esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Flexible Resistive Random Access Memory
## 2 Basic Working Principle of Flexible RRAM
### Resistance Transition Mechanism of Flexible RRAM  
In flexible RRAM, the main resistance transition mechanisms are the interface barrier mechanism [14; 15; 16] and conductive filaments mechanism [17; 18].  
The interface barrier mechanism means that the resistance change in devices is controlled by the height of the Schottky barrier or the width of the depletion layer at the interface, that is, the higher the Schottky barrier or the width of the depletion layer, the higher the resistance of devices, showing HRS. Conversely, the device exhibits LRS. The change in barrier height is usually caused by the accumulation of charge or migration of oxygen ions under the action of the electric field [16]. The mechanism is based on the quantum tunneling effect, and its LRS and HRS correspond to Poole-Frenkel carrier transport [14] and Schottky launches [15], respectively.  
The conductive filament mechanism means that the device forms conductive filaments that run through the whole dielectric material and connect the two electrodes, depending on the migration of metal ions or oxygen vacancies. When the forward voltage is applied to the active metal electrode, the metal atoms will become metal ions (M \(-\) ze\({}^{-}\)\(\rightarrow\) M\({}^{2+}\)) and then migrate to the inert electrode under the action of the electric field. When the metal ions pass through the dielectric and reach the inert electrode, they will go back to metal atoms (M\({}^{2+}\) + ze\({}^{-}\)\(\rightarrow\) M). Depending on the migration of metal ions, the device forms conductive metal filaments that connect the two electrodes, and the device is at LRS. If the voltage is  
Figure 1: (**a**) Schematic of a flexible crossbar resistive switching memory array bent with radius R; (**b**) schematic memory cell structure with measurement configuration.  
applied in the opposite direction, it will disconnect the conductive filaments and transform the device to HRS. Another conductive filament based on oxygen vacancy is mainly used to explain the resistance transition mechanism of oxide. Under the action of the electric field, oxygen ions migrate in the dielectric and form oxygen vacancies, resulting in conductive channels. The device of Ag/HfO\({}_{2}\)/Pt was studied to research the conductive filament mechanism by [PERSON] [17]. It was found that the implanted electrons generate oxygen vacancy on the inert electrode (Pt) and the active electrode (Ag) through the formation of Ag\({}^{+}\) to form conductive filaments so that the device has bistable characteristics.
## 3 Results
### Diffusion-Based Models
#### 3.3.1 Evaluation of Film Mass Transfer Coefficient  
W-M studies permitted us to show that the external film diffusion took place only at the early stage of the adsorption kinetics.  
Considering the film-surface diffusion model proposed by [PERSON] and [PERSON] [63], the initial adsorption rate \(r_{o}\) could be determined (see Section 2.9). Plotting \(t/Q_{t}\) against time, good straight lines were obtained with correlation coefficients ranging from 0.9988 to 0.9996 for the different ion exchange reactions studied (Figure 6). From the slope and intercept values, the \"\(r_{o}\)\" and \"\(a\)\" constants were determined, and results are listed in Table 2. The \(k_{f}\) value was then determined and is given in Table 2.  
To summarize, Equation (3) permitted us to estimate the film mass transfer coefficient (\(\rm{k_{f}}\)) through the initial adsorption rate (\(\rm{r_{o}}\)). This latter was determined at the early adsorption  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Film–Surface Diffusion** & **Cs\({}^{+}\)** & **Rb\({}^{+}\)** & **Co\({}^{2+}\)** & **Sr\({}^{2+}\)** \\ \hline \(\rm{r_{0}}\) (\(\rm{\mu mol\cdot g^{-1}.m\cdot m^{-1}}\)) & 1666.67 & 869.57 & 312.50 & 1282.10 \\ \hline \(\rm{a}\) (\(\rm{mn^{-1}}\)) & 6.23 & 2.62 & 2.25 & 4.79 \\ \hline \(\rm{k_{f}}\) (\(\rm{m\cdot s^{-1}}\)) & 0.005141 & 0.002556 & 0.00193 & 0.004229 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Film mass transfer coefficient evaluated using the film–surface diffusion model proposed by [PERSON] and [PERSON] [63].  
Figure 6: Plotting of \(t/Q_{t}\) (in g-mm-\(\rm{\mu mol^{-1}}\)) against time (mn). Application of film-surface diffusion model proposed by [PERSON] and [PERSON] [63] for adsorption kinetics of cations (Cs\({}^{+}\), Rb\({}^{+}\), Co\({}^{2+}\), and Sr\({}^{2+}\)) onto a brick composite in aqueous medium.  
stage (i.e., for a short adsorption time of less than 10 min for Cs+, Rb+, and Sr2+ and 14 min for Co2+, as shown in Figure 6). The k\({}_{\text{f}}\) values will be used in Section 3.4 for modelling breakthrough curves obtained under dynamic (fixed-bed column) operational conditions.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations  
There are two main types of approximations for Gaussian convolution filters: Finite Impulse Response (FIR) filters and Infinite Impulse Response (IIR) filters. Each of these types has distinct characteristics and trade-offs that affect their performance and implementation complexity. FIR filters are often desirable when exact precision is required as they provide a closer approximation to the Gaussian kernel. It makes them well-suited for applications that demand accurate filtering, such as image enhancement and feature extraction. IIR filters are more suitable for applications where computational efficiency  
Figure 1: Convolution of an image with the kernel of size \(5\times 5\).  
is of primary concern. Their recursive nature allows for efficient computation, making IIR filters appealing for real-time applications or scenarios where processing power is limited. They find applications in real-time video processing, audio signal processing, and communication systems.  
In this section, we describe several of the best-known FIR and IIR filters proposed over the last few decades.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.1 Failure Recovery Time  
Figure 8 shows and compares the link failure recovery time among three schemes, i.e., the FAPR, and proactive and reactive schemes. For the FAPR and proactive schemes, the failure recovery time refers to the duration from the onset of a link failure to the moment when the switch automatically transitions to a backup path, allowing the flow to continue forwarding. In contrast, for the reactive scheme, it denotes the time from the initiation of a link failure to when the switch receives and implements the backup path dispatched by the controller, enabling the flow to resume forwarding. It can be observed that the failure recovery time of the FAPR and the proactive schemes is significantly less than that of the reactive scheme, as the latter requires the intervention of the controller to participate in the recover after link failures. More specifically, the failure recovery time of the FAPR and the proactive schemes is generally within 20 ms, while the reactive scheme can reach more than 60 ms, and it may be even higher if the communication between the switch and the controller is in heavy burden. At the same time, it can also be found that the failure recovery time in these three schemes is not significantly related to the number of flows. In addition, Figure 9 elucidates the distribution of failure recovery times, revealing the FAPR and proactive schemes' stable performance, as evidenced by their tight interquartile ranges (IQRs). This stability is due to the automated path switching by the switch, precluding controller involvement. In contrast, the reactive scheme's broader IQR and outliers reflect its variability, arising from the necessity of controller-switch communication post-failure. This round-trip communication is subject to latencies from network congestion and controller processing, influencing the recovery time's unpredictability, which is particularly critical in time-sensitive network environments.  
Figure 8: Recovery time using three schemes.
## 3 Results and Discussion
### Forest-Non-Forest Classification  
The classification that was carried out over the HH and HV polarizations and all the manipulations found that PALSAR-2 images have different capability in defining forests. The study demonstrated that the most effective polarization for FNF classification was the HV. However, the HH polarization was found effective on delineating plantation areas, such as rubber and teak, because the orientation of the plantations is systematic and homogenous, which can be interpreted well by the HH polarization. The classification was made based on the major forest types found in Peninsular Malaysia and the results are summarized in Table 5. However, this study concentrated only in lowland, hill, and upper hill dipterocarp forests. The breakdowns of these forest types are summarized in Table 6. The classification results were compared with the land use map for the year 2014 that was produced by the Department Agriculture Peninsula Malaysia and the classification accuracy was attained at 91.3% with a kappa coefficient of 0.88. The remaining 8.7% belonged to errors due to misclassification of secondary forest and rubber plantation as defined on the land use map. The results were reliable because the classification interested only in distinguishing forests from other land covers.
# Ground Simulation Tests in Two-Dimensional Dynamic Acceleration Environment
## 3 Simulation of Impact Overload Environment
### Speed Control System Based on Active Disturbance Rejection Control
The load is rotating in the same direction as the motor and the speed difference between the load and the motor is small, so the change of the speed will become small and the recovery time will get short. Accompanied with the improved disturbance-rejection performance of the system, an orderly transition between the impact overload and steady-state overload can be achieved.
## 2 Materials and Methods
### Proposed Approach
#### 2.1.5 Evaluation Metrics  
We present the results of the proposed approach using the F1-score, which is the harmonic mean of recall and precision, as an evaluation metric for comparison with other work. The F1-score is a frequently used metric for the classification model effectiveness [28]. Additionally, in all experiments, accuracy was calculated and presented. The formulas of the metrics presented in this study are listed below.  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{1}\]  
\[\text{Precision}=(\text{TP})/(\text{TP}+\text{FP}) \tag{2}\]  
\[\text{Recall}=(\text{TP})/(\text{TP}+\text{FN}) \tag{3}\]  
\[\text{Specificity}=(\text{TN})/(\text{TN}+\text{FP}) \tag{4}\]  
\[\text{F1-score}=2(\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall}) \tag{5}\]  
where TP, TN, FN, and FP represent the: True Positive, True Negative, False Negative, and False Positive, respectively.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 6 Iris Neovascularization
### OCTA for Iris Neovascularization  
A potential alternative is the use of OCTA adapted for the anterior segment. [5] While current commercially available OCTA are designed to examine the posterior segment of the eye, an adapter lens can provide high-quality images of the anterior segment vasculature with good inter-observer agreement for qualitative measurements [1]. [PERSON] et al. showed neovascularization of the iris is characterized on OCTA by random orientation of vessels, significant tortuosity, and smaller caliber (Figure 9B). They demonstrated that OCTA is also effective in detecting subclinical NVI [6]. [PERSON] et al. proposed staging criteria for NVI using en face and cross-sectional OCTA based on the established staging system by [PERSON] and [PERSON]. Stage 1 is defined when fine, thin-walled irregular  
Figure 9: A case of iris neovascularization secondary to ocular ischemic syndrome. Slit lamp photograph (**A**) corresponding OCTA of the iris (**B**) showing fine new vessels at the pupillary border (yellow arrows) and slightly larger new vessels on the anterior surface of the iris (blue arrows).  
vessels at the pupillary border and iris root are visualized, stage 2 is where these vessels enlarge and penetrate to the anterior iris surface and stroma, and stage 3 is where two sets of NVI merge and this NVI covers parts of the anterior surface of the iris, with or without the presence of peripheral anterior synechiae. They added a fourth stage for regressed NVI seen after treatment [6].  
OCTA has a role in the evaluation of anterior segment vasculature in the setting of anti-angiogenic therapy, where regression of NVI can serve as markers of VEGF levels therefore guiding re-treatment decisions [4]. This may become a useful tool for clinical surveillance of proliferative and ischemic changes in DR [37]. However, OCTA of the anterior segment is still limited as it cannot visualize leakage and hence activity of new vessels [79].
## 2 Related Studies  
### YOLOv4  
YOLO [26] is a representative object-detection algorithm. YOLO predicts multiple bounding box coordinates and class probabilities simultaneously in a single convolutional network without using region proposals, which are used to locate objects in existing R-CNN models [27; 28; 29]. Therefore, the processing speed of YOLO is much higher than that of existing algorithms and real-time object detection is realizable. YOLOv2 [30] improves the model performance using batch normalization, anchor box, direct location prediction, and multiscale training methods. YOLOv3 [31] uses a bounding box with a multiscale and residual block to solve the problem of small object detection in the existing YOLO. YOLOv4 improves the model performance by applying cross-stage-partial-connection [32], spatial pyramid pooling (SPP) [33], and path aggregation network (PAN) [34]. YOLOv4 comprises a backbone, neck, and head. The backbone of YOLOv4 is CSPDarknet53 that comprises batch normalization and Mish activation functions. In CSPDarknet53, only one-half of the feature map passes through the residual block; therefore, a bottleneck layer is not required. The neck connects the backbone to the head and reconstructs the feature map. The neck of YOLOv4 is composed of SPP and PAN. SPP effectively increases the receptive field using contextual features extracted from four max-pooling layers. The PAN improves the overall feature layers and localization performance by expanding the path used for information flow between the lower layer and uppermost feature. The head has the same structure as YOLOv3 and predicts the prediction boxes using feature maps of three different scales. In addition, data augmentation using a new technique known as mosaic is used in YOLOv4 to improve the object detection ability of the training data. Figure 1 shows the architecture of YOLOv4.
# Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
###### Abstract  
Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was \(L_{\text{max}}=3\), the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and \(L_{\text{max}}\) was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and \(L_{\text{max}}\) to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.  
multi-agent system; contract net protocol; task assignment; response threshold; pheromone
## 2 Background and Experiment  
The experiment was carried out in Qitai County, Xinjiang, China, a map of which is shown in Figure 1. Xinjiang province is China's largest provincial administrative region, with an area of 1,660,000 km\({}^{2}\). Since it is far from the sea and located on the northwest border of China, this region has a typical temperate continental climate, with a mean yearly precipitation of 150 mm, and drought tolerant crops. Qitai County is located in the north of Xinjiang Province. The mean temperature of this area is 6.6 \({}^{\circ}\)C, and the mean precipitation is 245 mm. According to the statistical data, cotton is the main crop grown in the county.  
The 99 m \(\times\) 25 m study area was established in Qitai County, which is a small part of a farm with more than 133 hectares of cotton fields in Qitai County, as shown in the bottom right of the image. The study area was sprayed with herbicide on 15 April 2017, and then the cotton was planted on 17 April 2017 by using an automatic sowing machine with spacing of 70 cm. The cotton image collection experiment was performed on 12 May 2017, 14:25 UTC, when the height of most cotton plants was about 8 cm. The UAV used in the study was the DJI phantom 4 with an RGB camera with a resolution of 4000 \(\times\) 3000 and focal length of 4 mm carried on. The flight was carried out at a flying altitude of 15 m, which provided a ground sampling distance (GSD) of 5.2 mm/pixel, and the flying speed was 1.5 m/s\({}^{-1}\). Additionally, the overlapping along the direction of flight was set to 75%, and the overlapping across the flight was 60%. During the experiment, the weather conditions for this day were sunny and breezy, with a maximum temperature of 28 \({}^{\circ}\)C and a low of 16 \({}^{\circ}\)C. The experiment obtained total 156 images, and 118 images among them were selected, as the bottom figure showed in Figure 1.
# Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model
## 5 Discussion  
Based on the above fault analysis of the entire actuation system, as well as each of its internal components, it can be concluded that the fault coupling caused by dissipated energy will have different effects on actuators in different positions, which is mainly related to the intensity of field energy formed by the accumulation of dissipated energy transferred in space. More dissipated energy is accumulated in the central position of the actuation system, resulting in more serious degradation of the components, while the components at the edge position are less affected by the dissipated energy. The degradation rates of injection systems and components with different faults are also different. Among these faults, fatigue is the most serious, and the motor fault will have a greater impact on the hydraulic pump and hydraulic cylinder due to local fault coupling. However, the tie rod at the rudder surface, as a common component of EHA and HA, the failure will accelerate the degradation of the spare actuator after switching between the actuators. As a result, the reliability of the entire actuation system rapidly decreases, and the actuation system fails prematurely. In the design and fault detection of MEA actuation, more attention should be paid to the degradation of common components in the redundant system and the monitoring of fatigue stress factors, so as to improve the safety of MEA. In this study, the distance between the components of the drive system and the probability distribution of the three degradation parameters of wear, fatigue, and aging were hypothesized. In addition, although multiple fault injection methods are used in fault analysis, single fault injection is still used in reliability evaluation, which still has some limitations. In future work, multiple faults will be injected simultaneously according to the parameter distribution in the reliability evaluation to further explore the impact of fault propagation in the system on the system's reliability. In addition, the application of the model in practical scenarios will be studied further.
## 3 The Time Source Techniques
### Global Navigation Satellite Systems  
Global Navigation Satellite Systems (GNSSs) provide services for positioning, navigation, and timing. GNSSs include GPS, GLONASS, Galileo, and Beidou, all of which have similar functions [5]. In this paper, GPS is used as a representative example.
## 3 Experimental Setup
### Application of the Classification Tool  
The automatic annotation tool developed in this study offers a highly user-friendly interface. Using the classification modification correspondence table, the system can automatically generate updated annotations from the original data. When data classifications are altered, the administrator first suspends the ongoing annotation tasks. Then, the automatic annotation personnel handle the automated updating of annotation files. As illustrated in Figure 5, the system automatically updates annotated images according to the new classification scheme.  
Figure 5: Partial results of automatic annotation updates performed by the classification tool.
# Review of Hygroscopic Coating on Aluminum Fin Surface of Air Conditioning Heat Exchanger
## 2 Desiccant-Coated Heat Exchangers  
Many kinds of research have proven that separating to control indoor sensible and latent load has the potential to significantly increase the energy efficiency of the conventional air conditioning system [15; 16; 17; 18]. Desiccant is widely used to remove latent load in the air [19; 20]. According to the principle of mesoporous adsorption [21], when the humid air is in direct contact with the dry desiccant, the water molecules will be adsorbed into the mesoporous of the desiccant to achieve the purpose of dehumidifying the air. The desiccant cannot dry the humid air indefinitely, and the moisture absorption rate decreases as the moisture content of the desiccant increases. When the desiccant reaches the critical point of moisture absorption, the dehumidification will be stopped. To continue the operation, removing the water molecules in the desiccant is essential; it is called the regeneration process, which can be carried out by adopting solar energy and low-grade energy.  
Desiccant-coated heat exchangers (DCHE) were fabricated by directly coating desiccant material to the outer surface of the conventional fin tube heat exchanger, shown in Figure 1[22]. When the air conditioning cooling dehumidification mode is turned on, the humid air would flow through the heat exchanger and be in direct contact with the desiccant to remove the latent load; at the same time, a cooling fluid would pass through the tubes to handle sensible load [23; 24]. It will provide hot water to the DCHE after the desiccant reaches the adsorption saturation point, and the water will be released in the desiccant to realize the regeneration of the desiccant. The recent advances of DCHE will be explained in the following aspects: Desiccant materials; factors affecting the performance of DCHE, and applications in air conditioning systems.