esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
###### Abstract  
Increasing demand to strengthen energy security has increased the importance of natural gas sweetening and biogas upgrading processes. Membrane-based separation of carbon dioxide (CO2) and methane (CH4) is a relatively newer technology, which offers several competitive advantages, such as higher energy-efficiency and cost-effectiveness, over conventional technologies. Recently, the use of graphene-based materials to elevate the performance of polymeric membranes have attracted immense attention. Herein, we do not seek to provide the reader with a comprehensive review of this topic but rather highlight the key challenges and our perspectives going ahead. We approach the topic by evaluating three mainstream membrane designs using graphene-based materials: (1) nanoporous single-layer graphene, (2) few- to multi-layered graphene-based stacked laminates, and (3) mixed-matrix membranes. At present, each design faces different challenges, including low scalability, high production cost, limited performance enhancement, and the lack of robust techno-economic review and systematic membrane design optimization. To help address these challenges, we have mapped out a technology landscape of the current graphene-based membrane research based on the separation performance enhancement, commercial viability, and production cost. Accordingly, we contend that future efforts devoted to advancing graphene-based membranes must be matched by progress in these strategic areas so as to realize practical and commercially relevant graphene-based membranes for CO2/CH4 separation and beyond.  
graphene-based material; CO2 separation; mixed-matrix membrane; \(F_{\text{index}}\); Robeson upper bound; graphene-based laminate; single-layer graphene +
Footnote †: journal: Applied Physics Letters
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments
### Comparative Analysis of Experimental Results
#### 4.5.1 Key Parameter Selection Results and Analysis  
On the basis of calculating the PCC and combining the actual data generated by the SAG mill grinding process monitored by the beneficiation plant, variables with low correlation and above are selected as input parameters of the SAG mill power prediction model according to Table 1. Finally, seven key parameters were selected as the input of the hybrid model. The selected parameters include OFR, SRR, PEP, DEP, GS, WFR, and MT. The power consumption of the SAG mill grinding process is used as the model output. Figure 9 shows the heatmap of PCC between the selected parameters and the power consumption of the SAG mill. As can be seen from Figure 9, the ore feed rate, water feed rate, and power consumption are negatively correlated, and the remaining parameters are positively correlated with power consumption. Different input parameters have different effects on power consumption, and the degree of correlation between different parameters is also different. Therefore, considering the importance of different parameters is very important for the construction of the prediction model. Table 6 provides a detailed description and explanation of the selected parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Key Parameter** & **Description** \\ \hline ore feed rate (OFR) & The amount of ore supplied to the mill per unit of time \\ sand return rate (SRR) & The amount of sand and stone returned per unit of time \\ feed end pressure (FEP) & The pressure of the feed end bearing \\ discharge end pressure (DEP) & The pressure of the discharge end bearing \\ grinding sound (GS) & The grinding sound generated during the grinding process \\ water feed rate (WFR) & The amount of water supplied to the mill per unit of time. \\ motor temperature (MT) & The temperature of the SAG motor \\ power consumption (PC) & The power consumed during the grinding process \\ \hline \end{tabular}
\end{table}
Table 6: The key parameter during the grinding process.  
Figure 8: Box plots of the data before preprocessing and after preprocessing. (**a**) Box plot of data before preprocessing; (**b**) Box plot of the processed data.
## 4 The Experiment and Its Results
It is essential to compare the processed values with a standard to assess the efficacy of the employed methods. All spectra obtained for a given temperature (in this case, all \(10^{8}\)) were averaged to obtain an accurate standard value for the quantity under study, and reference BFS values were calculated using the LCF method. The sensor line was 1 km long. It should be noted that averaging was performed for each spatial coordinate separately, as any optical fiber, even those produced by well-established VAD/OVD methods, still exhibits some BFS variation along its length. Figures 9 and 10 illustrate the relationship between the error/repeatability in determining BFS and the number of experimentally measured spectra involved in the processing. In this instance, the spectra obtained with the accumulation of \(10^{3}\) were subjected to processing. Consequently, the SNR, as determined by the aforementioned method, was found to be 2-3 dB. The obtained results are in good agreement with the simulation. The proposed method outperforms LCF in both metrics. The optimal number of spectra for processing is approximately 16. For spectra with an accumulation of \(5\times 10^{4}\) (which corresponds to an SNR of approximately 11 dB), similar dependencies are shown in Figures 11 and 12. In this  
Figure 8: An experimental setup for obtaining BCS includes the following components: FUT (fiber under test), DAQ (digital acquisition), and NBL (narrow bandwidth laser). instance, the advantage is only achieved with large scanning steps. With a scanning step of less than 2 MHz, the proposed algorithm is outperformed by LCF. Finally, Figure 13 illustrates the discrepancy in the efficacy of the proposed algorithm and the LCF method for experimental data. Figure 10: Dependence of the BFS extraction precision on the number of averaged spectra for experimental data with low SNR. Green arrow shows gain in BFS extraction accuracy. Inset: dependence of the gain in precision on the scanning step. Figure 9: Dependence of the BFS extraction accuracy on the number of averaged spectra for experimental data with low SNR. Green arrow shows gain in BFS extraction accuracy. Inset: dependence of the gain in BFS extraction accuracy on the scanning step. Figure 11: Dependence of the BFS extraction accuracy on the number of averaged spectra for experimental data with high SNR. Green arrow shows gain in BFS extraction accuracy. Inset: dependence of the gain in BFS extraction accuracy on the scanning step. Figure 12: Dependence of the BFS extraction precision number of averaged spectra for experimental data with high SNR. Green arrow shows gain in BFS extraction accuracy. Inset: dependence of the gain in BFS extraction precision on the scanning step. Figure 13: Dependence of the gain/loss in the accuracy of determining the BFS of the presented algorithm over LCF when processing the spectra obtained in the experiment.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 2 Medical Imaging Methods
### Mri  
By applying a certain radiofrequency pulse to an organ in a static magnetic field, H protons in the organ tissue are excited, leading to the phenomenon of magnetic resonance. Upon termination of the radiofrequency pulse, H protons induce magnetic resonance signals during the relaxation process. After receiving magnetic resonance signals, spatial coding and image reconstruction, magnetic resonance images are generated [6]. MRI images achieve an excellent soft tissue resolution of liver and other organs, with a clear display of anatomical structures, such as vascular and biliary systems and lesion morphology, and provide valuable multi-orientation and multiparameter information, such as diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) and MR spectra.  
These data aid in determining the spatial location of the anatomical structure, and are beneficial for the metabolic analysis of liver tumors, leading to improved diagnostic value. However, the image can be affected by gastrointestinal gas and respiratory movement, and the location, puncture and ablation of lesions under the guidance of MRI all require special instruments and devices. Based on the same mechanics as MRI in terms of atomic physics, function MRI (fMRI) generates images of the metabolic activities within the anatomic structures generated by MRI scans [2].
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 7 Results, Evaluation of the Solutions
### Discussion  
#### 7.2.1 Effect of the Degree Bounds  
In the first experiment, we show the effect of the maximal degree bound \(D_{max}\) varied from 3 to 12 when \(D_{min}=1\). The edge costs were randomly generated with \(C_{max}=5\). Table 2 resumes the graph properties, and also the number of cases in which trees and hierarchies corresponding to the constraints exist. In each run, 100 random graphs with 100 nodes were created.  
\begin{table}
\begin{tabular}{r r r r r r r r r} \hline \hline \(D_{max}\) & \(1\,\boldsymbol{V_{1}}\) & \(1\,\boldsymbol{A_{G_{A}}}^{1}\) & \(\boldsymbol{nb(H)}\) & \(\boldsymbol{nb(T)}\) & \(\boldsymbol{c(MSTSL)}\) & \(\boldsymbol{c(Heur)}\) & \(\boldsymbol{c(DCMSH)}\) & \(\boldsymbol{c(DCMST)}\) \\ \hline
3 & 34.06 & 613.06 & 86 & 50 & 150.116 & 201.151 & 178.43 & 181.8 \\
4 & 25.14 & 689.67 & 96 & 96 & 142.188 & 180.656 & 153.802 & 156.979 \\
5 & 19.29 & 751.69 & 100 & 100 & 136.61 & 166.21 & 143.63 & 144.66 \\
6 & 17.1 & 768.76 & 100 & 100 & 134.19 & 157.54 & 139.32 & 139.9 \\
7 & 14.01 & 797.2 & 99 & 99 & 131.778 & 149.162 & 135.273 & 135.657 \\
8 & 12.9 & 807.07 & 100 & 100 & 131.05 & 145.46 & 133.8 & 134.12 \\
9 & 11.54 & 822.09 & 100 & 100 & 130.39 & 144.9 & 132.9 & 133.09 \\
10 & 9.92 & 835.36 & 99 & 99 & 130.162 & 140.222 & 132.061 & 132.172 \\
11 & 9.12 & 841.31 & 100 & 100 & 128.51 & 139.73 & 130.42 & 130.54 \\
12 & 8.32 & 849.74 & 100 & 100 & 127.17 & 135.76 & 128.67 & 128.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results by varying \(D_{max}\).  
Figure 8: A particular star and its DCMSH.  
The average number \(|E|\) of randomly generated edges was 463 in each run. Trivially, the average number \(|V_{1}|\) decreases by increasing \(D_{max}\). The column \(|A_{G^{\prime}_{A}}|\) indicates the average number of arcs in the simplified digraph. The next two columns indicate the number of graphs satisfying the conditions for the existence of the DCMSHs (the number of hierarchies) and the number of DCMSTs, respectively. With a large set \(V_{1}\), there are fewer possible trees. The last columns contain the average costs of the four structures. It is clear that as \(D_{max}\) increases, the costs converge to the same value. Probably, the cost \(c(MSTSL)\) converges towards the cost of the \(MST\). The cost \(c(DCMSH)\) of the optimal hierarchies is always better or equal to the cost \(c(DCMST)\) (if these solutions exist). By increasing \(D_{max}\), the probability of degree bounds one (and also of the other small degree bounds imposing real constraints on the degrees) decreases. Consequently, the difference between trees and hierarchies decreases and their costs tend to the cost of the MSTSL. Notice that the heuristic also provides good costs.
## 5 Classification the Results  
The results of the round nozzle field, Section 4.2, are compared with empirically determined equations established by other authors. As shown in Section 2.3, the model equations pertain to various geometric configurations and are, therefore, only partially comparable. Figure 8 compares the calculated mean heat transfer coefficients of forced convection for a round nozzle field according to various model equations with measurement results gathered with the presented method. A detailed description of all model equations can be found in [PERSON] [20].  
The general correlation that the forced heat transfer coefficient increases less linearly with the nozzle exit velocity is evident for all models. The measurements also confirm this correlation. The measurement follows the [PERSON] [12] model equation up to a nozzle exit velocity of \(u_{nazzle}=50\) m/s, after which the [PERSON] [17] model is the best approximation. The measurement show higher heat transfer coefficients than the [PERSON] [6] model, but lower heat transfer coefficients than the [PERSON] [8] model.  
In general, the classification of the measurement results shows that both the measurement and the energy balancing for calculating the heat transfer coefficient are realistic and follow the recognised models of other authors. Noticeable differences are attributed to the different measurement methods. The [PERSON] model predicts significantly higher heat transfer coefficients than all other models and the measurement. This can be explained by a significant change in the nozzle system compared to the other studies. [PERSON] investigated a geometry consisting of round holes in parallel nozzle ribs through which air  
Figure 7: (**a**) Distribution of forced local heat transfer and (**b**) heat transfer coefficient at section A–A through the centre of a round nozzle field at p = 1354 Pa, \(u_{nazzle}=48.4\) m/s, Re = 81,767.  
can flow. Accordingly, only a small space is available for the mixing zone with its backflows. The models of the other authors predict the same heat transfer coefficients as a function of the nozzle outlet velocity, taking geometric deviations into account.  
The good agreement between the measurement and the [PERSON] model is due to the short nozzle height of \(L\) = 30 mm. In his work, [PERSON] analysed a perforated plate which, unlike [PERSON]'s investigations, has no real nozzles. The length of [PERSON]'s nozzles is unknown.  
It is not possible to compare the local heat transfer coefficients from the measurement with the existing model equations. In the past, it was not possible to measure the temperature of the impact surface as accurately and with the same local resolution as it is possible today. The investigation methods with the local resolution are too different to be compared in a representative way.
## 4 Network Performance
### Ctp
It can be observed that the number of control packet receptions is higher than control packet transmissions, reflecting the effect of broadcast control messages received by multiple neighbor nodes. For analyzing the effect of control packets, Figure 12 compares this control traffic with data traffic transmissions in the network. It shows that the  
Figure 9: Network-level packet reception rate (PRR) during the CTP evaluation period. number of transmissions in control packets is higher than data packets, although the behavior of the curves is different. On one side, data packet transmissions are expected to have a linear behavior, sometimes affected by battery depletions, while control packet transmissions tend to stabilize over time. Furthermore, only after four months of operation, the data traffic exceeds control traffic in number of transmissions; however, control packets still consume more energy because broadcast messages use the entire LPL sleep interval. Figure 11: The cumulative number of network-level control packet (Ctrl Pkt) transmissions (Tx) and receptions (Rx) during the CTP evaluation period. Figure 12: The cumulative number of control packet transmissions and data packet transmissions during the CTP evaluation period in the network. Figure 10: Network-level cost of transmissions during the CTP evaluation period. Figure 13 shows the network PRR as a function of the number of connected nodes for each month analyzed for CTP. In this case, higher network PRRs are observed for higher numbers of connected nodes. This indicates that when using CTP, the network improves its throughput and thus, as more nodes are connected, CTP seems to not present problems forwarding the increased generated traffic. We further study the performance of the ASWP testbed using the node-level results shown in Figures 14 and 15. Forwarded packets in Figure 14 confirm the existence of highly used nodes  
Figure 14: Node-level health information totals for packets generated, forwarded, retransmitted and dropped during the CTP evaluation period. Figure 13: Network PRR _versus_ number of connected nodes in the network when using CTP. PRRs are computed monthly and the number of connected nodes is computed as the average of daily connected nodes within the network in the same time period. (_i.e._, 1010, 1020, 1030, 1050, 1100, 2141, 2150 and 3113), which forward most of the traffic in the network. In addition, these nodes do not show a low PRR or PSR, as seen in Figure 15, indicating that they are able to handle these traffic conditions. However, whenever nodes at these hot spots deplete their batteries, network partitions may occur, as experienced during December 2013.
## 4 System Model
### Technical Layer
#### 4.1.2 Fog Layer  
All data coming from the IoT layer are stored and processed on fog layer servers. One of the objectives of fog computing is to reduce the network burden. Smart devices generate and transmit large amounts of data for intelligent analysis and action.  
(a) Local servers: Servers are situated within the institutions, and they serve as hubs to which all the devices in the IoT layer are connected. These servers both store and process all incoming data, and when necessary, they transmit it to the subsequent cloud servers.  
(b) Smart reporting: The local server receives data from all smart devices and sensors connected to IoT layers. The data and streaming are stored in the database. Institutionaladministration and parents can view institutional and student reports on their registered devices, which are connected to fog servers. The server's algorithms also notify principals or parents when certain conditions are met. These reports may include smart results, students' progress reports, teacher progress reports, etc.  
\[\alpha_{SR}(x)=\begin{cases}Not(rslt)&\text{if result is updated}\\ Not(meeting)&\text{if Updated}\\ Nill&\text{not above}\end{cases} \tag{6}\]  
\[\alpha_{SPR}(x)=\begin{cases}Not(Monthly)&\text{if 1 st of the month}\\ Not(Weakly)&\text{if Last day of the weak}\\ Nill&\text{not above}\end{cases} \tag{7}\]  
\[\alpha_{TPR}(x)=\begin{cases}Not(Monthly)&\text{if 1 st of the month}\\ Not(Weakly)&\text{if Last day of the weak}\\ Nill&\text{not above}\end{cases} \tag{8}\]  
where \(\alpha_{SR}(x)\) denotes the results notification, \(\alpha_{SPR}(x)\) denotes the notification for the performance reports of students, and \(\alpha_{TPR}(x)\) denotes the teacher performance notification.  
(c) Flipped classrooms: Fog computing supports the implementation of flipped learning, an innovative pedagogical approach that reverses the traditional roles of homework and classroom instructions. In flipped learning, teachers create video lessons that students can access at home, while classroom time is devoted to engaging in active learning tasks with the teacher's guidance. The fog server hosts and distributes the video content and assignments, enabling parents to monitor the progress of students through their smartphones [56].  
(d) Assessment: To ensure the fair and accurate evaluation of teachers and students, assessment transparency is essential. Smart testing services enable the examination of students and the storage of their results on fog servers. These records facilitate transparent reporting and decision-making.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 2 Related Works  
Object detection using deep learning and convolutional neural networks (CNNs) has been extensively researched in various domains, including healthcare, manufacturing, and augmented reality applications. In particular, real-time object detection systems have demonstrated their potential to enhance safety and efficiency in critical environments, such as medical settings, industrial automation, and assistive technologies. Given the high risk associated with medication errors, the identification of look-alike vials remains a challenging problem. Traditional methods, such as barcode scanning, often face problems with obstructions, poor lighting, and the need to be checked by hand. This is why researchers are looking into deep learning models to improve vial recognition and classification.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 3 Results  
### Time-Domain Transmission Error  
Using the model settings and scenarios defined in Section 2, the time-domain TE curves exhibit three distinct trends. Figure 2 shows the relative transmission error (TE) angle for the three simulated configurations. The ideal geometry (blue) settles to a steady mean of \(-5\times 10^{-5}\) deg after 0.05 s. Activating the pitch error table (red) shifts the mean to \(-9\times 10^{-5}\) deg and superimposes a regular, saw-tooth modulation. Re-enabling the measured microgeometry (black) produces a much deeper negative offset (\(\approx\)\(-3\times 10^{-4}\) deg) and a long-wave fluctuation that repeats once per pinion revolution. Peak-to-peak TE rises from 1.2 \(\times\) 10\({}^{-5}\) deg (ideal) to 1.6 \(\times\) 10\({}^{-5}\) deg (pitch only) and to 3.1 \(\times\) 10\({}^{-4}\) deg with measured microgeometry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation** & **Flank Geometry** & **Pitch Table** & **Purpose** \\ \hline I Ideal & Nominal CAD & None & Baseline \\ II Micro & Measured & None & Microgeometry only \\ III Pitch & Nominal CAD & Calibrated table & Pitch error only \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation matrix.  
Figure 2: Relative TE angle versus time for the three simulated configurations (0–0.4 s, 1000 rpm). Red curve: pitch error only, blue curve: ideal geometry, black curve: measured microgeometry.