esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Mode-Switching Quality of Hybrid Tractor Equipped with HMCVT
## 2 Transmission System of Hybrid Tractor  
### Transmission Mode Analysis  
The hybrid speed-coupling mode is a working mode in which the engine and the motor drive the vehicle together. When the speed required by the vehicle exceeds the maximum speed of the engine and the power supply is sufficient, the system will adopt the hybrid speed-coupling mode to meet the higher speed requirements [13]. In this mode, the engine and motor work together to provide additional power support to ensure that the vehicle can maintain stable and efficient power output at higher speeds.  
The transmission route of the hybrid speed-coupling mode is shown in Figure 1. The clutches C0, C3, C4 and C6 participate in this mode. The speed of the motor part and the speed of the engine achieve speed coupling before the front planetary gear mechanism, and the coupled speed is input to the sun wheel of the rear planetary bank, and then coupled again with the speed of the hydraulic part to achieve speed coupling. The speed relationship between its input and output is as follows:  
\[n_{o}=\frac{(k_{1}+1)n_{e}-\frac{n_{m}}{i_{1}i_{2}}}{k_{1}(k_{2}+1)}+\frac{k_ {2}en_{m}}{(k_{2}+1)i_{1}i_{2}i_{5}i_{6}} \tag{1}\]  
where \(k_{1}\) and \(k_{2}\) are the characteristic parameters of the front and back planetary rows; \(e\) is the displacement ratio of pump and motor; \(n_{e}\) is engine speed (r/min); and \(n_{m}\) is the motor speed (r/min).  
The pure electric high-grade mode is driven by the motor alone, and is mainly used when the tractor demand torque is small and the battery power is sufficient. The tractor starts with the pure electric high-grade mode, which is suitable for make the tractor start quickly as the demand power is small, the endurance is short, and the precision is high.  
The transmission route of pure electric high-end mode is shown in Figure 2. When clutches C2 and C7 are engaged, the power provided by the electric motor passes through  
Figure 1: Hybrid speed-coupling mode transmission route.  
clutch C7 and fixed gear i3 and then enters the ring gear of the rear planetary row. Because clutch C2 is engaged, the rear planetary row gear mechanism is solidly connected as a whole, and the speed relationship between its input and output is as follows:  
\[n_{o}=\frac{n_{m}}{i_{3}i_{4}} \tag{2}\]  
where \(n_{\text{o}}\) is the output shaft speed (r/min); \(n_{m}\) is the motor output speed (r/min); and \(i\) is the gear pair ratio.
## 5 Case Study
### Scenario 1
#### 5.1.2 Optimal Results on Different Days for One Week  
According to the proposed method in this paper, the optimal results in different days for one week are obtained by simulation based on forecasted data, which are shown in Table 3.  
In a week, there may be variations of weather and load characteristics, and the optimal scheduling can be achieved, which indicates the proposed method has better performance and can be applied to different days.  
Due to the space constraints, here we choose two typical days (Day 1 and Day 2), whose optimal results are given in detail, respectively.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**RES** & **Rated Capacity (kW)** \\ \hline WT1 & 500 \\ PV1 & 500 \\ PV2 & 800 \\ WT3 & 500 \\ PV3 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the renewable energy sources (RESs).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**BESS** & **Rated Power (kW)** & **Rated Capacity (kWh)** & **\(I\) (Yuan)** \\ \hline BESS1 & 250 & 800 & 800,000 \\ BESS2 & 350 & 1000 & 1,000,000 \\ BESS3 & 400 & 1200 & 1,200,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the battery energy storage systems (BESSs).  
Figure 4: Structure of the MGs in coalition. PV: photovoltaic; WT: wind turbine.
## 6 Conclusions  
This study applies machine learning and model evaluation methods to the analysis of slope stability. Based on 102 slope sample data, 6 parameters related to the arc-shaped slope instability are selected as sample features, and 8 regression machine learning methods are trained and verified to predict FOS. The prediction results of FOS by different machine methods show obvious differences.  
The results of eight machine learning methods are evaluated by four accuracy evaluation indicators, including _MSE_, _RMSE_, _MAE_, and _Pearson correlation_, based on the entropy weight-TOPSIS and the CRITIC-TOPSIS models. The ANN and RF models show better performance in predicting FOS. Their comprehensive scores in the two evaluation methods are greater than 0.84, which is much higher than the comprehensive scores of other machine learning models. In addition, the ensemble methods show high accuracy.  
The prediction results of ANN are used to compare the FOS of the Sino-Russian border natural gas pipeline calculated by the numerical simulation method. The obtained FOSs of slopes under different water content are close, while the error is related to the choice of data set and the difference in analysis methods, which proves the effectiveness and feasibility of the machine learning model.  
Conceptualization, G.B. and D.S.; methodology, G.B. and Z.T.; software, Y.Z.; validation, B.W., N.A. and Y.Y.; formal analysis, Y.Y. and M.Y.; investigation, Y.Z. and M.Y.; resources, G.B.; data curation, Y.Z. and N.A.; writing--original draft preparation, G.B. and D.S.; writing--review and editing, G.B. and D.S.; visualization, Z.T. and Y.Z.; supervision, Y.H.; funding acquisition, G.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The 2021 Innovation Fund Project of Gansu Provincial Bureau of Geology and Mineral Resources, grant number 2021 CX13.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Water Content (\%)** & **Slope Layer** & \(\gamma\) **(kN/m3)** & \(c\) **(kPa)** & \(\varphi\) (\({}^{\circ}\)) & \(\beta\) (\({}^{\circ}\)) & \(h\) **(m)** & **ru** & **FOS** \\ \hline \multirow{4}{*}{6} & Sand-gravel layer & 13.20 & 8 & 8 & 40 & 120 & 0 & 1.05 \\  & Strong Weathered Andesite & 15.75 & 215 & 215 & 40 & 120 & 0 & 1.40 \\  & Sand-gravel layer & 13.75 & 85 & 85 & 40 & 120 & 0 & 1.38 \\
10 & Strong Weathered Andesite & 16.35 & 195 & 195 & 40 & 120 & 0 & 1.40 \\  & Sand-gravel layer & 15.63 & 9 & 9 & 40 & 120 & 0 & 1.08 \\
25 & Strong Weathered Andesite & 18.56 & 130 & 130 & 40 & 120 & 0 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Safety factor value based on ANN method.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.3 Caching Strategy  
The _caching strategy_, or _cache replacement strategy_, decides what element should be evicted when the Content Store of a node is full. As illustrated in Section 3.2, the IoT is subject to severe resource constraints, so using simple data structures and low-complexity algorithms for caching is desirable. The presented caching strategies are _Random_, _LRU_, _LFU_, _LRFU_, _FWF_, and _FIFO_.  
_Random_ is a trivial strategy that uniformly draws a random number from the set \(\{1,2,\ldots,\text{CS size}\}\) and evicts the element with that index. This makes the strategy computationally inexpensive.  
_LRU (Least Recently Used)_ keeps track of the last time each element has been requested. It evicts the element that has not been requested for the longest time by maintaining a timestamp that is incremented for every element access. The current timestamp is assigned to the requested element, and the element with the smallest assigned timestamp value is removed.  
_LFU (Least Frequently Used)_ is similar to LRU in that it considers the recent popularity of an element but instead maintains a counter that keeps track of the number of accesses for each element. The strategy is good at recognizing data that becomes popular through many recent accesses.  
_LRFU (Least Recently/Frequently Used)_[27] can be considered in between the extremes of LRU and LFU: While LRU gives weight only to the most recent access of an element, LFU gives equal weight to all accesses of an element (the frequency). LRFU, on the other hand, compromises between these two ideas by giving more weight to more recent accesses and less weight to less recent ones. This can be controlled by the weighing function \(F(x)=\left(\frac{1}{2}\right)^{\lambda\cdot x}\). The parameter \(\lambda\) ranges from 0 to 1 and allows a trade-off between recency and frequency. As \(\lambda\) approaches 0, LRFU is more frequency-inclined, whereas \(\lambda\) approaching 1 leads to a more recency-based policy. This function is used to compute the so-called _CRF (Combined Recency and Frequency)_ value. Given the current time \(T\), the element \(b\) to consider and its \(k\) reference times \(t_{b_{1}},\ldots,t_{b_{k}}\), the CRF value can be computed as follows:  
\[C_{T}(b)=\sum_{i=1}^{k}F(T-t_{b_{i}}) \tag{1}\]  
_FWF (Flush When Full)_ deletes all elements in the Content Store when it is full and inserts the new element after that.  
_FIFO (First-In First-Out)_ maintains a queue of elements for the Content Store. The queue first removes the element that was added first to the Content Store. While this strategy is similar to LRU, FIFO does not keep a recency counter for each element. So, accessing an element already present in the Content Store does not affect the position in the queue.
## 4 Discussion
### AoR Estimation by Means of Helical Axis Is Significantly Affected by Time Discretization
However, the results obtained by choosing the different weighting factors do not clearly support the use of one weight rather than the other. In general, the corrections help in achieving a more confined set of helical axes; however, they may compromise the identification of the correct location of the AoR.
## 4 Discussion
### Generation of the Digital Twin  
Once a path is found, it can be used to build a graph that serves as the basis for the digital twin. In the examples from Section 3, robust derivations of the twin were shown. For this purpose, the paths were supplemented with additional information such as the automatic path width or the manual specification of turn times. A change in the environment shows a change in the digital twin. Especially worth mentioning is the locality of the process. For example, if a new obstacle is suddenly found on the map, the paths and the robot commands only have to be adjusted locally. As a result, all unaffected robots can perform their task without modification. In addition, the path width specification offers the possibility to let opposing robots drive (left, right) if they can drive side by side including the size of their payload. Based on this twin, a state diagram could be created to evaluate drivability. Likewise, based on this, the position of the robots along the paths can be directly recorded. If repositioning is necessary, the individual paths for the paths of the robots can be generated locally without affecting the global paths of the digital twin. This combination of robust path generation, but also the possibility of paths for repositioning that do not affect the original paths, results in a robust twin. Since bridges, overpasses, or underpasses may also be present in the application, the procedure in Figures 22-24 were extended to 3D. The results show reasonably automated path generation even for 3D maps. However, the biggest drawback is the need for accurate RGB coding in the map and an accurate map layout. In real-world applications, a variety of artifacts can be caused by different sensors. In addition, these errors are not accounted for in the procedure described above, so further approaches to filtering the errors and capturing multiple maps has to be pursued in the future.  
In summary, path generation using anchor point shrinkage in combination with distance width allows the construction of robust digital twins. Local changes such as people on the roadway or repositioning of a robot also affect the map only locally. This results in paths that are centered, which provides maximum space for recalibration, but can also result in predictable driving behavior similar to rules such as on the roadway.
###### Abstract  
The hydraulic support is one of the most crucial pieces of equipment at the working face. To achieve the intelligization of the attitude-monitoring system, we have designed and developed a Fiber Bragg Grating (FBG) inclinometer for the hydraulic support. This innovation offers a brand-new monitoring tool and approach for measuring the attitude angle of the hydraulic support. The FBG inclinometer for the hydraulic support integrates passive grating sensing technology with an inclination force element. It not only fulfills the inclination measurement function but also employs passive sensing technology, rendering it safer and more reliable compared to electromagnetic inclinometers. First, we delved into the sensing principle of the grating based on its structure, and investigated its sensing characteristics under uniform axial stress and temperature variations. We analyzed the strain-temperature cross-sensitivity issue and applied a temperature compensation technique. Second, we carried out a novel structural design and proposed two design alternatives: the cantilever beam type was selected after a comprehensive comparison. Subsequently, we deduced the corresponding theoretical formulas and ultimately adopted the temperature compensation method using an unstressed reference grating. Finally, on-site verification was conducted on the hydraulic support in the general mining face of Delong Mine, and the FBG inclinometer successfully passed the test. Finally, an actual test was carried out at the Delong Coal Mine site, and the subsequent use yielded quite satisfactory results. An analysis of the data collected on-site by the FBG inclinometer for the hydraulic support revealed that the newly developed FBG inclinometer for the hydraulic support can be effectively applied in the field of intelligent monitoring in underground coal mines. The monitoring data can serve as a reliable data foundation for assessing the operating attitude of the hydraulic support. This indicates that the FBG inclinometer is highly suitable for wide-scale industrial applications.  
fiber-optic grating; FBG inclinometer; hydraulic support; intelligent monitoring Article
## 3 Evaluation with Expected Total Costs
### Probability Analysis  
In this paper, the load, capacity, and estimates of capacity are treated as random variables. By assuming that both the load and capacity follow a lognormal distribution, the probability of failure can be solved analytically, making the calculations of the probability much simpler than running Monte Carlo simulations. Model errors in the estimation of the pile bearing capacity from the geotechnical investigation [45] and piling data [46] were reported to follow log-normal distribution. Meanwhile, the model for the probability distribution of the load depends on the type of load: normal distribution for dead load, lognormal for annual wind load, and Gumbel for wind load [47]. In this study, the dead loads with small variations are basically targeted, although loads with large variations are examined in Section 4.4. The failure probability and reliability index, \(\beta\), can be calculated analytically [48]:  
\[P_{f}=\Phi(-\beta) \tag{3}\]  
\[\beta=\frac{\ln(m)}{\sqrt{\ln\left[\left(1+\text{COV}_{R}^{2}\right)\left(1+ \text{COV}_{Q}^{2}\right)\right]}} \tag{4}\]  
where COV is the abbreviation for the coefficient of variation, subscripts \(R\) and \(Q\) represent the capacity and load, respectively, \(m\) is the median of the ratio of the capacity to the load, and \(\Phi\) is the cumulative distribution function (CDF) of the standard normal distribution. Note that the assumption of lognormal distribution may lead to an amplified error of \(P_{f}\), especially for scenarios with low failure probabilities, and the probability distribution should be determined based on the project condition. The median, \(m_{x}\), and standard deviation, \(s_{x}\), of the lognormal variables are converted from the mean, \(\mu_{x}\), and standard deviation, \(\sigma_{x}\), as follows:  
\[m_{X}=\mu_{X}/\exp\left(\frac{1}{2}s_{X}^{2}\right) \tag{5}\]  
\[s_{X}=\sqrt{\ln\left[1+\left(\frac{\sigma_{X}}{\mu_{X}}\right)^{2}\right]} \tag{6}\]  
where subscript \(X\) represents variables.
## 5 Conclusions  
In this paper, we propose an automated methodology to predict severe sleep apnea, using a deep neural network model with scaled PCA based on the questionnaire items, including demographic information and existing forms, such as Berlin, NAMES, No-Apnea, No SAS, and STOP-BANG. The most significant finding of this study is the early detection of patients who have severe sleep apnea and need an additional checkup and appropriate treatment. The proposed method can minimize the efforts in designing, computing, and selecting the appropriate features for survey-based diagnoses. Since the used dataset has a limited number of categories, we applied DNN to train the categorical/binary type of input variables and used scaled PCA to convert them into continuous features as proper inputs for the DNN.  
Consequently, this study has demonstrated that integrating a DNN with PCA provides a robust method for predicting severe OSA. By optimizing and reducing the number of input variables, our model achieved high accuracy, sensitivity, and specificity, outperforming existing screening tools and models. The key contribution of this research lies in its ability to streamline the prescreening process, making it more efficient and practical for clinical application, particularly in resource-limited settings. This work not only advances the field of OSA diagnostics but also sets the foundation for future research aimed at further enhancing predictive performance and broadening the model's applicability to diverse populations.  
In future studies, we plan to modify and apply the proposed method to other survey-based datasets with larger populations, such as those for chronic obstructive pulmonary disease and hypopnea. In addition, we plan to employ the proposed method for patients with mild/moderate sleep apnea within large populations. Moreover, auto-fine-tuning methodologies to reduce the model training time and improve its overall performance will be developed and validated for other applications.  
Conceptualization, J.K.; Writing--original draft, J.K.; Writing--review & editing, J.P. (Jaebyun Park), J.P. (Jangwoon Park) and S.S.; Funding acquisition, J.P. (Jaebyun Park). All authors have read and agreed to the published version of the manuscript. This work was supported by the Incheon National University Research Grant in 2023. The Executive Committee Torr Sleep Center approved this study (PT #02-2020). All subjects gave their informed consent for inclusion before they participated in the study. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
## 6 Results and Discussion
### Discussion  
In the analysis of delay and distance, first, the delay for 100 nodes utilizing BEA-SSA at round zero is just 1 \(\times\) 10\({}^{-7}\). At the 2000 th round, for example, the delay value for 100 nodes using the BEA-SSA approach is 1.5 \(\times\) 10\({}^{-7}\) s. Similarly, as the number of rounds  
Figure 11: Throughput analysis using BEA-SSA over other models [20; 21; 22; 26] for node count of (a) 100 (b) 200.  
Figure 12: Trust analysis using BEA-SSA over other conventional models [20; 21; 22; 26] for node count of (a) 50 (b) 100.  
Figure 13: Security analysis using BEA-SSA over other conventional models [20; 21; 22; 26] for node count of (a) 50 (b) 100.  
rises, so does the distance. In fact, for the system to operate well, the packet transmission latency must be as minimal as possible. Table 4 shows that in the best-case scenario for 200 nodes, the suggested BEA-SSA approach has significantly reduced residual energy values (\(-0.00058\)). It is because BEA-SSA-based CHS was implemented and IDCNN was used to improve energy evaluation and prediction. For both nodes, the BEA-SSA has attained the least cost values as needed with an increase in iteration. The throughput is crucial for data transport; hence it must be high. For 100 nodes, the throughput peaks at the 750 th round, while for 200 nodes, it peaks at the 1000 th to 1250 th rounds. In round 12, the trust factor is high at first, but it decreases as more rounds are played. The BEA-SSA model, however, poses high trust and the security risk is lower for the BEA-SSA approach; however, it is higher for the GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20]. Moreover, the BEA-SSA model posed significant residual energy, high RSSI, and high PDR compared to GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, and RCSO [21]. The best CH in WSN is chosen by predicting the energy level using an improved Deep Convolutional Neural Network (DCNN). The BEA-SSA is used to choose the WSN's top CH. The Bald Eagle Assisted SSA (BEA-SSA) model has high RSSI, PDR, and residual energy. These improvements are attributable to better energy evaluation and prediction using DCNN and the installed BEA-SSA-based CHS.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 2 Results and Discussion
### UV/TiO\({}_{2}\) Photocatalysis of Real Poultry Atmosphere
#### 2.2.3. Coeffect of Spontaneously Produced Ozone  
Ozone was not introduced intentionally into the system, and the first sign of its presence was the typical \"swimming pool\" odor perceived by the operator at the exit of the reactor. Ozone measurements then performed with and without the presence of the TiO\({}_{2}\) catalyst. No ozone was detected without a catalyst; under these conditions (no catalyst), VOCs were not removed as effectively. Ozone was detected at concentrations in the range of 1-2 ppm after treatment, including a catalyst (none before treatment). Since some of the ozone presumably reacted with the VOCs in the photoreceptor, this ozone is considered \"residual\". Such levels of residual ozone produced under the UV\({}_{365}\)/TiO\({}_{2}\) system might need to be treated before being released into the environment and deserves safety considerations.  
UV light at 365 nm does not provide the energy needed to split molecular oxygen (O\({}_{2}\)); that is only available at 185 nm. However, under TiO\({}_{2}\) photocatalytic conditions, a variety of reactive oxygen species can be produced, notably including hydroxyl radical and potentially, though indirectly, O\({}_{3}\). Notably, spontaneously produced O\({}_{3}\) was not reported in previous studies on VOC treatment by photocatalysis in the 365 nm/TiO\({}_{2}\) systems. It may be that other mechanisms of O\({}_{3}\) generation play a role in real manure offgas compared to synthetic air mixtures of a few compounds. As known from atmospheric chemistry, O\({}_{3}\) is generated in the presence of NOx and other VOCs (NO\({}_{x}\) photodissociates at \(<\)420 nm) [46; 47]. The poultry manure suspensions used in this study as the source atmosphere evidently contained many VOCs and possibly NOx (not measured). Ammonia, NOx, and other VOCs in the manure atmosphere were likely involved [48]. Thus, O\({}_{3}\) formation was possibly a combination of both NOx and VOC photodissociation plus reactions with OH radicals that were formed in the 365 nm/TiO\({}_{2}\) system [47]. [PERSON] et al. (2010) [49] reported the possibility of O\({}_{3}\) formation from illuminated TiO\({}_{2}\) surfaces. If this is the case, then O\({}_{3}\) becomes an inevitable byproduct in the 365 nm/TiO\({}_{2}\) system treating barn emissions, and it would be difficult to elucidate the specific contribution of UV/TiO\({}_{2}\) and that of O\({}_{3}\) to the overall odor elimination process.