esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 5 Experiment on Wall Images  
We conducted a homography estimation on the wall images of our wall-climbing robots using the previously trained three-stage hierarchical HomographyFpnNet model. The test wall images were decoded from the recorded video (one image per 2 s) and there were 52 images (\(1280\times 720\) pixels) in total. We selected four fixed points on the wall and manually labeled the pixel coordinates of these four fixed points on each wall image. After conducting the four-point homography prediction with our three-stage HomographyFpnNet model, we resized the predicted four-point values back to the original image size, and then calculated the mean corner error; the mean corner error was computed in the image size of \(1280\times 720\) pixels.  
In the first experiment, for each image_a of wall image, we first warped the labeled four-point homography values (green box in Figure 10) to get image_patch_b, and then used the trained three-stage hierarchical HomographyFpnNet model and the input image pair (image_a, image_patch_b) to predict the four-point homography values (red box in Figure 10). The four-point homography estimation errors of our three-stage HomographyFpnNet on the 52 test wall images are shown in Figure 11, and the mean corner error of the 52 images was only 2.8 pixels. Figure 10 shows some prediction results on the wall images.  
future, for example generating training samples of fixed image_patch_b and a changeable image_a from videos.  
Figure 11: Experiment 1. Prediction errors of our three-stage hierarchical HomographyFpnNet on test wall images.  
Figure 10: Experiment 1. Prediction results of our three-stage hierarchical HomographyFpnNet on test wall images where image_patch_b is warped from each wall image. The corners of the green box are the ground truth four-point homography values and the corners of the red box are the predicted results. Since the error of the homography estimation is very small, the red and green boxes almost overlap.
## 2 System and Channel Models  
### Target System  
In Figure 1, we illustrate an MN MIMO system model under consideration with two sensor nodes (SENs), each employing \(M\) or fewer transmit antennas. Unlike the SN MIMO system, any \(M\) transmit antennas can be selected from 2 \(M\) antennas of two sensor nodes in the MN MIMO system. An SN MIMO model can be regarded as a subset of general MN MIMO systems when all the signals are sent from a single sensor node (SEN). While we consider two sensor nodes for simplicity of description, the MN MIMO model can be easily extended to cases with more than two sensor nodes. We consider a single mobile sink node (MS) with \(N~{}\geq~{}M\) and assume that \(M\) transmit antennas from among the 2 \(M\) total sensor node antennas will be selected for either spatial multiplexing or diversity transmission. Weassume equal transmit power from each sensor node and equal power on each antenna (basically, there is no adaptive power control).
# Analytical MPC Algorithm Using Steady-State Process Model
## 2 Materials and Methods  
### MPC Algorithms  
The MPC algorithms generate the future control signal trajectories in such a way that, predicting many sampling instants ahead, the future behavior of the control plant and the shape of control signals fulfill assumed criteria, which, for SISO (Single-Input Single-Output) control plants, are expressed in the form of the following optimization task [7; 8; 10; 12]:  
\[\arg\min_{\Delta u}\Bigl{\{}J_{\text{MPC}}=(\overline{\mathbf{y}}-\mathbf{y})^{T} \cdot(\overline{\mathbf{y}}-\mathbf{y})+\lambda\cdot\Delta\mathbf{u}^{T}\cdot\Delta\mathbf{u} \Bigr{\}} \tag{1}\]  
where \(\mathbf{y}=\left[y_{k+1|k},\ldots,y_{k+p|k}\right]\), \(\Delta\mathbf{u}=\left[\Delta u_{k|k},\ldots,\Delta u_{k+s-1|k}\right]\), \(y_{k+j|k}\) is the output value for the \((k+i)^{\text{th}}\) sampling instant predicted at the \(k^{\text{th}}\) sampling instant using a control plant model, \(\Delta u_{k+i|k}\) are future changes in the manipulated variable, and \(\lambda\geq 0\) is the weighting coefficient for the changes of the manipulated variable; \(p\) and \(s\) denote prediction and control horizons, respectively. The following vector defines the reference trajectory:  
\[\overline{\mathbf{y}}=\left[\overline{y}_{k+1|k},\ldots,\overline{y}_{k+p|k} \right]\,. \tag{2}\]As a solution to the optimization problem given in (1), the optimal vector of control action is obtained in such a way that future predicted control errors are minimized (the first component of the performance index) and the control signal does not change too much (the second component). The \(\Delta u_{k|k}\) element of the vector of decision variables \(\Delta u\) is applied to the control system and the algorithm passes to the next iteration. The predicted values of the output signal \(y_{k+j|k}\) are derived using the control plant model the MPC algorithm is based on. In order to take into account modeling uncertainty and unmodeled disturbances, the prediction is calculated, including correction, which is the difference between the measured process output and the output of the model used by the algorithm. If a linear model is used, then (1) becomes quadratic with respect to the changes in the control signal. Moreover, a control law can be obtained if the optimization problem is solved without constraints. The DMC algorithm will now be detailed.
## 4 Experiment and Analyses
### Objective Evaluation
#### 4.2.3 Evaluation Using DIIVINE  
The multi-exposure fused image should not only retain more image information from the multi-exposure source images, but it should also match the human visual perception as closely as possible. Therefore, the non-reference image metric, DIIVINE, was introduced, based on perceived quality, into the fusion metric. DIIVINE evaluates the image quality through 88-dimensional image features, which are based on natural scene statistics. Meanwhile, DIIVINE is also capable of assessing the distorted image quality across multiple distortion categories. The larger the DIIVINE value, the more comfortable it is to human visual perception.  
The performance of different methods on the 12 image sequences using DIIVINE is listed in Table 3. It can be seen that, in the fusion metric DIIVINE, the performance of the proposed method was superior to the other three comparative methods and near to the Liu15 method. However, it was not as good as Vonikakis11, which uses illumination estimation to evaluate the source image exposure quality. However, this method may also lead to loss of details in the blacker regions, as shown in Figure 9b.
## 2 Materials and Methodology
### Network Architecture of CCS-YOLOv8
#### 2.2.4 Small Object Detection Layer  
The YOLOv8 network uses a three-layer proportional feature map architecture in its neck structure. Three distinct feature maps can be produced following the fusing of features. Smaller receptive fields and greater information regarding target positions and nearby features are characteristics of larger-scale feature maps, which make them appropriate for small target detection. Smaller-scale feature maps, on the other hand, are better at recognizing huge objects because they contain richer semantic information and broader receptive fields, but they also lack clear local details. The YOLOv8 network may not fully meet the requirements for detecting young animals due to its limited \(80\times 80\) maximum feature map size.  
To improve the recognition performance of young and small target livestock, we added a small object detection layer to the YOLOv8n network during the neck feature fusion step. With this improvement, the YOLOv8n network can identify objects in a narrower receptive field, which improves its capacity to capture the distinctive characteristics and intricate  
Figure 5: The general structure of CARAFE. The up-sampling factor in the figure is 2.  
details of young livestock. YOLOv8n takes advantage of the four distinct detection layer scales for feature fusion, which allows the system to efficiently utilize semantic information and fine-grained details at every level, which reduces the possibility of misidentifying or omitting small targeted livestock and promotes more precise livestock identification and localization.
# Solution-Processed Memristor Devices Using a Colloidal Quantum Dot-Polymer Composite
###### Abstract  
In this study, we demonstrate solution-processed memristor devices using a CdSe/ ZnS colloidal quantum dot (CQD)/poly(methyl methacrylate) (PMMA) composite and their electrical characteristics were investigated. Particularly, to obtain stable memristive characteristics with a large current switching ratio, the concentration of CdSe/ ZnS QDs in the PMMA matrix was optimized. It was found that with the CdSe/ ZnS QD concentration of 1 wt%, the memristor device exhibited a high current switching ratio of \(\sim\)10\({}^{4}\) and a retention time over 10\({}^{4}\) s, owing to the efficient charge trapping and de-trapping during the set and reset processes, respectively. In addition, we investigated the operational stability of the device by carrying out the cyclic endurance test and it was found that the memristor device showed stable switching behavior up to 400 cycles. Furthermore, by analyzing the conduction behavior of the memristor device, we have deduced the possible mechanisms for the degradation of the switching characteristics over long switching cycles. Specifically, it was observed that the dominant conduction mechanism changed from trap-free space charge-limited current conduction to trap charge-limited current conduction, indicating the creation of additional trap states during the repeated operation, disturbing the memristive operation.  
memristors; colloidal quantum dot; polymer; solution process; endurance +
Footnote †: journal: Applied Sciences
## 4 The Coordinated FR Strategy of DFIG and EV
### Real-Time Allocation of System FR Task  
In order to realize the coordination of DFIG and EVA in system FR, a real-time allocation strategy of system FR task is proposed. The de-loading operation will influence the economic efficiency of DFIG [4]. Therefore, taking into account the economical operation of the wind farm, the DFIG is set to have priority over EV in up-regulation. Meanwhile, when the EV is connected to the grid, charging is the first demand for EV owner. In order to reduce EV's charging load for driving demand, the EVA is set to have priority over DFIG in down-regulation.  
When the system frequency fluctuates, the system FR task \(P_{task}\) will be allocated to DFIG and EVA according to the FR capacity and the type of FR, which includes five modes, as elaborated below. \(P_{task}\) is set to be positive for up-regulation and negative for down-regulation.  
**Mode 1:** If \(P_{task}>0\), the system FR task \(P_{task}\) will be allocated to DFIG first. If the up-regulation capacity of DFIG is more than the system FR task, i.e., \(R_{up}^{wind}\geq P_{task}\), all the FR power output will be provided by DFIG, while EVA will not participate in FR. Thus, the FR tasks of DFIG and EVA are shown in (16):  
\[\left\{\begin{array}{ll}P_{task}^{wind}=P_{task}\\ P_{task}^{ev}=0\end{array}\right. \tag{16}\]  
where \(P_{task}^{wind}\) and \(P_{task}^{ev}\) are the FR tasks allocated to DFIG and EVA, respectively.  
**Mode 2:** If \(P_{task}>0\), \(P_{task}\) will be allocated to DFIG first. If \(R_{up}^{wind}<P_{task}\), the DFIG cannot accomplish the system FR task alone, so the shortage will be allocated to EVA. Thus, the FR tasks of DFIG and EVA are shown in (17):  
\[\left\{\begin{array}{ll}P_{task}^{wind}=R_{up}^{wind}\\ P_{task}^{ev}=P_{task}-R_{up}^{wind}\end{array}\right. \tag{17}\]  
**Mode 3:** If \(P_{task}<0\), \(P_{task}\) will be allocated to EVA first. If the up-regulation capacity of EVA is more than the system FR task, i.e., \(R_{down}^{ev}\leq P_{task}\), all the FR power output will be provided by EVA, while the DFIG will not participate in FR. Thus, the FR tasks of DFIG and EVA are shown in (18):  
\[\left\{\begin{array}{ll}P_{task}^{wind}=0\\ P_{task}^{ev}=P_{task}\end{array}\right. \tag{18}\]  
**Mode 4:** If \(P_{task}<0\), \(P_{task}\) will be allocated to EVA first. If \(R_{down}^{ev}>P_{task}\), the EVA cannot accomplish the system FR task alone, so the shortage will be allocated to DFIG. Thus, the FR tasks of DFIG and EVA are shown in (19):  
\[\left\{\begin{array}{l}P_{task}^{ev}=R_{down}^{ev}\\ P_{task}^{wind}=P_{task}-R_{down}^{ev}\end{array}\right. \tag{19}\]  
**Mode 5:** If \(P_{task}=0\), DFIG and EVA will not participate in FR. Thus, the FR tasks of DFIG and EVA are shown in (20):  
\[\left\{\begin{array}{l}P_{task}^{ev}=0\\ P_{task}^{wind}=0\end{array}\right. \tag{20}\]
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 5 Performance Evaluation
It can be observed that, regardless of whether \(v\) = 5 kn or 10 kn, the BER decreased as the number of outer-loop iterations increased. There was a minimal difference in BER performance between three iterations and two iterations. Therefore, subsequent simulations employed two outer-loop iterations. To comprehensively analyze the IDF-MP algorithm proposed in this study, we compared its BER performance with OFDM, an initial estimate iterative decoding feedback (IE-IDF-MRC) algorithm [17], and a two-dimensional passive time reversal decision feedback equalization (2D-PTR-DFE) algorithm [21]. The IDF-MP algorithm is built upon the proposed dual-domain joint channel estimation technique, distinguishing it from other algorithms that rely on the OMP method for channel estimation. Both the IE-IDF-MRC and IDF-MP algorithms were configured with an outer-loop iteration count of 2, and LDPC codes were employed for channel coding across all algorithms, achieving a communication rate of 2.5 kbps. As shown in Figure 10, the results demonstrated that the OTFS communication system outperformed the OFDM system, regardless of whether \(v\) = 5 kn or 10 kn, with a more pronounced performance difference observed under severe channel time variations. Specifically, the IDF-MP algorithm proposed in this study exhibited the best performance. This superiority can be attributed to the IDF-MP algorithm's utilization of the soft information output from LDPC decoding as prior information for the MP detector, facilitating iterative information exchange that minimizes information loss and enhances the system's detection and decoding performance. Figure 9: BER comparison of the IDF-MP method under different iterations.
###### Abstract  
The monoethanolamine (MEA)-based post-combustion CO\({}_{2}\) capture plant must operate flexibly under the variation of the power plant load and the desired CO\({}_{2}\) capture rate. However, in the presence of process nonlinearity, conventional linear control strategy cannot achieve the best performance under a wide operation range. Considering this problem, this paper systematically studies the multi-model modeling of the MEA-based CO\({}_{2}\) capture process for the purpose of (1) implementing well-developed linear control techniques to the design of an advanced controller and (2) achieving a wide-range flexible operation of the CO\({}_{2}\) capture process. The local linear models of the CO\({}_{2}\) capture process are firstly established at given operating points using the method of subspace identification. Then the nonlinearity distribution at different loads of an upstream power plant and different CO\({}_{2}\) capture rates is investigated via the gap metric. Finally, based on the nonlinearity investigation results, the suitable linear models are selected and combined together to form the multi-model system. The proposed model is validated using the measurement data, which is generated from a post-combustion CO\({}_{2}\) capture model developed in the go-carbon capture and storage (gCCS) simulation platform. As the proposed multi-linear model has a simple mathematical expression and high prediction accuracy, it can be directly employed as the control model of a practical advanced control strategy to achieve a wide operating range control of the CO\({}_{2}\) capture process.  
monoethanolamine-based post-combustion CO\({}_{2}\) capture; multi-model modeling; nonlinearity investigation; subspace identification; control design +
Footnote †: journal: Applied
## 3 Results
### Subgroup Analyses for 6-Minute Walk Test Dynamics  
Compared with HIIT and CG, CE significantly improved cadence (F = 7.69, \(p\) = 0.002), walking distance (F = 4.07, \(p\) = 0.025), HRmax (F = 6.41, \(p\) = 0.004), and exercise intensity  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Mean Difference Pre and Postintervention**} & & & & \\  & & **n** & **CE** & **n** & **HIIT** & **n** & **CG** & **F** & \(P\) & **Post Hoc** \\ \hline Cadence & & & & & & & & & & \\ (steps/min) & Per-protocol & 16 & 16.51 (5.11) & 21 & 3.13 (2.80) & 26 & \(-\)0.01 (2.22) & 5.02 & 0.009 & CE \(>\) CG \\ Distance & & & & & & & & & \\ (meters) & Per-protocol & 16 & 54.70 (23.73) & 21 & 11.07 (8.51) & 26 & 10.97 (6.18) & 2.07 & 0.133 & - \\ HRmax & & & & & & & & & \\ (beats/min) & Per-protocol & 16 & 12.5 (5.06) & 21 & \(-\)5.33 (2.92) & 26 & \(-\)3.88 (2.16) & 5.58 & 0.006 & CE \(>\) HIIT, CG \\ Exercise & & & & & & & & & \\ (beats/min) & Per-protocol & 16 & 12.5 (6.65) & 21 & \(-\)5.33 (2.92) & 26 & \(-\)3.88 (2.16) & 8.18 & \(<\)0.001 & CE \(>\) HIIT, CG \\ Exercise & & & & & & & & & \\ (beats/min) & Per-protocol & 16 & 12.5 (6.65) & 21 & 2.62 (3.57) & 26 & \(-\)2.77 (1.50) & 10.17 & \(<\)0.001 & CE \(>\) HIIT, CG \\ \hline \hline \end{tabular} Mean (SE). CE: combined exercise; HIIT: high-intensity interval training; CG: control group; HRmax: maximum heart rate. ANOVA models were used.  
\end{table}
Table 2: Differences in 6-minute walk test dynamics before and after the intervention.  
Figure 2: Differences in 6-minute walk test dynamics between pre-post interventions. x axes: pre-post measurements for the CE, HIIT and CG groups; y axes: mean values. CE: combined exercise; HIIT: high-intensity interval training; CG: control group; HRmax: maximum heart rate. ANOVA models were used. (**A**) Cadence (steps/min), (**B**) Distance (meters), (**C**) HRmax (beats/min), (**D**) Exercise intensity (%).  
(F = 5.12, \(p\) = 0.012) in patients aged \(\geq\) 70 years. Compared with the CG, the CE significantly improved HRmax (F = 4.46, \(p\) = 0.019) and exercise intensity (F = 15.47, \(p\) < 0.001) in male participants. Similarly, in participants with a baseline cadence < 120 steps/min, compared with the CG, the CE significantly improved cadence (F = 3.27, \(p\) = 0.050) and exercise intensity (F = 3.76, \(p\) = 0.031) (Table 3).  
Associations Between Changes in Cadence and Distance, HRmax, and Exercise Intensity in the 6-Minute Walk Test  
Figure 3 shows the associations between changes in cadence and other 6 MWT parameters, such as distance, HRmax, and exercise intensity. The strongest correlation was observed between cadence and distance covered in the 6 MWT for the overall group (r = 0.762, \(p\) < 0.001), with CE showing a strong correlation (r = 0.684, \(p\) < 0.001) and HIIT also demonstrating a significant association (r = 0.413, \(p\) = 0.036). Interestingly, while cadence was significantly
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### THM Coupling Models  
Research on THM coupling in rocks has advanced significantly. This multi-field approach is essential for understanding the complex interactions within rock formations under varying environmental conditions. THM coupling is particularly relevant in contexts such as geothermal energy extraction, underground mining and geothermal energy [152], carbon sequestration, and nuclear waste disposal, where rock stability and permeability are critical. Formulated governing equations for THM coupling use principles of mass conservation, momentum balance, and energy conservation, detailing processes at each coupling stage [156; 157; 168]. Key interactions within the THM coupling framework include (1) temperature-induced stress and strain, (2) rock thermal property changes due to internal heat dissipation, (3) variations in porosity and fracture width, (4) water pressure effects on stress, (5) fluid property shifts with temperature, and (6) fluid thermal convection effects, as shown in Figure 8.  
To understand the thermal responses of claystone rocks under thermal loading, it is essential to adopt a THM framework [153; 154; 155; 156; 169]. Within this framework, temperature changes significantly impact hydraulic behavior by altering both fluid viscosity and pore pressure through thermal pressurization. This temperature-induced pressure increase affects the permeability and flow characteristics of pore water, integrating TH interactions. Simultaneously, TM coupling arises as temperature shifts create thermally induced strains, which affect rock stability and stress distribution.  
HM interactions further enhance the complexity of the THM behavior; hydraulic changes, such as variations in pore water pressure, directly influence the mechanical behavior by altering effective stresses in the rock matrix, while strain variations impact pore pressures and adjust intrinsic permeability [76; 77; 170]. Although hydraulic processes can theoretically influence thermal behavior through heat convection, in low-permeability claystone, such thermal-hydraulic effects remain minor due to limited fluid movement [72]. This interdependence of THM processes forms a multi-directional interaction network crucial for predicting rock behavior under thermal loads, particularly in low-permeability geomechanical contexts.  
[PERSON] et al. [168] studied the deformation of fractured rock mass under the coupling action of three fields in nuclear waste treatment and proposed the following problems: Figure 9 shows a schematic diagram of the three-field coupling of a nuclear waste repository.  
Figure 8: Conceptual model of the coupled THM.  
Figure 9: Interrelationships between hydrothermal processes.
# Applications of Plant Polymer-Based Solid Foams: Current Trends in the Food Industry
## 2 Food Industry Applications
### Plant Polymer-Based Foams as Food Packaging Materials  
The distribution chain of the food industry is heavily dependent on appropriate packaging materials. When considering the performance required for prolonging shelf life, the properties of most interest in packaging applications, and their relevance in use, can be classified as mechanical and optical properties, chemical stability, and moisture and gas barrier function [69].  
For several years, conventional, non-biodegradable plastics have been the principal source of material for food packaging. Many already known reasons are responsible for the wide use of plastics, e.g., they are generally lighter in weight, more easily formed into different shapes, extremely versatile, and have a low cost of production [69]. However, severe concerns related to environmental catastrophe have set suitable conditions for the development of alternative eco-friendly materials derived from natural polymers for packaging [8].  
A natural packaging material must fulfil the demands made by the food industry. For example, it should serve as a physical barrier and temperature controller--that is, it must have a high impact resistance and high thermal insulation. It also should act as a biochemical and microbiological preserver (e.g., gas exchange regulator). Equally important is that its production and disposability have a low carbon footprint, as well as low pollution risks and greenhouse gas emissions. Meeting these criteria, plant-derived biopolymers from agro-industrial sources that are renewable, abundant, and inexpensive would present a convenient and attractive alternative [55].