.  
From these figures, it can be noted that both C/N and latency decrease with an increase in altitude. One of the factors important to note here is that, due to the vertical polarization of the rain attenuation, the power loss is more in the vertical direction as compared to the horizontal direction, and hence, for the given problem statement, a UAV moving vertically up is more likely to lose its connection to the base station compared to a UAV moving horizontally due to rain.
## 3 Materials and Methods
### System Model  
This section describes the system model underlying our proposed approach. In this deep reinforcement learning environment, the focus is on the DASH system. Within this DASH system, the DASH server makes available a collection of videos represented as V {v\({}_{1}\), v\({}_{2}\)\(\ldots\) v\({}_{\rm{N}}\)}. Each of these videos is segmented into various bitrates or quality levels, denoted as Q {q\({}_{1}\), q\({}_{2}\), q\({}_{3}\)\(\ldots\) q\({}_{\rm{M}}\)}, with each segment having a fixed duration. Furthermore, each video is accompanied by a media presentation description (MPD) file containingcomprehensive information about these segments. At the inception of a video streaming session, the DASH client kicks off the process by requesting and subsequently analyzing the MPD file. This analysis serves as the foundation for initializing the streaming process, aligning it with the current network conditions and the capabilities of the user's device. Within this framework, we conceptualize the client-side adaptation as an autonomous agent tasked with determining the most suitable bitrate for each segment based on an optimal policy. Upon downloading the initial segment, this agent actively monitors the environment. This environment encompasses critical parameters such as bandwidth measurements, buffer status, and the quality (bitrate) of the previously downloaded segment. The agent leverages this environmental information to make informed decisions about the optimal bitrate for the next segment. This iterative process continues as the agent takes actions and receives rewards, culminating when all segments have been successfully downloaded.  
We employ the Markov Decision Process (MDP) learning model to formulate the optimal bitrate adaptation strategy, leveraging the distance factor between two consecutive segments. Our objective is to ensure a high level of user satisfaction, denoted as \(S=\{s_{1},s_{2},s_{3}\ldots\}\), which is defined by the avoidance of playback interruptions and the provision of a consistently high-quality video streaming experience. In this context, the variable \(S\) represents the function of playback interruption avoidance and the maintenance of stable, high-quality streaming, denoted as follows:  
\[S=f\Big{(}\text{playback}_{\text{interruption}},\text{stable}_{\text{ high}}\text{quality}\Big{)}, \tag{1}\]
## 2 Study Area and Data
### Data
#### 2.2.2 Auxiliary Data  
This study introduces crop phenology information to enhance the model's ability to perceive the growth cycle. Phenology data records the key growth stages of each crop and their corresponding time windows, which help the model dynamically adjust its focus at different time points, concentrating on the most distinguishing growth stages, thereby improving crop classification and multi-class semantic segmentation accuracy (as shown in Figure 3). These phenology data primarily come from the agricultural department of Hunan Province and are validated by relevant data from the Agricultural Meteorological Bureau, covering the growth stage time windows of cotton, tea, and rapeseed in 2022. These data specifically reflect the timing of various growth stages, such as sowing, green-up, heading, flowering, and maturation, providing the model with key temporal features.  
Since phenology data is typically presented at discrete time points, the stage information needs to be first converted into vector form using one-hot encoding and then further mapped into a continuous feature space through linear transformation to ensure that phenology information can seamlessly integrate with other features of remote-sensing data. Furthermore, because the sampling frequencies of phenology data and remote-sensing data may differ, and phenology data often contains missing values and noise [28], linear interpolation and Savitzky-Golay (SG) filter smoothing are required to fill missing values and remove outliers, ensuring the temporal synchronization and data quality stability between phenology and remote-sensing data [29].
## 3 Results
### Multi-Channel Operation  
The main advantage of this generator is the possibility to work with 4 independent output channels, simultaneously applying different pulse protocols to the load. Figure 9 shows an example of this multi-channel operation with the load connected between the HV+ terminals of two channels, 0 and 1; one pulse is generated in each channel with a time delay, generating a bipolar pulse in a resistive load.  
Table 4 shows the pulse command line in the two channels, 0 and 1, considering an input voltage of U\({}_{\rm dc}\) = 625 V, i.e., 8U\({}_{\rm dc}\)= 5 kV.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# MM0** & **Pause (ns)** & **Pulse (ns)** & **Mask** & **Stages on** \\ \hline (a) 1 & 200 & 200 & 510 & 8 \\ (b) 1 & 200 & 100 & 510 & 8 \\ (c) 1 & 200 & 100,000 & 510 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single channel command line for the pulse sequence shown in Figure 7.  
Figure 8: Single pulse operation of the generator, with various pulse widths and amplitudes during one burst: (blue) output current 10 A/div, 400 ns/div.  
As shown in Figure 9, a bipolar symmetric pulse of approximately 5 kV, 50 A, and 200 ns width is applied to a 100 \(\Omega\) load, with 250 ns time between pulses. The bipolar pulse has about 30 ns rise and fall times in both positive and negative pulses. Although the pulses shown in Figure 9 are shifted, it is possible to define a command line where pulses can be applied to different channels at the same time.  
Another example of the multi-channel operation, using all the 4 channels is presented in Figure 10, where two shifted bipolar pulses with decreasing amplitude are generated. One between HV+ terminals of channels 0 and 1 and the other between HV+ terminals of channels 2 and 3. The command line for Figure 10 is described in Table 5, for each MM.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 6 Experimental Evaluation  
### System Network Topology  
The system combines the time phase quantum key distribution equipment with the classical network, and the designed video conference system network topology based on the quantum key is shown in Figure 8, wherein:  
1. The dotted line indicates the quantum channel for quantum key distribution; the solid line indicates the classic channel for the encryption communication line of the video conference system. The line attenuation value of the main site and the sub-site is less than or equal to 18 dB. All lines are bare fibers, and devices such as optical amplifiers cannot be included in the line.
2. The video conference system comprises a main site and a sub-site. The main site includes a single-receiving time phase QKD device; the sub-site includes a single-emitting time phase QKD device. The key is generated through the QKD device quantum channel of each site and saved to the quantum key management equipment of each site. Based on the decoy BB84 protocol, the QKD device integrates the quantum signal- emitting or -receiving module with a working frequency up to GHz. The signal light has a wavelength of 1550 nm and a pulse width of 200.
3. Through wavelength division multiplexing, the multi-channel quantum wavelength division multiplexing terminal aggregates multiple QKD devices into the same fiber for transmission, which achieves multiple expansion of the quantum key distribution rate.
4. The video conference system consists of one server and two users, namely user1 and user2. The server is a ThinkServer TS540 with a CPU model of Intel Xeon E3-1225, a clock speed of 3.2 GHz, and a memory size of 4 GB. The user uses an 8G, i5-4200U industrial computer. The virtual machine uses the Centos 6.6 64 bit operating system.
5. Each terminal node can directly access office applications such as voice calls and faxes or access office data subnets. When the internal IP service data performs secure communication with the outside, it needs to be encrypted by a quantum key.
## 4 Case Study: Searching Physical Models in MATLAB Simulink  
In this study, we aim to address two key questions in the context of the presented case study: (1) Is it feasible to develop a semantic model to provide a search engine based on vector representations? and (2) Can this search engine deliver accurate results? To answer these questions, a case study using MATLAB Simulink was conducted to provide an advanced mechanism for indexing and retrieval of Simulink models. The experiment sought to evaluate the advantages of the proposed solution to retrieve physical models, and was designed as follows:  
Figure 6: Example of the pattern matching calculation process.  
A dataset of Simulink models covering electronic models and physical models in the automotive and aerospace domains was defined. This dataset comprised 40 models.
2. A query dataset for evaluating the retrieval capabilities of the proposed solution was created. Queries were automatically designed by mixing the available terms in the physical models with other related terms that do not appear in the generated text of the model, such as the term circuit (see Table 1).
3. The indexing and retrieval process was executed. For each query defined in the previous step, we analysed the models retrieved via the implemented method, taking into account all matching term distances and patterns between the query and models.
4. The results were analysed and validated using the schema proposed in [35]. Specifically, the performance metrics used to evaluate the method were (1) precision (the proportion of retrieved information that is relevant); (2) recall (the proportion of relevant information that is retrieved); and (3) the F1 score, which is a combination of the first two metrics.
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 2 Statement of the Problem
### Material Parameters  
The nanocrystalline material can be considered as a two-phase composite that comprises the interface and the grain phases (Figure 2). By assuming that both phases are isotropic, [PERSON] [40] derived the effective modulus of elasticity of nanocrystalline materials using the Mori-Tanaka effective field method. The results included the effective bulk modulus (or volume modulus) \(K\), shear modulus \(G\), and [PERSON]'s modulus (or elastic modulus) \(E\) of nanocrystalline materials. In this study, the Lame parameters \(\lambda\) and \(\mu\) and mass density \(\rho\) of FG nano copper are derived on the basis of [PERSON]'s approach.  
\(K\) and \(G\) can be expressed in terms of \(E\) and [PERSON]'s ratio \(\
u\).  
\[K=\frac{E}{3(1-2\
u)},G=\frac{E}{2(1+\
u)} \tag{6}\]  
\[K_{m}=\frac{E_{m}}{3(1-2\
u_{m})}\,K_{c}=\frac{E_{c}}{3(1-2\
u_{c})}\,G_{m}= \frac{E_{m}}{2(1+\
u_{m})}\,G_{c}=\frac{E_{c}}{2(1+\
u_{c})} \tag{7}\]  
where subscripts \(m\) and \(c\) represent the parameters of the interface phase and the crystal, respectively. In this study, the Poisson's ratio of the interface phase is assumed to be the same as that of the crystal, that is, \(\
u_{m}=\
u_{c}\). Based on the Mori-Tanaka effective field method, the relationship between the elastic moduli of the interface phase and the crystal is defined as [40].  
\[\frac{E_{m}}{E_{c}}=\frac{E(r)}{E(r_{0})}=\frac{\left[(n+1)\left(\frac{r_{0}}{ r}\right)^{n+3}-(m+1)\left(\frac{r_{0}}{r}\right)^{m+3}\right]}{(n-m)} \tag{8}\]  
where \(m\) and \(n\) are material constants, \(r\) is the average atomic spacing, and \(r_{0}\) is the equilibrium position. The relationship of the average atomic spacing and mass density of the interfacial phase \(\rho_{m}\) is expressed as [40].  
\[\frac{r_{0}}{r}=\left(\frac{\rho_{m}}{\rho_{c}}\right)^{\frac{1}{3}} \tag{9}\]  
The volume fraction of the crystal \(c_{c}\) is determined as [40].  
\[c_{c}=\left(\frac{L^{3}}{L+d}\right)^{3}=\left(1+\frac{d}{L}\right)^{-3} \tag{10}\]  
where \(L\) is the average radius of the grain and \(d\) is the average thickness of the interfacial phase.  
From Reference [40], the parameter \(\beta_{1}\) and \(\beta_{2}\) are defined as:  
\[\beta_{1}=\frac{2(4-5\
u_{m})}{15(1-\
u_{m})}\ \text{and}\ \beta_{2}=3-5\beta_{1} \tag{11}\]  
Figure 2: Microstructure model of nanocrystalline materials [40].  
The shear and effective bulk moduli of nanocrystalline materials are obtained as:  
\[K=K_{m}+\frac{c_{c}K_{m}(K_{c}-K_{m})}{K_{m}+\beta_{2}(1-c_{c})(K_{c}-K_{m})},G=G_ {m}+\frac{c_{c}G_{m}(G_{c}-G_{m})}{G_{m}+\beta_{1}(1-c_{c})(G_{c}-G_{m})} \tag{12}\]  
The Lame parameters \(\lambda\) and \(\mu\) can be determined in terms of \(K\) and \(G\) as:  
\[\lambda=K-\frac{2}{3}G,\ \mu=G \tag{13}\]  
The mass density also depends on the volume fraction of the crystal.  
\[\rho=\rho_{c}c_{c}+\rho_{m}(1-c_{c}) \tag{14}\]  
where \(\rho_{c}\) and \(\rho_{m}\) are the mass densities of the ideal crystal and the interfacial phase, respectively.
## 2 Related Works
We generalize our previous work [4] to the case of directed path allocation problems (DPAP) and consider another conflict expression that is based on resources. Note that a more detailed description of the work performed in [4] is presented later in the paper. In another direction, there is a wide literature on Earth observation scheduling problems (EOSPs) [17]. In such problems, some observation requests have to be assigned tosatellites and scheduled for each satellite so that several constraints (e.g., temporal constraints related to the possible maneuvers of the satellites) are satisfied. Various criteria have been studied in the literature. Nevertheless, as the problem is generally over-constrained because of the number of requests to satisfy, a classical criterion is to optimize a (weighted) total reward provided by the satisfied requests. The Earth observation scheduling problem has a lot in common with the orbit slot allocation problem (OSAP) considered in this paper. Indeed, both problems involve observation requests posted by users, visibility windows, constraints related to the satellite disjunctive nature, etc. However, there are several differences between the two problems. First, in the orbit slot allocation problem, users want to \"own\" the satellite during some orbit portions in order to perform a set of observations. The targets to be observed during each slot are not precisely known in advance, which means that constraints about the satellites' maneuvers are irrelevant in the case of OSAP. Then, the requests' nature is different. In fact, in OSAP, the requests are composed of several slots possibly over several months ahead, and each slot is quite long as it is supposed to allow the user to perform several observations. In the case of EOSPs, there are many more requests but on a very short time horizon (a few days at most), and each request requires a very small amount of satellite time. Finally, fairness between users is essential in the case of OSAPs, whereas it is rarely considered in EOSPs. Two exceptions are the work described in [18], where the authors study a multi-objective EOSP and aim at maximizing the total profit and minimizing the maximum profit difference between each pair of users, and the work described in [19], where a heuristic method is proposed to solve the EOSP while taking into account fairness. Using graphs in the context of EOSPs is not novel. In [20], an activity-on-node graph allows modeling of all the alternatives to satisfy observation requests by a set of satellites (one node is one opportunity to observe a request target by a satellite, and the edges allow conflicts between observation candidates to be represented). Then, maximizing the number of satisfied requests amounts to computing the maximum independent set of the graph.