esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Degradative Kinetics
Condition: H\({}_{2}\)O\({}_{2}\) = 0.4 M, pH = 5, and NaNO\({}_{2}\) = 0.02 M.  
The reaction orders with respect to H\({}_{2}\)O\({}_{2}\) concentration and HCOOH concentration deviated from 1, indicating a nonlinear relationship between the degradative reaction. An increase in reaction temperature and the concentrations of H\({}_{2}\)O\({}_{2}\) and HCOOH enhanced the chain-scissoring reaction. On the other hand, the negative power of NaNO\({}_{2}\) concentration indicated that the M\({}_{\rm n}\) reduction was inversely proportional to NaNO\({}_{2}\) concentration. Hence, an increase in NaNO\({}_{2}\) concentration retarded the depolymerization rate. The reaction orders of [H\({}_{2}\)O\({}_{2}\)], [HCOOH], and [NaNO\({}_{2}\)] were 1.58, 0.79, and \(-\)0.65, respectively (Figures 9-11). According to Figure 12, The pre-exponential factor and activation energy were found to be 1.04 \(\times\) 10\({}^{9}\) M\({}^{-1.72}\) t\({}^{-1}\) and 78.66 kJ/mol, respectively, according to Figure 12. As a result, the kinetic model for random scission of NR using H\({}_{2}\)O\({}_{2}\), HCOOH, and NaNO\({}_{2}\) as reagents can be summarized as Equation (5):  
\[\frac{\mathrm{M}_{0}}{\mathrm{M}_{\rm n(t)}}-\frac{\mathrm{M}_{0}}{\mathrm{M}_ {\rm n(0)}} =1.04\times 10^{9}\,\mathrm{M}^{-1.72}\,\mathrm{t}^{-1}\,\mathrm{e}^{- \frac{78.66\,4\,\mathrm{J}\,\mathrm{mol}^{-1}}{\mathrm{RT}}}\,[\mathrm{H}_{2} \mathrm{O}_{2}]^{1.58}\,[\mathrm{CH}_{2}\mathrm{O}_{2}]^{0.79}\,[\mathrm{NaNO }_{2}]^{-0.65}\,\times\mathrm{t} \tag{5}\]  
Figure 13 shows a satisfactory relationship between the experimental and predicted values obtained from Equation (5) of degradation of NR. The kinetic parameters were estimated based on experimental data. When R\({}^{2}\) was equal to 0.9771, the data prediction showed good performance and a high correlation between the predicted value obtained from Equation (5) and the experimental value.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 4 Results and Discussion
### Quantitative Comparison
Table 7 shows the results of the MSE for all the comparison methods using different ECG signals under consideration at a particular input SNR level of 20 dB. It is vivid from this table that the proposed method yields the smallest MSE and verify its capability to yield enhanced ECG signal with better quality. A bar diagram presenting the percentage PRD(%) results obtained by using different de-noising methods are plotted in Figure 10. We add Gauss white noise with input signal-to-noise ratio of 20 dB to ECG signal. The histogram clearly shows that the proposed method performs best for any ECG signal because it yields the lowest PRD(%). \begin{table}
\begin{tabular}{c c c c c} \hline
**ECG Signals** & **EMD Soft Thresholding** & **Wavelet Soft Thresholding** & **EMD Wavelet** & **Proposed Method** \\ \hline
100 & 0.009 & 0.0026 & 0.0019 & 0.00058 \\
103 & 0.01 & 0.003 & 0.0024 & 0.00068 \\
105 & 0.0228 & 0.003 & 0.0032 & 0.00053 \\
115 & 0.0094 & 0.0035 & 0.0030 & 0.00055 \\
215 & 0.0105 & 0.0029 & 0.0028 & 0.0005 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of the MSE obtained by using different denoising methods. Figure 10: Comparison of the PRD(%) obtained by using different de-noising methods. Figure 9: WT and proposed method output SNR improvement versus different input SNRs for the first 4096 samples of the MIT-BIH record number 100.
## 2 The Overall Structure of the Control System
### Control Methods
#### 2.1.3 Optimal Water Level Control (\(Owlc\))  
This control system is based on the use of both \(MPPT\) control and water level control (\(WLC\)) systems. Firstly, the \(MPPT\) control system estimates the optimal water level that makes the \(AST\) operate the maximum power point. Then \(WLC\) is used to maintain the water level at the level previously estimated.  
The \(OWLc\) acts on only one parameter of Equation (2), the head (\(h\)). But, as this \(h\) ensures that the mechanical power in Equation (2) is at maximum, similar results to the \(MPPT\) control are obtained. The control system will regulate the speed of the electric generator so that the preset head will remain constant. Moreover, the repetitive search for the \(MPPT\) control method is avoided. If there are no significant changes in the flow rate, this method obtains the best results, but it is very sensitive to changes in the flow rate.  
This control system is very sensitive to the set point water level measurement. To obtain this value, two ultrasonic devices are used to measure the water levels upstream and downstream of the \(AST\). Therefore, the accuracy of this control system depends on the correct installation of these devices.  
A diagram of an optimal water level control system is shown in Figure 5.  
Figure 4: An MPTT algorithm implemented.  
Figure 5: Diagram of an optimal water level control system.  
The fact that an increase in water flow causes a noticeable change in the water levels at an ultra-low head must be taken into account with this type of control system [26]. As the control system maintains the water level at the preset level, the optimum operating level will have to be re-determined.
## 1 Introduction
Ignoring these variations can result in over-designs or unexpected failures during physical testing. As a result, bridging the gap between simulations and structural analyses is essential for improving simulation accuracy and reducing the number of expensive experimental validations. This paper presents a novel approach to improve FEM structural simulations by integrating non-uniform mechanical properties derived from stamping simulations, focusing on a railway passenger seat structure, which is a critical component governed by strict safety standards such as AFNOR NF F 31-119 [24]. The methodology involves simulating the stamping process to predict local variations in thickness and yield stress, mapping these properties into the structural FEM model, and validating the results against experimental static tests. By incorporating real-world material behavior into FEM analyses, this approach provides more realistic predictions of deformation, stress distribution, and residual plasticity. The objectives of this work are threefold:  
1. To develop a robust workflow for mapping stamping-induced property variations into structural FEM models;
2. To evaluate the impact of mapped properties on the accuracy of FEM simulations compared to uniform-property models;
3. To validate the mapped FEM simulations against experimental data, highlighting improvements in predictive capability.
# Hydrothermal Carbonization of Spent Coffee Grounds
## 3 Results and Discussion
### Fuel Characteristics  
To investigate fuel characteristic changes in hydrothermal carbonized samples [11; 20], Figures 5 and 6 and Tables 2 and 3 show the results of elemental and proximate analysis of the SCGs raw material and reaction products under different conditions. While the carbon  
Figure 4: Change in pressure with reaction temperature for different water contents. Figure 3: Variation of mass yield (MY), energy yield (EY), and higher heating value (HHV) of solid coffee grounds (SCGs) with different reaction temperatures and water contents. and FC contents increased as the reaction temperature increased, as shown in Figure 4, the oxygen and VM contents decreased (Figure 5). This confirmed that the decomposition of volatile oxygen-bearing components during the hydrothermal carbonization of SCGs led to a relative increase in the carbon content. On the other hand, Tables 2 and 3 show that there was almost no change (less than 1%) in the amount of nitrogen and ash components. The products obtained with a 1:1.5 water content exhibited higher carbon and FC contents than those obtained with the 1:4 condition, but showed lower oxygen and VM contents. In particular, when the SCG-water ratio was 1:1.5, the VM content decreased sharply, while the amount of  
To investigate changes in the fuel characteristics of the SCGs reaction products subjected to hydrothermal reactions, the O/C and H/C ratios were determined for the reaction products produced with different reaction temperatures and water content (Figure 7). At both water contents, the O/C and H/C ratios decreased as the reaction temperature increased, with the 1:1.5 condition resulting in lower values than the 1:4 condition under most temperature conditions. In particular, the O/C ratio was more affected by the water content than the H/C ratio. This is likely, as the elemental analysis results in Tables 2 and 3 show, because there was hardly any change in the hydrogen content with increasing reaction temperature, at both water content conditions, while the increase in carbon content and the decrease in oxygen content. Figure 8 shows the Van Krevelen diagram [21], in which the properties of H/C and O/C ratios of the raw material used in this study were compared with those of the hydrothermally carbonized reaction products and coal that the properties of the SCGs reaction products gradually became more similar to those of coal as the reaction temperature increased, owing to a reduction in both the O/C and H/C ratios. Figure 9 shows the correlations between the carbon and hydrogen contents and the HHV of the hydrothermally carbonized SCGs reaction products. From Figure 9, the coefficient of determination (R\({}^{2}\)) for the linear regression analysis of the correlations between the carbon and hydrogen contents and HHV was found to be 0.6356 for carbon and 0.2396 for hydrogen at an SCG-water
# Design and Experiment of Grain Litter for Sorghum Harvester
## 1 Introduction  
Sorghum is the main raw material for making liquor and plays an important role in our national economy [1; 2]. Especially in recent years, driven by the rapid development of the liquor industry in 2020, China's sorghum planting area has reached 635,000 \(\mathrm{hm}^{2}\) and the output reached 2.97 million tons, it is the fifth largest food crop in the country [3; 4], but constrained by many factors, China's sorghum harvesting mechanization is still in its infancy [5]. One of the main tasks of the harvester is to cut off and transport sorghum plants. However, due to the inconsistency of sorghum row spacing, dense plant spacing, easy lodging, and soft and loose ears, the plants skew and are difficult harvest [6; 7; 8; 9; 10]. Sorghum stems and leaves easily appear and the ears overlap, which increases the difficulty of harvesting sorghum, increases the harvest loss of cutting table, and even causes cutting table blockage in serious cases [11; 12; 13; 14].  
In order to solve this problem, domestic and foreign experts have conducted various studies on it [15]. For example, in order to reduce the influence of overlap and entanglement of sorghum ears, stems, and leaves on mechanical harvesting operations, [PERSON] adopted the edge supporting technology to cut the active separation of sorghum plantssticking inside and outside the cutting table; on the basis of improving varieties (changing loose ear sorghum varieties to tight ear and medium tight ear varieties), CASE Company (New Delhi, India) adopted the method of increasing the range of pulling wheel to achieve low loss harvest of high stalk sorghum [16; 17; 18]. Domestic research on sorghum harvesting technology started late. [PERSON] et al. [19] reduced the loss of the cutting table during sorghum harvest by lengthening the bottom plate of cutting table and enlarging the range of action of the plucking wheel. [PERSON] et al. adopted the cutting method of stem and ear to reduce the loss of cutting table in sorghum harvest. There are also reciprocating knives to cut the overlapping plants and reduce the adhesion of sorghum, which improves the reliability of the cutting table.  
Obviously, the sideline of the sorghum harvester is the key to affecting the reliability of the whole machine and reducing the harvest loss of the cutting table [19; 20; 21; 22; 23; 24]. Although the predecessors have carried out some research on it, the ideal effect has not been achieved. In order to achieve low loss and high efficiency harvest of sorghum, this paper designed a kind of crop support device that is suitable for sorghum and skew and fall harvest.
# The Effect of Varying Out Beverage Ratios on the Characteristics of Fermented Dairy-Oat Beverages
## 3 Results
### Lactose Content During the Fermentation  
Lactose is the primary sugar found in cow's milk and serves as a crucial substrate for lactic acid bacteria, which convert it into lactic acid. At the start of fermentation (0 h), the sample without added oat beverage (0%) exhibited the highest lactose content, approximately 5% (Figure 1). Samples with a higher proportion of oat beverage contained less initial lactose due to the dilution of cow's milk by the lactose-free oat beverage. The greatest differences between groups were observed at the beginning of fermentation, as the initial lactose content varied significantly based on the mixture composition. As fermentation progressed, lactose gradually decreased. After 6 h, the lactose content in the 0% sample dropped to around 2.5%. The higher the oat beverage proportion, the lower the initial lactose content and the slower the rate of its breakdown. The high initial lactose content in cow's milk favored faster fermentation and more intense acidification of the beverage. Since oat beverage lacks lactose, its addition reduced the available substrate. The most pronounced decrease in lactose occurred in the 0-50% oat samples, indicating greater  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{1}{c}{**\% Oat**} & & & & & & \\ \multicolumn{1}{c}{**Beverage to Be**} & **0** & **25.0** & **33.3** & **50.0** & **66.6** & **75.0** & **100** \\ \multicolumn{1}{c}{**Cow’s Milk**} & & & & & & & \\ \hline dry matter [\%] & 11.84 \({}^{\pm}\) 0.11 & 11.42 \({}^{\text{b}}\)\(\pm\) 0.08 & 11.32 \({}^{\text{b}}\)\(\pm\) 0.08 & 10.98 \({}^{\text{c}}\)\(\pm\) 0.08 & 10.72 \({}^{\text{c}}\)\(\pm\) 0.08 & 10.62 \({}^{\text{c}}\)\(\pm\) 0.08 & 10.14 \({}^{\text{d}}\)\(\pm\) 0.11 \\ proteins [\%] & 3.16 \({}^{\text{a}}\)\(\pm\) 0 activity of the fermentative bacteria. High oat beverage content could limit the activity of yogurt bacteria, which prefer lactose as an energy source. This might explain the slower lactose breakdown rate in samples with more oat content. In the 75% oat beverage sample, lactose content declined from approximately 1.2% to 0.5% during fermentation, suggesting that the enzymatic process continued but at a less intense rate compared to samples with higher cow's milk content. The 100% oat beverage samples exhibited 0% lactose throughout the fermentation period. With a high oat content, fermentative bacteria could partially utilize starch as an alternative energy source, reducing the demand for lactose.
## 4 Numerical Results
### Estimation Results Based on Simulation Data
#### 4.1.2 Robot Pose Estimation  
We evaluated pose estimation performance, including roll, pitch, yaw, Y-displacement, and Z-displacement, under varying sensor depth errors. Figure 6 shows the results of the pose estimation. We can see that all five pose elements had similar trends with the sensor depth error. RU-EPD had no estimation bias, while there was a small estimation bias with C-EPD. The estimation error range of both C-EPD and RU-EPD increased as the sensor depth increased, but RU-EPD achieved a much smaller estimation error range. RU-EPD maintained the estimation error range of pitch and yaw accuracy as 0.12\({}^{\circ}\) while the one of the Y- and Z-displacement error was 2.15 mm. The roll angle error was relatively  
Figure 4: Comparison of pipe diameter estimation errors between C-EPD and RU-EPD under varying sensor depth errors.  
Figure 5: Comparison of pipe diameter estimation errors between C-EPD and RU-EPD under varying pipe diameters.  
higher than that of the pitch and yaw but the estimation error range was still within 8.28\({}^{\circ}\). These results confirm that RU-EPD not only eliminated estimation bias but also delivered consistent performance, making it suitable for in-pipe navigation systems.
# Enhancement of Heat Dissipation from the Hydraulic System Using a Finned Adaptive Heat Exchanger
## 2 Materials and Methods
To properly position the auxiliary hydraulic oil cooler in the circuit, it is important to identify the main sources of heat to ensure that it is positioned in the most appropriate location for efficient heat dissipation. An example of the AHE installation in a hydraulic circuit is shown in Figure 3. The experimental hydraulic circuit consisted of several main components connected to each other by the hydraulic pipe (2) with an internal diameter of 16 mm. Hydraulic oil flowed through the hydraulic pipe from the oil reservoir (4). The oil reservoir also housed a heating coil (5), which was used to heat the hydraulic oil to the required temperature. The hot hydraulic oil was sucked by the gear pump (6) from where it flowed through the vane flow meter (11) to the inlet of the AHE (1). As the hot hydraulic oil passed through the AHE, some of the heat was transferred to the ambient air through the finning on the surface of the AHE. The cooled hydraulic oil flowed from the AHE to the oil filter (3), in which impurities were removed from the oil. From the oil filter, the cooled and cleaned oil flowed back into the oil reservoir (4), where it was heated again, and the process was repeated. The 500 W heating coil (5) was powered by an industrial 12 V power supply. The changing temperature of the hydraulic oil inside the pipes was monitored by temperature sensors located at the inlet (9) and the outlet (10) of the AHE. Additionally, the surface temperature of the pipe was monitored by means of a surface temperature sensor (8) which encircled the hydraulic pipe (2). Data from the individual sensors were recorded via a data logger (12). The heat exchanger can be mounted on different pipe and hose diameters, either straight or curved. The design of the AHE allows it to be installed in the circuit without shutting down or interrupting the integrity. The newly designed heat exchanger differs from the classical concept in that the fluid does not flow directly through its interior. The upper and lower flange encircles the outer wall of the pipes through the enlarged surface area of the finned heat exchanger, removing heat continuously during operation.
# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 2 Technical Background
### LSTM Network  
LSTM (Long Short-Term Memory) neural network was proposed by [PERSON] and [PERSON] in 1997 [24]. It is an optimized RNN (Recurrent Neural Network) that is designed to treat for the time sequence data based on the recurrent framework. With the increase number of times of information transmission, RNN will have problems in learning to convey the information due to the gradient vanishing or exploding issue, but the LSTM network can avoid these difficulties perfectly. The most remarkable point of LSTM is its repeated neural network memory module, also known as the hidden layer which is no longer a simple neuron node; it's a computing node with rich internal structure, which contains a self-connected memory cell and three \"gates\" that control the flow of information. The gates include input gate, output gate and forget gate. Due to their recurrent structure,even a single layer of LSTM nodes can be considered as a \"deep\" neural network. The structure of LSTM is shown in Figure 2.  
At time step \(t\), the calculation formulas of the \(l\)-th layer units can be expressed as:  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f})\quad i_{t}=\sigma(W_{xi}x_{t}+W_{ hi}h_{t-1}+b_{i}) \tag{9}\]  
\[\widetilde{C}_{t}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c})\quad C_{t}=f_{t} \otimes C_{t-1}+i_{t}\otimes\widetilde{C}_{t} \tag{10}\]  
\[o_{t}=\sigma(W_{x0}x_{t}+W_{ho}h_{t-1}+W_{co}C_{t}+b_{o})\quad h_{t}=o_{t} \otimes\tanh h(C_{t}) \tag{11}\]  
where the vectors \(f_{t}\), \(i_{t}\), \(o_{t}\), \(C_{t}\) represent the activation of the forget, input, output gates, and memory cells, respectively. \(W_{ab}\) terms denote the weight matrix between \(a\) and \(b\) (e.g., \(W_{xi}\) is the weight matrix from the inputs \(x_{t}\) to the input gate \(i_{t}\)), and these four weight matrixes are associated with the inputs \(x_{t}\). The \(b_{\alpha}\) describes the bias term of \(\alpha\) with \(\alpha\in\{i,f,C,O\}\), \(\sigma\) is the logistic sigmoid function as \(\sigma(x)=1/(1+e^{-x})\), and '\(8\)' denotes the Hadamard product. The multiplicate LSTMs model can be applied to solve many real-life time sequence modeling problems, and multiple LSTMs can be stacked or concatenated flexibly to form more complex structures.
# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 5 Model Types
_Ontologies_ (denoted as ONTO) represent the last evolution of logic-based approaches and have increasingly gained attention as a generic, formal and explicit way to \"capture and specify the domain knowledge with its intrinsic semantics through consensual terminology and formal axioms and constraints\" [39]. They provide a formal way to represent sensor data, context, and situations into well-structured terminologies, which make them understandable, shareable, and reusable by both humans and machines. A considerable amount of knowledge engineering effort is expected in constructing the knowledge base, while the inference is well supported by mature algorithms and rule engines. Some examples of using ontologies in identifying situations are given by [40] (later evolved in [20; 21]). Instead of using ontologies to infer activities, they use ontologies to validate the result inferred from statistical techniques. Figure 4: Model formalism used in [38] based on R-pTL formulas. The model is based on correlations between events. Figure 5 contains three different examples of ontologies employed to model smart spaces. Clearly, each approach adopts the formalism most devised for its application, and many aspects can be modeled under different granularities. The way an AmI system makes decisions on the actions can be compared to decision-making in AI agents. As an example, _reflex agents with state_, as introduced in [42], take as input the current state of the world and a set of Condition-Action rules to choose the action to be performed. Similarly, [PERSON] [43] introduces the concept of Active DataBase (ADB) composed by Event-Condition-Action (ECA) rules. An ECA rule basically has the form \"ON _event_ IF _condition_ THEN _action_\", where conditions can take into account time. The first attempts to apply techniques taken from the business process management--BPM [44] area were the employment of workflow specifications to anticipate user actions. A workflow is composed by a set of tasks related by qualitative and/or quantitative time relationships. Authors in [45] present a survey of techniques for _temporal calculus_ (i.e., [PERSON]'s Temporal Logic and Point  
Figure 5: Examples of Ontology Models. (a) Example of machine-oriented ontology. Picture taken from [41]. (b) Example of taxonomy based ontology. Picture inspired by chen-ont [18]. (c) A portion of the resulting ontology model proposed in rib-prob [20; 21]. The dashed lines represent the relations super/sub between classes. Algebra) and _spatial calculus_ aiming at decision-making. The SPUBS system [46; 47] automatically retrieves these workflows from sensor data. Table 3 shows, for each surveyed paper, information about RQ-B1.1 (Formalism), RQ-B1.2 (Readability Level) and RQ-B1.3 (Granularity).