esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts
#### 3.2.4 Nh\({}_{3}\)-TPD  
The acidity of the surface of the catalyst can be effectively measured by NH\({}_{3}\)-TPD. Figure 6 presents the NH\({}_{3}\)-TPD curves of the catalysts with different CeO\({}_{2}\) contents, in which Figure 6a-d correspond to CeO\({}_{2}\) contents of 15, 5, 20, and 0%, respectively. The four catalysts with different CeO\({}_{2}\) contents have the same peaks at 225 and 430 \({}^{\circ}\)C that correspond to the Bronsted and Lewis acid positions [19], respectively. The extent of these two peaks before the reaction is shown in Table 6.  
The total acidic sites of the CeO\({}_{2}\) catalyst decreased significantly with the addition of CeO\({}_{2}\). However, the Bronsted acid sites increased first and then decreased at 20% CeO\({}_{2}\). At a 15% CeO\({}_{2}\) addition, the ratio of Bronsted acid to total acids sites was the largest (51%) as compared to only 21% for Ni-Cu/HZSM-5. The increase in ratio of Bronsted acid to total acids may enhance the cracking ability of large molecules [20].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & **Lewis Acid Band Area** & **Bronsted Acid Band Area** & **Total Acid Area** \\  & **(m\({}^{2}\)/g)** & **(m\({}^{2}\)/g)** & **(m\({}^{2}\)/g)** \\ \hline Ni-Cu/HZSM-5 & 1808 & 673 & 2481 \\
5\%CeO\({}_{2}\)–Ni-Cu/HZSM-5 & 1480 & 989 & 2469 \\
15\%CeO\({}_{2}\)–Ni-Cu/HZSM-5 & 1114 & 1159 & 2273 \\
20\%CeO\({}_{2}\)–Ni-Cu/HZSM-5 & 1503 & 727 & 2230 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The peak areas for the catalysts with different CeO\({}_{2}\) contents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Catalysts**} & \multicolumn{2}{c}{**Specific Surface Area**} & \multicolumn{2}{c}{**Pore Volume**} \\  & **(m\({}^{2}\)/g)** & & **(mm\({}^{3}\)/g)** \\ \cline{2-5}  & **Before Reaction** & **After Reaction** & **Before Reaction** & **After Reaction** \\ \hline Ni–Cu/HZSM-5 & 346 & 158 & 216 & 136 \\
5\%CeO\({}_{2}\)–Ni–Cu/HZSM-5 & 330 & 217 & 204 & 138 \\
15\%CeO\({}_{2}\)–Ni–Cu/HZSM-5 & 322 & 222 & 196 & 175 \\
20\%CeO\({}_{2}\)–Ni–Cu/HZSM-5 & 310 & 205 & 192 & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The specific surface area and pore volume of different catalysts before and after the reaction.  
Figure 6: The NH\({}_{3}\)-Temperature-Programmed Desorption (TPD) curves of the catalysts with different CeO\({}_{2}\) contents: (**a**) 15%CeO\({}_{2}\)–Ni–Cu/HZSM-5, (**b**) 5%CeO\({}_{2}\)–Ni–Cu/HZSM-5, (**c**) 20%CeO\({}_{2}\)–Ni–Cu/HZSM-5, and (**d**) Ni–Cu/HZSM-5.
## 9 Conclusions  
This paper discussed a network-assisted mobility support scheme for 6 LoWPAN nodes, which are extremely energy and resource constrained devices in nature. The paper proposed the LoWMob mobility support scheme, where a MN associates with the SN, which is responsible for mobility of the MN. The proposed scheme reduces the tunneling overhead between the GW and the SN, without involving MN in a significant amount of signaling. Signaling overhead has been reduced at a greater extent by introducing a special packet format that utilizes the dispatch values of the 6 LoWPAN adaptation layer and 16 bits addressing of IEEE 802.15.4. Furthermore, this paper proposed a distributed version of LoWMob, named as DLoWMob. It employs the concept of MSPs and route optimization for intra-PAN communication. Mobility-related packets are handled by the MSPs that route them to and from the CN or the GW. We have shown that at different speed of the MN, DLoWMob shows better performance than LoWMob in terms of end-to-end delay, packet success ratio, and signaling costs.
## 2 Methodology
### Data Collection  
The experimental data of BDS-3 satellites come from a total of 80 MGEX and iGMAS ground stations and 190 pairs of satellite-to-satellite links during the period of day-of-year (DOY) 198-280 in 2019. The distribution of these stations is marked in Figure 1. Note that during this period only 21 BDS-3 satellites were in orbit, including 18 MEO satellites, 2 IGSO satellites (C38 and C39), and 1 GEO satellite (C59). However, the IGSO and GEO satellites are still in a test stage, during which the signal can only be decoded by the iGMAS receivers. The observation of satellite C39 started from DOY 227. A brief comparison of the amount of two types of measurements in BDS Week 715 (DOY 251-257) is plotted in Figure 2 as an example. Figure 2a shows the number of available one-way ISL measurements in pairs, where the outliers and single one-way observations that cannot form a dual one-way link are discarded. Figure 2b shows the number of available code/phase measurements after screening outliers and deleting short arcs less than 1200 s. It is obvious that there are many more ISL data than L-band measurements despite the different types of orbits. The number of L-band measurements of IGSO and GEO satellites is less than that of MEO satellites.  
Figure 1: Distribution of 80 MGEX (blue dot) and iGMAS (red dot) stations.
## 3 Solar Arrays Pointing Algorithms
### Control Algorithm  
The control algorithm is dedicated to sequencing the pulsed modulation to select the correct control axis to rotate the spacecraft and align the solar arrays. The control algorithm is successive to the determination section algorithm, and it is driven by the output of the previous section to compute the proportional term in Equation (9). This is done by selecting the proper modulation vector, \(\mathbf{\hat{m}}\). The derivative control action is not affected by the algorithm since it is always driven by the estimation of the angular rates of the spacecraft. The pulsed modulation is designed in a way that after an active proportional control action, a period with only derivative control is present. This is done to assess the evolution of the Sun angle before applying the next proportional control pulse, and it shall be synchronized with the determination logic. Then,  
\[\mathbf{\hat{m}}=\begin{cases}\mathbf{\hat{m}},\text{ if }t_{0}<t_{i}<t_{W}\\ \mathbf{0},\text{ if }t_{W+1}<t_{i}<t_{2K-1}\end{cases}, \tag{19}\]  
where \(W<K\) is the number of time steps when the proportional control is active. Then, it defines the duty cycle of the proportional control. The intensity of the pulsed proportional control is scaled according to the amplitude of the error with respect to the target pointing. This is done to avoid the Sun angle flickering when \(\alpha_{S}\to 0\) can introduce large control pulses leading to an unstable target pointing. In fact, small fluctuations of the Sun angle around the zero value are accepted as normal, and the control shall not intervene to immediately correct them. Hence, the scaling term in the proportional control is:  
\[f(\alpha_{S})=1-\cos\alpha_{S},\text{ with }f(\alpha_{S})\in[0,1]. \tag{20}\]  
Whenever the proportional control is not zeroed, the control algorithm applies a torque around one axis according to the following repeating sequence:  
\[\mathcal{S}_{\mathbf{\hat{m}}}=\{+\hat{\varphi}_{1}\rightarrow-\hat{\varphi}_ {1}\rightarrow+\hat{\varphi}_{2}\rightarrow-\hat{\varphi}_{2}\}, \tag{21}\]  
which is a constant parameter for the algorithm. When the system is activated, the repeating sequence starts from the beginning, \(+\hat{\varphi}_{1}\). This is achieved by initializing the last commanded direction as \(-\hat{\varphi}_{2}\). The entire control logic explained above is summarized in the pseudo-code of the control algorithm reported in Algorithm 2, which is the second and final section of the operative state. The final output of the ADCS is the control torque, \(\mathbf{t}_{c}\), actuated by the specific onboard actuators. For reaction wheels and magnetic torquers, the actuation commands are obtained inverting, respectively, Equations (4) and (5).
# UAV Charging Station Placement in Opportunistic Networks
## 7 Routing Protocols
### Epidemic Routing Protocol  
In epidemic routing [37], each host maintains a summary vector (SV) that consists of a unique identifier for every message this host carries. The message either originated at this host or it has been buffered on behalf of other hosts. When two hosts come into communication range of one another, an anti-entropy session is initiated, during which the two hosts exchange their summary vectors to determine which messages the hosts carry, then each host decides which messages it needs by performing a logical AND operation between the negation of its summary vector and the summary vector of the other host. Following this, each host requests copies of those messages that it has not yet seen. Algorithm 3 depicts the Epidemic Routing protocol.  
```
Input: Hosts \(u\) and \(v\) boolean \(antiEntropySession\)\(SV(u)\) and \(SV(v)\) Output: boolean \(messagesExchange\)
```  
Drop messages with expired lift-time from \(u\) and \(v\);  
if\(antiEntropySession\)then  
Host \(u\) sends \(SV(u)\) to \(v\);  
\(msg\overline{s}s\gets SV(u)+(\
eg SV(v))\);  
Host \(v\) requests \(msg\overline{s}s\) from \(u\);  
Host \(u\) sends \(msg\overline{s}s\) to \(v\);  
foreach\(m\in msg\overline{s}s\)do  
if\(isDestination(v)\)then  
\(consume(v,m)\);  
else  
\(store(v,m)\);  
end if  
end \(messagesExchange\gets true\) ```
**Algorithm 3** Epidemic routing protocol  
[PERSON] and [PERSON] [37] proposed a session strategy that is utilized in Epidemic, Spray and Wait, and SCR. Figure 3 shows an example session between nodes A and B. Considering that node A and B have encountered each other, node A will send its ID, concatenated with the message IDs that node A has in its buffer. In this example case, node A has messages with IDs 5, 14, 28, and 89. Once node B receives this message, node B replies with a message containing the message IDs that node B does not have. Node A sends the requested messages, with IDs 28 and 89, to Node B. Once the message exchange finishes, the same session strategy is repeated for node B and node A. At the end of the next session, the messages between A and B will be synchronized.
## 3 Method and Materials
### Interview Results and Discussion
For example, when asphalt concrete was being paved, there was a problem with the temperature of the mixture, which could not be traced back to whether the insulation measures were in place during transportation or the temperature of the mixture when it left the site. Consequently, the process has certain problems and consumes too much time and money. Another obstacle is the lack of technical skills and inexperience of employees. In this regard, respondents (A2, A5, A16, A21, A22) indicated that the current construction personnel lacked relevant professional knowledge and necessary operating skills, which made them unsuitable for the needs of modern construction projects. Benefits for system implementation  
The benefits of using monitoring systems were extensively discussed in the interviews with respondents. For example, the application of highway side slope automatic monitoring systems, highway electromechanical equipment intelligent monitoring systems, and asphalt pavement damage monitoring systems have improved highway construction and management [31; 32; 33]. During the interviews, respondents agreed that the main benefits of adopting a monitoring system were greater efficiency and accuracy as well as improved safety when compared to traditional manual monitoring. Another advantage, according to five respondents, was the reduction of errors caused by human factors, such as reading data incorrectly. Six respondents believed that the monitoring system could effectively reduce the cost of the whole project, and the information management system could improve the management efficiency and convert the previously handwritten forms into electronic records to reduce the cost. Therefore, the application of monitoring systems could greatly improve the construction quality of asphalt pavement and make a great contribution to modernization. Improvements for system implementation  
Based on the respondents' responses, the interviewers discussed how to improve the asphalt pavement construction monitoring system. Firstly, 15 respondents said that when implementing the monitoring system, it was necessary to ensure that the processing of monitoring data met the requirements of asphalt pavement construction. Therefore, the monitored data could be integrated to develop a monitoring system that was more suitable for on-site construction and management. Secondly, 20 interviewees agreed that there was a need to expand the scope of monitoring and use only one system for monitoring. It was necessary to conduct real-time monitoring of the mixture from production, mixing, transportation, paving to rolling, and monitor key parameters at each stage. Feedback to staff when the threshold was reached was also needed as part of the process. Finally, construction workers needed the appropriate training and professional knowledge and skills to meet the needs of modernization projects. The specific improvement measures are shown in the next two subsections.
## 1 Introduction
Leveraging the strengths of the stochastic resonance phenomenon, the successful detection of weak useful signals in a strong background noise environment is a crucial challenge in signal processing. This has significant theoretical importance and wide-ranging practical applications, particularly in the research of new signal-detection methods in power systems. In our previous research, we found that the combination of a chaotic system with a random resonance detection system can be used to accurately detect the frequency and amplitude of harmonic signals. However, the noise resistance of the chaotic system is generally stronger than that of the random resonance detection system. The chaotic system relies on the random resonance system to detect harmonic frequency when the signal is unknown. Therefore, it is important to improve the noise resistance of the random resonance detection system for detecting harmonic signals in power systems. Additionally, improving the performance of bistable and multistable stochastic resonance detection systems can enhance the detection performance of their single-well approximation models. This breakthrough can enable stochastic resonance to detect non-periodic signals, which is significant for blind detection of other non-periodic signals in power systems under strong background noise.
## 5 Methodology and Experiments
### Lstm  
The LSTM model that is used in this work is presented in Figure 11. The model has two LSTM cells with 100 units and one dense layer. For the gates, the sigmoid activation function was used, and for the state vector, the hyperbolic tangent function was used.  
Even though recurrent networks are good at dealing with sequences, they cannot memorize long sequences. To mitigate this downsize, LSTM networks, which are a kind of variant version of RNN networks, are used. Because of the recurrent configuration of the RNN models, when the number of time steps increases, it causes a vanishing gradient  
Figure 11: LSTM model representation.  
Figure 10: Forecasting performance of the stateful RNN model.  
Figure 9: Forecasting performance of the stateless RNN model.  
problem [12]. The LSTM architecture is shown in Figure 12, and it provides an efficient way for the gradients to backpropagate.  
LSTM models are more complex than RNN models, but they can deal with longer sequences, such as several hundred time steps; thus, they can produce successful results on language models [43]. As seen in Figure 12, short-term memory is conveyed with a simple RNN cell, and long-term memory is preserved with a dense layer using a sigmoid activation function and two units, forget gate and input gate. Forget gate takes the previous state vector and current sample as input. It produces values between one and zero since it uses the sigmoid function. If forget gate outputs values close to one, long-term memory is kept, but if it outputs values close to zero, long-term memory is reset. The input gate also produces values between one and zero, and the product is multiplied by the RNN cell's output. If a trend is detected in the current input that has not existed before, the input gate decides to add this information to long-term memory. Finally, the output gate produces the result of current time steps by multiplying long-term memory with a dense layer. Forecasting performance of the LSTM model is presented in Figure 13, where orange line is the predicted values, and the blue line is the real numbers. The LSTM model has a forecasting metric of 5.375 in terms of MAE. As a result, the LSTM model performed better than the RNN models and dense model.
## 2 Background and Related Work
### Number of Errors per Page and Layer  
[PERSON] et al., showed that the number of errors in 3D TLC NAND flash differs for each layer and page [1]. Figure 3 is a distribution chart of the number of errors reported by [PERSON] et al. [1]. Endurance testing is a procedure that determines the number of incorrect bits for each page and layer after repeatedly writing and erasing a memory block in a random pattern. Figure 3 depicts an error bit distribution measured at the end of the endurance test reported by [1]. The \(x\)-axis layers in Figure 3 range from 0 to 1. The closer the layer is to 0, the lower the layer, and the closer the layer is to 1, the higher the layer. Furthermore, the Fail bits represent the number of times an error occurred, and it is expressed as a relative value ranging from 0 to 1. The black, blue, and red dots represent the top, middle, and bottom of the page, respectively. We can see the characteristics of the errors that occur in each layer in Figure 3. The lower page has the most errors in the lower layer, and the number of errors occurs regardless of the page type. Conversely, in the middle layer, you can see that the number of error occurrences for each page is comparable. In addition, in the upper layer, it can be seen that the upper page has the most number of errors and the lower page has the least number of errors.  
Figure 2: Average bit error rate per block number in different P/E cycles [16].
## 3 Numerical Examples
### Inversion Effects Using Single-Component Data with Bad Traces  
In this section, we continue testing the inversion effects using data with bad traces, but assume that the data quality is poor and only single-component seismic data can be used for inversion. It is assumed that only X-component data are available. The observed data with bad traces were the same as the previous section, and one single-shot recording is shown in Figure 9b. The near-offset direct wave of the X-component observed data was intercepted to perform source wavelet inversion. The inversion results of the two methods are shown in Figure 14. It is evident that due to the influence of poor-quality traces, the L2 EWI method has encountered challenges in achieving accurate source wavelet estimation results. There are obvious errors in the amplitude and phase information of the inverted source wavelet. The CNEWI method can still obtain good source wavelet estimation results, especially for the estimation of the phase information. Compared with the inversion results in Figure 12, we can see that using multi-component seismic data inversion can achieve better inversion results of the source wavelet. However, judging from the inversion results, for source wavelet inversion, whether single-component or multi-component seismic data are used, the CNEWI method can obtain reliable phase information of the source wavelet.  
Still using the X-component seismic data, the source wavelets estimated by the L2 EWI and CNEWI methods were used to conduct elastic full-waveform inversion, and the results are shown in Figure 15. It can be seen that due to obvious errors in the source wavelets estimated by the L2 EWI method, there are obvious errors in the inversion results of Figure 15a,b. Elastic full-waveform inversion using source wavelets estimated by CNEWI shows robustness, and the inversion results in Figure 15c,d are significantly better than those in Figure 15a,b. However, since only the X-component data are used, the construction effect of the P-wave velocity model in Figure 15c is not as good as the S-wave velocity model in Figure 15d.  
Figure 14: Source wavelet inversion results using single-component seismic data.  
Figure 15: EFWI results comparison using single-component seismic data with bad traces. (**a**) P-wave velocity model using source wavelet estimated by L2 EWI; (**b**) S-wave velocity model using sourcewavelet estimated by L2 EWI; (**c**) P-wave velocity model using source wavelet estimated by CNEWI; (**d**) S-wave velocity model using source wavelet estimated by CNEWI.
## 5 Discussion  
According to the experimental results in the fourth Section, the ARFM model proposed in this study exhibits fairly good performance in terms of both loss value and accuracy. The findings were as follows:  
1. The model with the addition of a multi-residual network layer has stronger feature crossing ability, which can be seen from the improvement in model accuracy, but at the same time, there is no obvious weakening in running speed, indicating that the residual network has very good adaptability to the model and will not forcibly improve the model effect at the cost of sacrificing time.
2. The combination of the attention layer and aging mechanism proposed in this study is surprisingly good, which may be precisely because their essence is to explore the influence of data or features on the recommendation effect and the model output. By adding the aging effect as a weight to the effect of the attention network, the differences between the features can be highlighted to obtain more accurate results.
3. Compared to other models, the ARFM model proposed in this study has various advantages. Regardless of the loss or results, the overall stability and average result data of the ARFM model were better than those of the other models, thereby proving the validity of the model.
4. Through experiments, we found that the performance of ARFM on different datasets differed to some extent. It can be clearly observed from the loss result diagram that ARFM performed better than the other models on datasets with a larger scale and more samples. This indicated that the model was more suitable for relatively large and complex scenarios.  
In this study, the ARFM model was compared with four other models. Specifically, the performance of the ARFM on Dataset 1 was not outstanding; the loss value was slightly higher than that of the well-optimized wide and deep models, and the accuracy was slightly higher than that of the other models. However, the performance of the other two datasets was very obvious. It can be clearly observed that the accuracy of the deep crossing model is lower than that of the other models. With an increase in training time, the ARFM model  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Baselines** & **Inprovement** \\ \hline
**Deepcrossing** & +2.15\% \\ \hline
**Wide and Deep** & +1.24\% \\ \hline
**Deepfm** & +1.05\% \\ \hline
**AFM** & +1.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance growth of ARFM.  
is more stable and efficient in terms of both reduction in the loss value and improvement in accuracy. Except for the ARFM model, the AFM model has the best performance among the models; however, the ARFM model has a higher accuracy and smaller loss value than the AFM model, which is sufficient to explain the advantages of ARFM.