esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Construction of Shearer Drum Height Prediction Model Based on AR-AIGA-FRRBFNN
#### 5.2.2 AR-AIGA-FRRBFNN Model and Evaluation Method
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{6}{c}{**Predicted Drum Height/m**} & \multirow{2}{*}{**Real Drum**} \\ \cline{2-2} \cline{5-8}  & **AR-AIGA-FRBF** & & **AIGA-FRBF** & & **AR-GRU** & **AR-SVR** & **AR-RBF** & **MSLSTM [12]** & **AFRPN [16]** & **Height/m** \\ \hline
1 & 1.4637 & 1.4942 & 1.5320 & 1.5162 & 1.5691 & 1.5135 & 1.4898 & 1.44 \\
2 & 1.4284 & 1.4750 & 1.5094 & 1.4893 & 1.5236 & 1.5091 & 1.4767 & 1.40 \\
3 & 1.4477 & 1.4571 & 1.5476 & 1.4978 & 1.5721 & 1.4890 & 1.4635 & 1.43 \\
4 & 1.4653 & 1.4835 & 1.5816 & 1.6060 & 1.6367 & 1.5243 & 1.4982 & 1.44 \\
5 & 1.4816 & 1.4779 & 1.5273 & 1.4725 & 1.5565 & 1.5287 & 1.4673 & 1.45 \\
6 & 1.5031 & 1.5283 & 1.5331 & 1.5126 & 1.5476 & 1.5397 & 1.5109 & 1.47 \\
7 & 1.4907 & 1.4899 & 1.5662 & 1.5439 & 1.6101 & 1.5383 & 1.4976 & 1.44 \\
8 & 1.5135 & 1.5362 & 1.5534 & 1.4819 & 1.5633 & 1.5546 & 1.5455 & 1.45 \\
9 & 1.4762 & 1.5018 & 1.5125 & 1.4633 & 1.4875 & 1.5075 & 1.4894 & 1.43 \\ \(\cdots\) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Predicted height of the training sample of the left drum of the shearer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameters** & **Final Value** \\ \hline RBF & Primary function & Gaussian function \\ \hline  & Kernel function type & Radial basis kernel function \\ SVR & Kernel coefficient g & 0.125 \\  & penalty coefficient C & 151 \\ \hline GRU & GRU helped layer & 1 \\  & Number of hidden layer neurons & 250 \\ \hline MSLSTM layers & 3 \\  & Number of neurons in the first hidden layer & 100 \\  & Number of neurons in the second hidden layer & 50 \\  & Number of neurons in the third hidden layer & 50 \\ \hline AFRPN [16] & Attenuation rate \(\rho\) & Self-adaption \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Hyperparameters of different prediction models.
## 4 Conclusions  
The cascade NEAS featured multiple platforms on the pressure-volume curve, enabling it to absorb energy at various levels simultaneously within a single system and significantly broaden the scope of NEAS. Pore size distribution and system temperature are of crucial importance to build cascade NEAS.  
The lift of system temperature may transform a NEAS displaying cascade characteristics into a single-stage NEAS. The higher the system temperature, the larger the critical pore difference for NEASs to display cascade characterizes. In DNEAS, the infiltration process of water molecules into two tubes are independent to each other. In SNEAS, water molecules will lose some hydrogen bonds during invading and transporting through the bottom tube, the critical infiltration pressure of the top tube was lower than that of the corresponding single tube. The fitting correlation between critical pore difference and system temperature was \(\Delta d^{\rm D}_{\rm cr}=0.067T-13.259+(18.317-0.087)/(1+e^{(d_{\rm large}+0.01 4T-25.352)/(0.025T-6.182)})\) for DNEAS, and \(\Delta d^{\rm G}_{\rm cr}=0.07T-14.287+(0.073T-16.409)/(1+e^{(d_{\rm bottom }+0.02T-27.28)/(0.015T-3.484)})\) for SNEAS. It is hoped to be helpful for the design and application of cascade NEAS.  
Cascade NEAS exhibited greater potential for application than single-stage NEAS. The infiltration of water molecules into nanopores, as discussed in this study, is driven by pressure. By manipulating system temperature as a driving force, it is possible to achieve hierarchical energy storage and release through control of said temperature.  
Conceptualization, Y.Z. and Y.D.; methodology, Y.Z. and H.X.; validation, H.W. and H.X.; formal analysis, H.X. and S.L.; investigation, H.W.; resources, W.W.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, Y.Z.; supervision, Y.D.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 10: The total energy absorbed of a certain tube in the constructed NEAS at different temperatures. (**a**) Total energy absorbed by the large tube; (**b**) total energy absorbed by the small tube; (**c**) total energy absorbed by the bottom tube; (**d**) total energy absorbed by the top tube.  
**Funding:** This research study was funded by National Natural Science Foundation of China (grant No. 51974246 and 51806173), and Innovative Talent Promotion Program \"Young Science and Technology Star Project\" of Shaanxi Province in China (grant No. 2021 KJXX-40).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 DNN Performance and Numerical Results
Figure 6 compares the average sum rate of the proposed DNN model-based scheme and other reference schemes, where the feedback size are 6 bits and 10 bits in Figure 6a,b, respectively. For comparison, we consider the following five reference schemes:  
* Optimal scheme: The optimal dimensions of the outer precoder are obtained via brute-force numerical search. * Full rank-based scheme: The dimensions of the outer precoder are the number of ranks of covariance matrices. * Lower bound-based scheme: The dimensions of the outer precoder are obtained by the lower bound-based analysis and the solution of problem **P3**. * Fixed dimension scheme 1: The outer precoder dimensions are fixed to five, i.e., \(s_{1}=\cdots=s_{G}=5\). * Fixed dimension scheme 2: The outer precoder dimensions are fixed to eight, i.e., \(s_{1}=\cdots=s_{G}=8\). For performance comparison, we average out 100 covariance realizations varying the angular spread. In Figure 6a, the proposed scheme outperforms the reference schemes and is comparable to the optimal scheme. The reason is that the dimensions obtained by the proposed DNN framework are closer to the optimal dimensions. Note that the lower bound-based scheme increases the average sum rate compared to the other reference schemes, (i.e., full rank-based scheme, and fixed dimension scheme 1 and 2), and it is lower bounded on both the optimal and the proposed schemes. In Figure 6b,  
Figure 5: Training state and performance of the proposed DNN model. (**a**) the cross entropy loss with respect to the number of iterations; (**b**) the accuracy with respect to the number of iterations; (**c**) the accuracy with respect to the number of ranks. as expected, the proposed scheme outperforms the reference schemes and achieves near-optimal performance. The gap between the proposed scheme and other reference schemes is different from those from the gap in Figure 6a. This is because the optimal dimensions are affected by feedback bit allocations. Consequently, we conclude that the dimension of the outer precoder is optimized to maximize the average sum rate and the proposed DNN model-based scheme performs well and is comparable to the optimal scheme.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 4 Modeling Time Series Using LSFCM
### Constructing the LSFCM Model
Any datum \(\overline{x}(t)(t=1,2,..n)\) belonging to the time series can be transformed into the form of fuzzy membership values \(u_{it}(\ i=1,2,\cdots c;t=1,2,\cdots n)\) by the fuzzy c-means algorithm. When the structure of the LSFCM is formed, the dynamic characteristics of the given time series can be interpreted or predicted by the LSFCM. We can predict future fuzzy membership values based on the LSFCM model. According to (19), the forecast membership value \(\hat{U}_{t}=f(U_{t-1}\mathrm{W})\), then the forecast datum, can be reconstructed as the following,  
\[\hat{\mathbf{x}}_{t}=\frac{\sum_{i=1}^{c}\hat{u}_{it}^{m}v_{i}}{\sum_{i=1}^{c}\hat {u}_{it}^{m}}, \tag{21}\]  
where \(v_{i}\) is the concept of FCM, which is calculated by the fuzzy c-means clustering algorithm. For the quantitative evaluation of LSFCM model quality, the performance index root mean squared error (RMSE) is defined by the following,  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}\|\mathbf{x}_{t}-\hat{\mathbf{x}}_{t}\|^{ 2}}\, \tag{22}\]where \(n\) is the number of time series data, \(\mathbf{x}_{t}\) is the actual value and \(\hat{\mathbf{x}}_{t}\) is the predictive value of time series at the time \(t\). Obviously, the smaller the RMSE is, the higher the quality of the model is.
## 2 Results and Discussion
### Performance of the Prepared Fibers
In the used mixture, the molecules with higher \(V_{P}\) are ketones, followed by alcohols, and later monoterenes. In terms of \(V_{M}\), (S)-(\(-\))-\(\beta\)-citronello, 1-octanol, 2-heptanone, and 2-heptanol show the largest values. Regarding \(W_{S}\), values lower than 2 g\(\cdot\)L\({}^{-1}\) are found for 1-octanol and the three monoterenes studied, in the range of (2-15) g\(\cdot\)L\({}^{-1}\) for the compounds 2-heptanone, 2-heptanol, 2-hexanone, and cyclopentanone, and only with \(W_{S}\) higher than 43 g\(\cdot\)L\({}^{-1}\), there are benzyl alcohol, 3-pentanone, and 1-butanol. From Figure 6b, it can be observed that the peak areas detected for each compound are closely related to their \(W_{S}\). Moreover, it can also be concluded that after \(W_{S}\), \(V_{M}\) also influences the most detected compounds, which are those with the largest molar volumes, such as 2-heptanone and 2-heptanol. These two facts point out that this fiber is the more suitable to detect a wide range of compounds, from bulky non-polar compounds to small polar molecules. Interestingly, when comparing the total detected areas obtained with the pD8 and PDMS fibers depicted in Figure 6a, it can be seen that they are very similar. However, when the individual  
Figure 6: Comparison and performance of the prepared fibers along with commercial fibers in terms of: (**a**) total detected areas and (**b**) individual compound area. (\(T\) = 45 \({}^{\circ}\)C, \(t\) = 15 min, 2.5 wt % of NaCl, 200 rpm, concentration: 200 mg\(\cdot\)L\({}^{-1}\)). compounds areas are considered in Figure 6b, distinct behaviors can be observed: for smaller alcohols and ketones, pD8 fiber showed the best results; for 1-octanol similar results were obtained for the two fibers; for monoterpenes, PDMS fiber showed higher detections than pD8. This might indicate that, even for compounds that have high water solubility such as benzyl alcohol, 3-pentanone, and 1-butanol, and thus lower headspace concentrations, pD8 showed higher sensitivity. Moreover, even pD2 fiber achieved higher sensitivities for these polar compounds than the two commercial fibers.
## 4 Energy-Aware Routing Protocol
### Active Member Nodes Selection  
Coverage is one of the most important issues in WSNs and it has been studied extensively in recent years [16-18]. In most cases, \"coverage\" means area coverage. And K-coverage can be described as that every point in the monitored field is covered by at least K sensors. In [16], the authors consider that it is hard to guarantee full coverage for a given randomly deployment area, even if all sensors are on-duty. Small sensing holes are not likely to influence the effectiveness of sensor networks and are acceptable for most application scenarios. It's enough to meet the application's requirements if the active nodes in the network could maintain reasonable area coverage--coverage expectation. Coverage mechanism is to choose a subset of active nodes to maintain the coverage expectation.  
We introduce into clusters the notion of \"intra-cluster coverage\", which selects some active nodes within clusters while maintaining coverage expectation of the cluster. Utilizing the idea proposed in our research [19], cluster head randomly chooses m' nodes according to equation (3):  
\[p_{\rm{cover}}=\sum_{i=K}^{\infty}C_{i}^{i}\left(\frac{r}{R}\right)^{2i}\left( 1-\frac{r^{2}}{R^{2}}\right)^{\rm{m}-i} \tag{3}\]  
where P\({}_{\rm{cover}}\) is the coverage expectation of sensing field determined by specific applications; and r is sensing radius, R is cluster radius; m' is the number of active nodes. For example, distributing 200 nodes in a 100\(\times\)100m\({}^{2}\) field, r = 12m, R = 30m, then the average number of cluster members is 60 or so. With intra-cluster coverage, if P\({}_{\rm{cover}}\) = 99% which means 99% of sensing field is expected to be monitored, 27 members should be active in each cluster to ensure 1-coverage of the cluster and 38 members to ensure 2-coverage. If P\({}_{\rm{cover}}\) =95%, only 16 nodes and 25 nodes should be active to ensure 1-coverage and 2-coverage respectively.  
Use of intra-cluster coverage has three advantages. The first is to reduce energy consumption in each round by turning redundant nodes' radio off so that network lifetime is prolonged. The second is to reduce TDMA schedule overhead. Once clusters are grouped, all cluster heads broadcast a TDMA schedule packet which contains the members' ID and the slot number allocated to the member. When node density is high, the number of cluster members turns higher so that the length of TDMA schedule packet becomes longer and that means more energy will be consumed to transmit and receive it. However, the length of TDMA schedule packet would not too long with intra-cluster coverage because the number of active node varies slightly when node density goes higher. Apparently, through intra-cluster coverage, EAP can function as a topology control protocol but does not pay any extra energy cost.
# Design, Implementation, and Field Testing
## 5 Field Study
### Usability Survey Result
A main lesson that we learned from the field study is that the usability of the system must be improved significantly because FACTS is to be used by nursing assistants with no training in engineering. The introduction of the lease-based mechanism improved the usability of the system. However, the use of the smartphone as the bridge between the communication of the smartwatch and KinectServer still proved problematic. The manual registration requirement could also be improved. In future work, we plan to switch to Android smartwatch that is capable of communicating with the KinectServer using WiFi so that the smartphone will not function in the critical path of the operation of FACTS. In a lightly longer term, we aim to introduce a biometric-based auto-registration mechanism, for example, based on speaker identification, to further improve the usability of FACTS without compromising the privacy-protection of patients. **7. Patents**  
Part of this paper contains information included in a US patent application 20160381328, titled \"Systems and Methods for Privacy-Aware Motion Tracking with Notification Feedback\", where [PERSON] is the inventor [45]. **Acknowledgments:** We thank the anonymous reviewers for their invaluable comments and suggestions on an earlier version of this paper. This study is supported in part by a grant from the Ohio Bureau of Workers' Compensation (SPC1004933944), by an award for undergraduate research, a graduate faculty travel award, a faculty research development (FRD) award, and a FRD-Internet of Things award, all from Cleveland State University. The authors received no funding to publish open-access. **Author Contributions:** [PERSON] conceived and designed the system; [PERSON] and [PERSON] researched and finalized the requirement for using PACTS in nursing homes; [PERSON] provided instrumental suggestions on the user interface of the system; [PERSON], [PERSON] and [PERSON] collected and analyzed the experimental results; and [PERSON], [PERSON] and [PERSON] wrote the paper. **Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 3 Hysteresis Based Pulse Width Modulation Schemes  
This chapter introduces the \(\Delta\Sigma\)-PWM as well as the DTC scheme. When using a classical modulation scheme, like a carrier-based PWM or the space vector modulation, a fixed switching frequency is applied [25; 26]. In contrast, hysteresis based schemes introduce new switching events only if the control error crosses predefined boundaries. This leads to a non-uniform switching frequency [11].
## 4 Conclusions  
This paper numerically investigates the effect of the fuel mass fraction on the combustion and fluid flow in a SRU thermal reactor to provide a guideline for adjusting the fuel mass fraction to reduce the temperature inside the thermal reactor and to allow an acceptable sulfur recovery. Practical operating conditions for a petrochemical corporation in Taiwan are used as the design conditions. To determine a suitable fuel mass fraction for operation, a detailed numerical simulation should be performed first to find the stoichiometric fuel mass fraction which produces the most complete combustion and the highest temperature. This stoichiometric fuel mass fraction should be avoided because the high temperature could damage the zone 1 corner or the choke ring. A higher fuel mass fraction (i.e., fuel-rich or air-lean condition) is more suitable because it can avoid deteriorations of both zone 1 and the heat exchanger tubes. Although a lower fuel mass fraction (i.e., fuel-lean or air-rich condition) can avoid deterioration of zone 1, the heat exchanger tubes may be damaged. The present simulation results show that the current design condition is a fuel-rich (or air-lean) condition and allows an acceptable thermal reactor temperature for an oxygen-normal operation. However, for an oxygen-rich operation, the local maximum temperature exceeds the suggested maximum service temperature, although the average temperature is acceptable. The high temperature regions, such as the zone 1 corner, must be inspected very carefully during the annual maintenance period if there are oxygen-rich operations. For lower zone 1 fuel mass fractions such as 0.0625 and 0.125, the average temperature in zone 2 is higher than the average temperature in zone 1, which could cause damage to the downstream heat exchanger tubes. If a low zone 1 fuel mass fraction is used to produce a lower zone 1 temperature, the temperatures in zone 2 and the heat exchanger section must be monitored closely and the zone 2 wall and heat exchanger tubes must be inspected very carefully during the annual maintenance period.  
**Acknowledgments:** The author gratefully acknowledges the grant support from the Ministry of Science and Technology, Taiwan, R.O.C., under contract MOST 104-2221-E-150-032. The author would also like to express thanks for the useful data and constructive suggestions provided by the Formosa Petrochemical Corporation in Taiwan.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 7: A comparison of the (**a**) sulfur and (**b**) sulfur dioxide concentrations at the exit for a SRU thermal reactor with different zone 1 fuel mass fractions.
# Machine Translation in Low-Resource Languages by an Adversarial Neural Network
## 3 Adversarial Model
### Lac  
As depicted in Figure 1a, the entire GAN system is composed of a discriminator D and generator G, which play minimax games with each other. Two adversarial losses are used to optimize the parameters of G and D in turn. G yields fake samples to confuse the discriminator D and adjusts its parameters according to the recognition in terms of D. In contrast, the goal of the discriminator D is to identify the fake samples generated by G as accurately as possible and adjust its parameters accordingly. Adversarial training and GAN are different concepts. A GAN is used for unsupervised learning, which can generate explicit images without human intervention. Our proposed Lac model is classified as supervised learning. We incorporated the rival losses of a GAN for machine translation because they were helpful for LRL translation.  
The Lac model comprises a generator G and a discriminator D, as shown in Figure 1b. The source language and human translations are embedded by a public Multi-Layer Perceptron (MLP). An MLP is a class of feed-forward neural networks. It can be comprised of different layers, and its purpose is to map the one-hot representation of a token into context embedding, which aligns with the work done by [PERSON] et al. [32]. Here, we utilize a 1-layer feed-forward neural network for simplicity. The public feed-forward network is used for the source and target languages. To avoid underrepresenting, we setthe hidden units to 5000. We define the distribution of the source language \(Yz\), human translation \(Yr\), and generated translation \(Yg\). The inputs to the discriminator are \((Yz,Yg)\) and \((Yz,Yr)\) in turn, yielding two types of adversarial losses \(Lg\) and \(Lr\), respectively, as shown in Figure 1b. The distribution of \((Yz,Yg)\) and \((Yz,Yr)\) is as close as possible, based on WGAN-GP. That is, embedding \(u\in Yz\) with distribution \(u\sim p_{Yz}(u)\) will be mapped into the domain \(\hat{v}\in Yg\) with distribution \(\hat{v}\sim p_{Yg}(\hat{v})\), \(\hat{v}=G(u)\).  
The distribution of \(Yg\) and \(Yr\) is also as close as possible. That is, \(\hat{v}\in Yg\) approaches \(v\in Yr\) with distribution \(v\sim p_{Yr}(v)\) as close as possible. We constrain \(v\), \(\hat{v}\), and \(u\) in the same dimension. The adversarial losses \(Lg\), \(Lr\) are generated from \(D\) to measure the Wasserstein distance of \((Yz,Yg)-(Yz,Yr)\). The translation consistency loss \(Ls\) measures the distance of \((Yg)-(Yr)\). The objective function is expressed as:  
\[\begin{