esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al. [13] compared the effect of shape of absorber (fin), angle of tubes and arrangement of tubes on the performance of 4 models of evacuated-tube solar collector. They concluded that the shape of absorber was the most influential for the performance of the tube. U-tube welded inside a circular fin (model II) provided the best performance. However, the incidence direction of solar radiation should be considered in the design of evacuated tube [14]. In the industry application part, [PERSON] et al. [15] reported that the use of the evacuated-tube solar collector with 9 tubes to produce a 45 \({}^{\circ}\)C, 120 L of hot water per day in a domestic service could save 81.7% of fossil fuel compared with non-solar energy system. [PERSON] et al. [16] found that the evacuated tube solar collector integrated with a gas boiler in an absorption cooling system could generate 57% of total thermal energy. [PERSON] [17] designed the 110 m\({}^{2}\) of the evacuated-tube solar collector system to produce 4000 kg of hot water per day at 80 \({}^{\circ}\)C for the syrup preparation process in the soft drink industry. The annual lifetime saving was estimated as USD 900 per year. [PERSON] et al. [18] reported that the integration of the evacuated-tube solar collector with the continuous multi-period heat exchanger in a chemical industry could reduce the total annual operating cost around USD 650,000 per year. [PERSON] et al. [19] evaluated the design of the evacuated-tube solar collector network in industry and reported that the number of tubes in series and parallel lines was defined by the target temperature and total mass flow rate, respectively. [PERSON] et al. [20] introduced the optimal number of evacuated tube chart used to match the rise of hot water temperature at different water mass flow rate, inlet water temperature, and solar irradiance for facilitating solar absorption chillers. However, the effect of storage volume on storage temperature, the development of a predictive model aimed for industrial applications, and the combination of experimental and modeling works have been rarely investigated. The main objective of this work is to evaluate the effect of modifying the inner structure of commercial grade evacuated tubes to improve the ability to transfer heat absorbed from the sun radiation to heat the heat transfer fluid stored in the heat pipe using a cheap and readily available material like stainless-steel scrubbers. In this study, the mechanistic ordinary differential equation model was set up and validated with the experimental results conducted from the 20-ETSC set at different heating medium volumes. This model was then used to predict the performance of the modified ETSC system and evaluate the feasibility of applying the modified ETSC in a small-scale industry in terms of payback period and economic worthiness.
## 9 Insight on Homo- and Heterofermatative LAB
### Homofermentative LAB  
The most commonly available starter culture contains homofermentative LABs, which are robust and efficient to produce lactic acid and thus improve the quality of silages. _Lactinplatibacillus plantarum, Lactobacillus acidophilus, E. ficeium, P. acidilactici_ and _P. pentosacous_ are the most popular LAB species [67]. Homofermentative bacterial groups are more powerful than heterofermentative ones. The significance of homofermentative bacteria is that they can catalyze each glucose molecule of lactic acid and yield high dry matter with less energy reduction for silages. Lactic acid is a more potent acid that can reduce silage pH more than other acids. The final pH is increased by a scale-up process of heterofermenters. Native bacterial populations are highly different across plant environment ecosystems. The addition of homofermentative bacterial inoculum can reduce the pH very fast while inhibiting other harmful bacterial contamination and storing plant proteins. The homofermentative inoculum can increase animal performance (3-5%). The most common microbial inoculants used for the production of silages are homofermentative LABs. The current scenario has shown that numerous bacteria are involved in the homo LAB fermentation process [16].  
Animal trials have also shown the significance of homofermentative bacteria. They play a pivotal role in silage-making and milk production, can increase animal performance, inhibit the growth of most harmful bacteria, reduce toxin production, and result in good interactions with rumen microbes [83]. The encouraging effects of homofermentative microbes used in silage for reducing dry matter losses (DMLs) and improving the synthesis of metabolites are based on significant features of target substrates and suitable environmental parameters [84]. Generally, DMLs are linked to dominant kinds of metabolic activities with the tiny loss measured by the use of homofermentative bacteria [11]. These losses always depend on the DM concentration of the silage. High moisture content increases these losses. Homofermentative bacteria are considered good for reducing such losses. This parameter is linked to the potential of the inoculants. It depends on the strain and the substrate, thus influencing silage fermentation [85].
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 3 Track-Bridge Interaction Law of Different Bridge Types
### Suspension Bridge
#### 3.5.2 Track-Bridge Interaction Law  
To study the influences on force and deformation of the bridge-track system due to the temperature differences on the main beam, tower, main cable, and suspenders, four kinds of cases are set as follows:  
Case I: Temperatures decrease by 25 \({}^{\circ}\)C on the bridge and 48 \({}^{\circ}\)C on tracks.  
Case II: Temperatures decrease by 15 \({}^{\circ}\)C on towers and 48 \({}^{\circ}\)C on tracks.  
Case III: Temperatures decrease by 40 \({}^{\circ}\)C on the main cable and 48 \({}^{\circ}\)C on tracks.  
Case IV: Temperatures decrease by 40 \({}^{\circ}\)C on suspenders and 48 \({}^{\circ}\)C on tracks.  
The results of all cases are shown in Figure 15.  
Figure 14: Model of longitudinal force.  
Figure 12: The main beam section (unit: mm).  
Figure 13: The layout of a suspension bridge (unit: mm).  
Assuming the four lines are loaded simultaneously, and the two passenger lines adopt the load of 0.8 UIC and the two intercity lines adopt the load of 0.6 UIC, the track stress, track longitudinal displacement, beam-track relative displacement, and vertical track displacement are calculated (see Figure 16). Assuming the two lines braking simultaneously are considered and the braking force ratio is 0.25, the envelope diagram of track stress and beam-track relative displacement is shown in Figure 17[57].  
Figure 16: Results of bending force.  
Figure 17: Results of braking force.  
Figure 15: Influences on rail stress due to the temperature differences on components.  
The stiffness of the large-span suspension bridge under the action of high-speed trains can meet the requirements of safety and ride comfort of high-speed trains [58]. Since the vertical stiffness of the suspension bridge is relatively small, the bending force replaces the expansion force and becomes the controlling load of the rail [57; 59].  
When calculating the deflection and breaking force of a long-span railway suspension bridge, the initial internal force of the suspension bridge and the large deformation effect of the suspension bridge have obvious effects. When calculating the braking force, the initial internal force of the suspension bridge has a significant impact, the large deformation effect of the suspension bridge is significant and cannot be ignored, while for the main cable sag, the degree effect has little influence on the beam-rail interaction and can be ignored [60].  
By installing a rail expansion device at the end of a long-span suspension bridge, the stress on the rail at the beam end and the horizontal force on the top of the brake pier can be greatly reduced [57; 59], and the viscous damper has little effect on the track stress and the relative displacement of the beam and rail [57].
## 4 Numerical Simulation and Discussion
### Governing Equations and Constitutive Models  
The physical system of dam-reservoir-foundation incorporates a non-isothermal fluid flow in a deforming porous medium. The governing equations are derived from the local equilibrium of thermodynamics and macroscopic balance equations. Thus, the liquid phase pressure p, temperature T, and displacement vector U are the corresponding dependent variables.
## 5 Conclusions  
From the perspective of vehicle teleoperation, accurate state estimation accounting for communication delays is crucial. To the best of the author's knowledge, there has been limited exploration of communication delay estimation using a stochastic approach.  
This study introduces a novel method for distinguishing between outliers in communication delays (ODs) and nominal passive delays (PDs) using stochastic criteria. By conducting teleoperation experiments, one-way communication delays were gathered and analyzed, with PDs and ODs estimated at 98.67% and 1.33%, respectively. The findings demonstrate the effectiveness of the proposed approach under both stable and dynamic communication conditions, with an estimated coverage interval of 5.16 \(\sigma=25\) ms to 100 ms.  
Notably, the proposed outlier detection algorithm enhances performance by compensating for detected outliers within a predictor-based framework. However, it is important to note that the duration of time synchronization and related parameters must be thoroughly investigated to avoid compromising estimation performance.  
Future research should focus on implementing a delay compensation strategy using the proposed method, offering promising avenues for enhancing vehicle teleoperation, particularly in scenarios where real-time responsiveness is critical. Furthermore, exploring the applicability of this approach in other domains, such as remote control of robots or drones, holds significant potential for advancement.  
Conceptualization, E.K. and M.H.; methodology, E.K.; software, T.L. and C.J.; validation, S.Y., E.K. and H.C.; formal analysis, H.C. and C.J.; investigation, M.H.; resources, H.C.; data curation, T.L. and C.J.; writing--original draft preparation, E.K.; writing--review and editing, E.K.; visualization, M.H.; supervision, S.Y.; project administration, M.H.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This study has been conducted with the support of the Korea Institute of Industrial Technology as \"Development of Core Technologies for a Working Partner Robot in the Manufacturing Field\" (KITECH EO-23-0009). Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# MAC Layer Protocols for Internet of Things: A Survey
## 5 Lessons Learned  
Diversity of application scenarios and network deployments directly or indirectly interfere in each protocol performance. Protocol performance can be affected by uplink message factors, downlink message factors, throughput capability, delay tolerance, payload size, power consumption, or even the number of elements supported by the network [126]. Current and new applications come to explore and provide inputs for new studies and evaluations regarding transmission parameters, timing requirements, co-interference, and multi-use platform parameters. Concepts are being disrupted in order to follow the user needs evolution.  
Coexistence of systems in the same frequency band deserves attention and detailed studies due to the explosive growth of technologies that will coexist in the same spectrum. A strong example is the coexistence of Sigfox, and LoRa protocols that already demand coexistence studies. Some adjustments, which are being done to adapt the current technologies with the new ones, at the same time that it attends the evolutional demand, are generating competition for resources.  
M2M communication networks are being used on mobile devices as payment systems. In some cases, there is a sacrifice of energy to obtain a better result in another aspect such as latency, delivery time, or transmission data rates. There is little flexibility for protocols to adapt to these scenarios. Often, the absence of control mechanisms can be justified regarding the energy aspect. Since these are new technologies in new scenarios for new applications, many parameters and capacities are informed by studies and theoretical comparisons [127]. Deployment and operation of these new technologies can present results far beyond those proposed by technology.
## 2 Materials and Methods
### Evaluation Methods  
The evaluation of the imputation methods is conducted by comparing their performance to the original data. The performance assessment involves both the use of static indicators (mean absolute error, standard deviation, and the number of extreme values in errors) for locations with gaps, as well as the preservation of temporal correlations based on the Autocorrelation Function (ACF) between the original and the imputed series. In situations where real data necessary for comparison and evaluation are unavailable, innovative solutions must be developed. In a recent study by us, focusing on interpolating the surface areas of Moroccan lakes using satellite imagery, the ratio of existing to missing data was approximately 50-50%. In that case, we re-imputed the original data based on the imputations and used their comparison to assess the accuracy of the gap-filling process [55]. However, this approach could not be applied in the present study, as data gaps in hydrological stations typically range from 5-20%, making it impractical to re-impute 80-95% of the dataset. Consequently, we employed a novel approach and we developed an innovative evaluation metric.  
The rationale for this approach is grounded in the following concept: starting with the original dataset that contains missing values, we selected an equivalent set of adjacent time points surrounding these gaps. We hypothesized that the magnitude of errors generated when applying the same imputation method to these new gaps would closely mirror the errors observed in the original imputation process. This approach allows us to assess the performance of imputation techniques without relying on complete datasets (Figure 5).
## 3 Model Test and Method
### Comprehensive Uncertainty Analysis  
Relative uncertainty of the measuring model efficiency under the selected optimum operating conditions was collected 10 times and calculated with random uncertainty. Student \(t\) distribution was used according to the International Electrotechnical Commission's (IEC) standard IEC 60601-2-24 [15].  
Standard deviation of the average efficiency:  
\[S_{\overline{\eta}}=\sqrt{\sum\frac{\left(\eta_{i}-\overline{\eta}\right)^{2} }{N(N-1)}}=0.049 \tag{2}\]  
where \(N\) is the number of measurements, \(\eta_{i}\) is the \(i\)th efficiency measurement and \(\eta_{i}\) is the average efficiency.  
Random uncertainty of the test bench efficiency:  
\[E_{r}=\pm\frac{t_{0.95(N-1)}\times S_{\overline{\eta}}}{\overline{\eta}}\times 1 00\%=\pm 0.148\% \tag{3}\]  
where \(t_{0.95(N-1)}\) is the \(t\) distribution corresponding to a 0.95 confidence rate and \((N-1)\) degrees of freedom:  
\[t_{0.95(9)}=2.226\]  
Due to the application of high-precision measuring instruments and automatic data acquisition and a processing system, the random uncertainty in the measurements is greatly reduced, and the system uncertainty becomes the main part of the uncertainty. Th system uncertainty consists of the head uncertainty, torque-speed uncertainty and flow uncertainty and is calculated as follows:  
Head uncertainty:  
\[E_{\rm H}=\sqrt{E_{p}^{2}+E_{d}^{2}}=\pm 0.01\% \tag{4}\]  
where \(E_{p}\) is the static head uncertainty caused by the pressure gauge and differential pressure sensor, \(E_{p}=\pm 0.1\%\), and \(E_{d}\) is the dynamic head uncertainty. In this model test, the inlet and outlet pressure measuring points were on the inlet and outlet tanks, \(E_{d}=0\).  
Uncertainty of the torque speed:  
\[E_{\rm T}=\sqrt{E_{m}^{2}+E_{n}^{2}}=\pm 0.141\% \tag{5}\]  
where \(E_{m}\) is the torque uncertainty and equipment ex-factory calibration, \(E_{m}\)= 0.1%, and \(E_{n}\) is the uncertainty of the speed measurements, \(E_{n}\) = 0.1%.  
Figure 8: Energy performance curves of the repetitive experiment.  
Uncertainty of the flow measurements:  
\[E_{\text{Q}}=\pm 0.1\% \tag{6}\]  
Uncertainty of the test bench system:  
\[E_{\text{s}}=\sqrt{E_{\text{H}}^{2}+E_{\text{T}}^{2}+E_{\text{Q}}^{2}}=\pm 0.173\% \tag{7}\]  
Therefore, the comprehensive uncertainty of the test efficiency is  
\[E_{\text{n}}=\sqrt{E_{\text{s}}^{2}+E_{\text{r}}^{2}}=\pm 0.228\% \tag{8}\]
## 4 Discussion
### Limitations and Future Research Directions  
The proposed VCMS has two primary potential applications for performance improvement. First is machine learning-based respiratory disease prediction. Based on accumulated patient information, patient prognosis and prediction might be performed based on clinical experience. Recently, computer-aided diagnosis based on machine learning has been introduced, and features highly correlated with diseases have been identified. [PERSON] et al. investigated the prediction of ventilator-associated pneumonia using five machine learning models: logistic regression, multilayer perceptron, random forest, support vector machines,  
Figure 9: Scene of real-time web-based display in the ventilator central monitoring system (VCMS) between the ventilator device and mobile application.  
and gradient-boosted trees [54]. These were learned 48 h after mechanical ventilation. The model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC) in a 20% hold-out test set, and the highest value was 0.854. [PERSON] et al. developed and validated a deep learning model for mechanical ventilation [55]. Secured COVID-19 patient data from the University of California San Diego Health and Massachusetts General Hospital and an open-source software-based deep learning model for optimizing tracheal intubation timing and improving patient treatment were proposed and verified using AUROC. This showed a higher AUROC than the baseline model and ROX score. We plan to conduct precision medicine research using big respiratory and biosignal data based on data accumulated with the VCMS. Second, because the VCMS is web-based, it allows medical institutions to monitor home ventilators. It integrates home ventilation based on external cloud services. Many studies have shown that home ventilation using non-invasive ventilation can improve sleep and quality of life, reduce exacerbations and hospital admissions, and improve survival rates [56]. It can significantly change the management of patients with chronic respiratory failure, improve access to treatment, and reduce emergency room visits and hospitalizations, which can positively affect treatment continuity and reduce healthcare costs [57]. A patient's full-cycle observations are expected to be made by communicating the real-time information of the home ventilator with the VCMS using an external cloud service, which can suggest a new paradigm for pulmonary treatment and rehabilitation. However, additional problems, such as collecting patient personal information and strengthening the cybersecurity of cloud services, must be overcome by combining home ventilators and the VCMS [58].
## 5 Application of MSWOA in Fault Diagnosis of Analog Circuits
### Fault Diagnosis of Four-Op-Amp Biquad High-Pass Filter Circuit
#### 5.2.2 Experimental Results and Analyses  
Similar to the first group of analog circuit fault diagnosis experiment described in the previous subsection, in this part, the MSWOA algorithm was compared with PSO and WOA by optimizing the SVM parameters for fault diagnosis. The initialization parameters settings for the three metaheuristic optimization algorithms were identical to the first group, i.e., the swarm size was 20, and the maximum steps of iterations were 100. To avoid the contingency of one experimental result, we performed 20 executions of each algorithm, and the statistics are presented in Figure 20, where the B.V., W.V., and A.V. mean the best value, the worst value, and the average value of the twenty experimental results, respectively.  
As shown in Figure 20, the proposed MSWOA method did better than the other two algorithms, which means that the proposed strategies effectively facilitate the performances of the original WOA algorithm. Besides, Figure A3 illustrates the box plots achieved by the 20 diagnosis results. It can be noticed that the MSWOA method obtained a more concentrated distribution, which indicates a lower standard deviation. Additionally, the red curves and the blue curves shown in Figure 21 are the best fitness values and the mean fitness values obtained by the whole population in one optimization process (100 iterations) of PSO, WOA, and MSWOA, respectively. From this figure, we can conclude that the classifiers optimized by the proposed MSWOA and WOA had better performances in accuracy. In general, SVM optimized by the MSWOA algorithm was more practical and efficient in circuit fault diagnosis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Nominal** & **Tolerance Range** & **Corresponding Fault Range** \\ \hline Normal & - & - & - \\ C\({}_{1}\)\(\downarrow\) & 5 nF & 10\% & [3 nF4.5 nF] \\ C\({}_{1}\)\(\uparrow\) & 5 nF & 10\% & [5.5 nF,7 nF] \\ R\({}_{1}\)\(\downarrow\) & 6 k\(\Omega\) & 5\% & [3.6 k\(\Omega\),5.7 k\(\Omega\)] \\ R\({}_{1}\)\(\uparrow\) & 6 k\(\Omega\) & 5\% & [6.3 k\(\Omega\),8.4 k\(\Omega\)] \\ R\({}_{3}\)\(\downarrow\) & 6 k\(\Omega\) & 5\% & [3.6 k\(\Omega\),5.7 k\(\Omega\)] \\ R\({}_{3}\)\(\uparrow\) & 6 k\(\Omega\) & 5\% & [6.3 k\(\Omega\),8.4 k\(\Omega\)] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fault types of four-op-amp biquad high-pass filter circuit.  
Figure 20: Diagnosis results of four-op-amp biquad high-pass filter circuit.