esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of Deep Reinforcement Learning Controller for Stabilized Platform
### Designing the Reward Function  
When formulating the reward function, we take into account the impact of system error on system control performance. When the current error, denoted as \(\theta_{t}\), approaches \(\theta_{r}\), signifying a smaller error, a higher desired reward value is sought. Consequently, the reward function is structured as the summation of the current error value \(e_{t}\) and previous moment's error \(e_{t-1}\). The design of the reward function ensures a positive reward when both the current error and the previous moment's error fall within the expected range. Any deviation beyond this range results in a negative penalty.  
The reward function at the current moment can be expressed as:  
\[r_{t}=\alpha r_{1}(t)+\beta r_{2}(t), \tag{11}\]  
\[r_{1}(t)=\left\{\begin{matrix}1,&\left|e(t)\leq\epsilon_{1}\right|\\ -\frac{1}{\alpha},&\left|else\right|\end{matrix},\right. \tag{12}\]  
\[r_{2}(t)=\left\{\begin{matrix}1,&\left|e(t-1)\leq\epsilon_{2}\right|\\ -\frac{1}{\beta},&\left|else\right|\end{matrix}.\right. \tag{13}\]  
In the equation, \(r_{1}(t)\) and \(r_{2}(t)\) represent the current error reward value and the previous moment error reward value, respectively. Additionally, \(\alpha\) and \(\beta\) symbolize the current error reward value coefficient and the previous moment error reward value coefficient, respectively. These coefficients are instrumental in fine-tuning the relative importance of \(r_{1}(t)\) and \(r_{2}(t)\) in the reward value calculation. Furthermore, \(\epsilon_{1}\) and \(\epsilon_{2}\) denote the permissible error ranges for the current moment error and the previous moment error, respectively.
## 3 Results  
### Characterization of Fine Aggregates  
Figure 1a shows a SEM image of the limestone aggregates passing the 200 mesh (74 \(\upmu\)m), where the general physical aspect is observed, like their irregular shapes of angular type and a narrow size distribution. Figure 1b shows the detailed particle size distributions of the fine limestone aggregates obtained by sieving. The gradation curve shows a slight deficiency in the particle size distribution; however, this sand was used trying to have results as close as possible to actual working conditions for the fine finishing mortar, which has a fineness modulus of 2.63, a percentage of absorption of 5.1, and a specific gravity of 2.5. The summary of the physical properties is listed in Table 1.  
Figure 1a shows a SEM image of the limestone aggregates that pass the 200 mesh (74 \(\upmu\)m), where the general physical aspect is observed, like their irregular shapes and with a narrow size distribution. Figure 1b shows the limestone aggregates granulometry and it depicts the deficiencies in the particle size distribution; however, this sand was used in an attempt to produce results as close as possible to actual working conditions for the fine finishing mortar, which has a fineness modulus of 2.63.  
In Figure 2, an image from a particle of the aggregates is observed as well as the result of the elemental chemical composition by EDX. Figure 2a shows a fracture surface that presents diverse flat and cleaved surfaces along the planes, and structures in the shape of polyhedrons with well-defined corners that form the particles. Several EDX analyzes were performed per sample. Results indicated that the limestone aggregates contained a high magnesium percentage (Mg) as shown in Figure 2b. According to the literature [7; 33; 34], this could correspond to a type of aggregate not clastic, coming from sedimentary crushed rocks called dolomite.Therefore, it is a carbonate mineral (calcium magnesium carbonate, CaMg(CO\({}_{3}\))\({}_{2}\)) formed by chemical or biological precipitations from seawater reacting with CaCO\({}_{3}\), whose main components are calcite and dolomite.  
The X-ray diffraction analysis of the aggregates shows that the characteristic peaks corresponded to the crystalline structure of calcite and dolomite, as is possible to observe in Figure 3. The results corroborate that the fine aggregates used for the preparation of mortars for this investigation correspond to calcite dolomitic.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Absorption (\%)** & **Fineness modulus** & **Specific gravity** \\ \hline Average & 5.1 & 2.63 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of limestone aggregates.  
Figure 1: Micrograph obtained by SEM of aggregates passing the 200 mesh and granulometric curve of the fine limestone aggregates with a fineness modulus of 2.63.  
Figure 2: Micrograph obtained by SEM of a limestone aggregate particle and its elemental chemical analysis carried out by EDX.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method  
The aim of this research was to verify whether there is an automated workflow between building information modelling (BIM) and building energy model (BEM) software, and how this interoperability works. The idea was to experiment with three different flows from BIM to BEM, which had the same BIM authoring tool in common and the same calculation engine in the BEM software: Energy Plus. For this study, 3D models were made in Autodesk Revit 2020 and the three BEM programs that were chosen were Design Builder, Open Studio, and CYPETHER HE. Figure 1 represents the paper's structure and process.  
The paper was divided into two main steps: The first one is about the interoperability from BIM to BEM for two case studies, a residential building and an industrial warehouse. For both projects, the files were exported from Revit in two formats: green building extensible markup language (gbXML) and industry foundation classes (IFC). To export the gbXML file, we needed to create an analytical model in Revit, which was not necessary for the IFC format. However, to import the IFC file into CYPETHERM HE, another software was required (Open BIM Analytical Model), in order to generate an analytical model based on the IFC format without errors. At the end of this step, a comparison of the gbXML and IFC import results was made to identify the interoperability strengths and weaknesses. In addition, a qualitative evaluation of this first step was carried out, in order to verify which of the three processes was more feasible, under the vision of an architect user of the software.  
The last step was a comparison of the simulation results of the first step. The extracted files from BIM were imported into the BEM software: Design Builder and Open Studio were used for gbXML and CYPETHERM HE for IFC. To make the comparison more precise, we focused on the energy envelope performance through windows and opaque surfaces. In this way, none of the data about people, equipment, lights, infiltration, or HVAC were selected and, therefore, we could explore only the parameters affected by the interoperability construction process in more detail.  
As a result, we intended to discern whether there exists an automated or semi-automated workflow between BIM and BEM.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 4 Experimental Results  
### Results and Analysis  
The real part of the impedance is measured for ten different operating viscosities and the results are superimposed as shown in Figure 11.  
Figure 10: 10 kHz–1 MHz conductivity curve.  
Figure 9: Experimental setup.
## 3 Results
#### 3.1.3 Comparison of Proposed and Realized Training Intensity through PASm-12 (Q2)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pair**} & \multicolumn{2}{c}{**Effort Intensity Variable**} & \multirow{2}{*}{**Mean S1–S6 (\%)**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{\(p\)**} \\  & \multicolumn{2}{c}{**Compared/Mesocycle/Realized-Proposed**} & & & & \\ \hline \multirow{2}{*}{1} & Realized training intensity mesocycle I & 69 & \multirow{2}{*}{\(-\)1.0} & \multirow{2}{*}{\(-\)1.283} & \multirow{2}{*}{0.128} \\  & Proposed training intensity mesocycle I & 70.0 & & & \\ \hline \multirow{2}{*}{2} & Realized training intensity mesocycle II & 70 & \multirow{2}{*}{\(-\)5.0} & \multirow{2}{*}{\(-\)0.867} & \multirow{2}{*}{0.213} \\  & Proposed training intensity mesocycle II & 75.0 & & & \\ \hline \multirow{2}{*}{3} & Realized training intensity mesocycle III & 68 & \multirow{2}{*}{\(-\)2.0} & \multirow{2}{*}{\(-\)2.904} & \multirow{2}{*}{0.017} \\  & Proposed training intensity mesocycle III & 70.0 & & & \\ \hline \multirow{2}{*}{4} & Realized training intensity mesocycle IV & 69 & \multirow{2}{*}{\(-\)7.3} & \multirow{2}{*}{\(-\)2.961} & \multirow{2}{*}{0.016} \\  & Proposed training intensity mesocycle IV & 76.3 & & & \\ \hline \multirow{2}{*}{5} & Realized training intensity mesocycle V & 73 & \multirow{2}{*}{\(-\)4.6} & \multirow{2}{*}{0.885} & \multirow{2}{*}{0.208} \\  & Proposed training intensity mesocycle V & 77.6 & & & \\ \hline \multirow{2}{*}{6} & Realized training intensity mesocycle VI & 69 & \multirow{2}{*}{\(-\)3.8} & \multirow{2}{*}{\(-\)1.972} & \multirow{2}{*}{0.053} \\  & Proposed training intensity mesocycle VI & 72.8 & & & \\ \hline \multirow{2}{*}{7} & Realized training intensity mesocycle VII & 76 & \multirow{2}{*}{\(-\)4.8} & \multirow{2}{*}{2.833} & \multirow{2}{*}{0.018} \\  & Proposed training intensity mesocycle VII & 80.8 & & & \\ \hline \multirow{2}{*}{8} & Realized training intensity mesocycle VIII & 69 & \multirow{2}{*}{\(-\)2.0} & \multirow{2}{*}{\(-\)1.972} & \multirow{2}{*}{0.053} \\  & Proposed training intensity mesocycle VIII & 71.0 & & & \\ \hline \multirow{2}{*}{9} & Realized training intensity mesocycle IX & 74 & \multirow{2}{*}{\(-\)2.2} & \multirow{2}{*}{1.164} & \multirow{2}{*}{0.148} \\  & Proposed training intensity mesocycle IX & 76.2 & & & \\ \hline \multirow{2}{*}{10} & Realized training intensity mesocycle X & 77 & \multirow{2}{*}{\(-\)3.5} & \multirow{2}{*}{3.984} & \multirow{2}{*}{0.005} \\  & Proposed training intensity mesocycle X & 80.5 & & & \\ \hline \multirow{2}{*}{11} & Realized training intensity mesocycle XI & 76 & \multirow{2}{*}{\(-\)4.5} & \multirow{2}{*}{2.956} & \multirow{2}{*}{0.016} \\  & Proposed training intensity mesocycle XI & 80.5 & & & \\ \hline \multirow{2}{*}{12} & Realized training intensity mesocycle XII & 71 & \multirow{2}{*}{\(-\)7.3} & \multirow{2}{*}{\(-\)0.526} & \multirow{2}{*}{0.311} \\  & Proposed training intensity mesocycle XII & 78.3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Differences between realized and proposed training intensity through PASm-12.
## 3 Results and Discussion
### FE-SEM-EDX and TEM Analysis  
Figure 3 shows the FE-SEM images obtained before and after the Cu(II) biosorption stage. In images b and d, it is clearly seen that Cu(II) causes significant damage to the cell structure when compared to the images obtained before this stage (images a and c). Likewise, the EDX spectrum (image f) obtained for image d identifies the characteristic peaks of copper, showing that the precipitates covering the cell surface are formed by this metal. Finally, massive clusters are identified in image b, which may correspond to CuO nanoparticles. The EDX spectra, in which an increase of the peak corresponding to oxygen is observed after the biosorption stage, could support this assertion. To prepare Table S1 from the EDX spectra included in Figure 3, ImageJ 1.53e software was used. The table shows the approximate percentage of the elements present before and after the biosorption stage. It is observed that oxygen is involved in the retention of Cu(II) because an increase in percentage and arbitrary units of this element is identified. A similar change was also found in phosphorus, which indicated that this element was also involved in the adsorption of Cu(II). The elemental maps shown in Figure S1 confirm this statement. In conclusion, FE-SEM-EDX images and FT-IR analysis allow us to affirm that for Cu(II) biosorption, _Staphylococcus epidermidis_ CECT 4183 actively involves surface adsorption mechanisms, some of which could be catalyzing the formation of CuO nanoparticles. Other authors have reported similar results; for example, [PERSON] and [PERSON] studied the adsorption of Cu(II) by _Chara vulgaris_ with good results and suggested that the biosorption process was dominated  
Figure 2: XRD spectra corresponding to the nanoparticles obtained from the cell extract of _Staphylococcus epidermidis_ CECT 4183. The spectrum obtained before the calcination stage is shown in blue, while the spectrum after calcination is shown in red.  
by physicochemical interactions at the surface level such as precipitation, ion exchange, and complexation-coordination [22]. Similarly, [PERSON] and [PERSON] demonstrated the presence of ion exchange mechanisms using the fungus _Botrytis cinerea_[27].  
On the other hand, Figure 4 shows the FE-SEM and TEM images obtained on the CuO-NPs synthesized from the cell extract, confirming the results of the XRD analysis and indicating that the precipitates obtained are CuO-NPs of high purity. In addition, the images obtained on the original nanoparticles, and therefore without the addition of PVA, show uniform sizes below 50 nm with varied and highly aggregated morphologies.
# The Current Development Status of Agricultural Machinery
## 1 Introduction
Nevertheless, three major limitations remain in current research: (1) Insufficient terrain coverage: Systematic studies on steep slopes (15-25\({}^{\circ}\)) and unstructured terrains--such as terraced fields and rugged muddy areas--are still limited; (2) Control strategy constraints: Existing algorithms inadequately address dynamic collaborative optimization across multiple subsystems (power, drive, and leveling), lacking closed-loop capabilities that integrate real-time terrain perception, decision-making, and execution; (3) Lack of alignment with Dual Carbon goals: Principles of energy efficiency optimization and lightweight design have not been sufficiently integrated into the development frameworks of intelligent chassis systems. It is essential to develop a dynamic modeling framework grounded in unified state-space equations, where the slope angle \(\theta\) and slip ratio s serve asfundamental state variables. This framework must incorporate a cooperative optimization mechanism that integrates torque distribution vectors with terrain parameters (e.g., soil shear modulus), thereby improving the generalization performance of theoretical models under complex operational conditions. In hilly and mountainous regions marked by complex terrains and variable slopes, the power system, traveling system, and leveling system of agricultural machinery chassis constitute essential technological pillars for ensuring operational safety and efficiency. Through improvements in terrain adaptability and energy utilization efficiency, coordinated innovation among these systems provides crucial technical support for advancing agricultural modernization in challenging environments. Based on multi-scenario application requirements and aligned with the strategic objectives of China's Dual Carbon goals, this paper presents a systematic review of the developmental status of agricultural machinery chassis tailored for hilly- mountainous regions. It conducts a comprehensive analysis of the technical characteristics, working principles, and scenario-specific adaptability of key subsystems--namely, power transmission, terrain navigation, and dynamic stabilization. To meet the unique operational demands of these terrains, strategic directions are proposed for the development of intelligent chassis technologies, focusing on three core areas: innovative multi-mode locomotion mechanisms, optimized collaborative control algorithms, and integrated intelligent perception systems. These directions aim to provide both theoretical foundations and practical technical pathways for the design and implementation of next-generation terrain-adaptive intelligent chassis systems.
## 2 Predictive Model Based on ESN
### Network Architecture
#### 2.2.2 Learning Skills of ESN Model Parameters  
The reservoir is the core structure of an ESN model, and its related parameters have an important impact on the final performance of the model. These parameters mainly include the reservoir's size, sparsity, the spectral radius \(\rho(\mathbf{W})\) of the internal connection, and the input scaling.  
The size of the reservoir \(N_{x}\) determines the capacity of the model. If we take proper normalization techniques, more nodes will imply stronger liquidity of the model. This is conducive to find out the characteristics of different aspects of historical data. At the same time, the choice of \(N_{x}\) is also related to the number of samples.  
The sparsity of the reservoir represents the connection between neurons in the reservoir. There are several advantages to guarantee the sparsity of \(\mathbf{W}\) in the learning of the ESN model. The sparsity of the reservoir helps the information to reverberate in the local area. In addition, the sparsity can also greatly accelerate the calculation.  
The connection weight spectral radius \(\rho(\mathbf{W})\) in the reservoir is the maximum absolute value of the eigenvalue of \(\mathbf{W}\). Only if \(\rho(\mathbf{W})<1\), ESN can have the echo network properties. In practice, if learning task needs to memorize information for a longer time, then \(\rho(\mathbf{W})\) needs to be set relatively large, and vice versa.  
The scale of the reservoir's input unit is a scale factor that needs to be multiplied before the input signal is connected to the internal neurons of the reservoir. Put in another way, the input signal is scaled to a certain extent. Generally speaking, the stronger the nonlinearity of the object is, the larger the scale factor should be.
## 4 Simulation Results and Analysis
### Simulation Results
Therefore, in the following simulations, we adopt a compromise value of 20 time slots. Figure 8 illustrates the comparison of average weighted sum rate between the non-proportional fairness scheduling algorithm JAS-CPUS and the proportional fairness algorithm JAS-PFUS. JAS-CPUS is an effective algorithm, in which the antenna selection part adopts a successive elimination method, while the user scheduling part is based on the channel power of each user. The weights of all the users are fixed at 1 to calculate the weighted sum rate. In order to fully consider the fairness of users' participation in communication, PFUS algorithm gives each user an adjustable weight and schedules different  
Figure 6: System sum rate under imperfect CSI. Figure 7: Average sum rate of JAS-PFUS for various time slots. users to participate in communication in each time slot by adaptively adjusting the weights. As can be seen from the figure, JAS-PFUS only slightly reduces the weighted sum rate. The efficiency loss is very small, which ensures the effectiveness of the transmission. In Figure 9, we compare the fairness of two algorithms in different scenarios, where the fairness metric is average rate per user. The simulation setup is 20 time slots, 16 total users, and 10 dB SNR. We compare the fairness of two algorithms by calculating the average rate of each user over 20 time slots. A user has a positive rate only when he is scheduled. As illustrated in Figure 9a, JAS-PFUS algorithm is capable of scheduling 15 users to participate in communication within a time duration of 20 time slots, while the algorithm without proportional fairness can only schedule 4 users. In Figure 9b, the number of RF chains increased to six. Assuming that the quantity of selected users equals, i.e., the quantity of users allocated is also six. JAS-PFUS can be still capable of scheduling up to 14 users. In Figure 9c, by limiting the total number of users to 10, JAUS-PFUS is able to schedule all users to participate in the communication. Thus, regardless of whether the quantity of selected participants is increased or the total quantity of participants is decreased, the proposed algorithm is able to schedule more users, which fully realizes fairness in user scheduling.
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 5 Immunization Strategy of Intermediary Layer
After adopting immunization strategy 1, the spreading speed is significantly reduced and the number of infected nodes is also significantly reduced. After adopting strategy 2, the spreading speed is significantly slowed down, but the density of the infected nodes does not change when it reaches the steady state. In addition, this paper takes into account the seed nodes; in other words, the influence of the attribute of the spreading source node on disease spread on the three-layer network EBE, and the spreading change when the node with larger degree and betweenness is the node of spreading source, are considered. As the intermediary layer network possesses the scale-free characteristic, this kind of strategy does not have a significant influence on the global spreading behavior based on the global structure analysis. The immunization strategy also works well when the overall network degree distribution shows the power-law properties. According to the characteristics of different network models, the corresponding immunization strategies are designed through simulation experiments. The experimental results show that the spreading procedure of the global network can be well suppressed by adopting strategy 1, which reduces the inter-layer link probability for the intermediary layer. In a three-layer network where the intermediary layer is ER, the link probability between intra-layer nodes is reduced by adopting strategy 2, which can effectively suppress the disease spread and the infection scope. However, due to the small-world characteristics of the intermediary layer of the WS network, the spreading phenomenon is not significantly suppressed after adopting this strategy; the main reason it that the intermediary layer is the WS network in the three-layer network. The intermediary layer is the BA network. the strategy has no obvious effect on the global spreading behavior if the global network is a scale-free network. The strategy can play a good role when the degree of distribution of the network exhibits power-law characteristics. By adopting strategy 3, it can effectively suppress the global spread of the disease when the intermediary layer is an ER network, but it has no significant effect when the intermediary layer is a WS network. The network is not considered where the intermediary layer is a BA network, because the number of nodes has little effect on the scale-free network.