esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 6 Mechanism of Nanoformulated Peptides in Disease Theranostics
### Endosomal Disrupting Peptide  
Endosomal escape is a major challenge for the poor nanocarrier-mediated delivery of therapeutics, due to the endocytic pathway for cellular internalization of entrapped endosomes and degradation risk via active enzymes and acidic pH [159], as shown in Figure 4. It is easy to overcome the nanomedicine's endosomal escape challenge via endosomal disrupting peptides, such as fusogenic and melittin.  
Fusogenic peptides are pH-dependent, endosomal-disrupting peptides [155]. They induce endosomal escape with various mechanisms, such as the formation of an intermediate membrane state and membrane (endosome) disruption during the fusion process, later releasing the therapeutic drug content into the cytosol. It can be noted that the nanoformulated fusogenic peptides have improved the efficacy of this delivery system. Recently, [PERSON] et al. (2018) developed a dual-functionalization of smart polymersomes with targeting and fusogenic peptides (cRGD/GALa-Ps) to deliver cytochrome C (CC) to A549 human lung cancer cells. They discovered cRGD/GALa-Ps has great loading efficiency for CC, which is between 60.5% and 62.5%, and better endosomal escape of FITT-CC, as shown when compared with cRGD-Ps via confocal microscopy studies. Cell apoptosis of cRGD/GALa-Ps (with 4 mol% GALA) was greater than cRGD-Ps alone, which is 36.4% versus 14.4%. This smart polymersome revealed its ability to boost the cytosolic release of CC, which helps to enhance apoptosis and anticancer activities [161]. Further, [PERSON] et al. (2021) designed D-melittin polymeric micelles (DMM) for anticancer treatment by inducing hemolysis via a pH-dependent approach and immunogenic cell death. The in vivo studies revealed that the nanoformulated peptide not only boosted the efficacy of the tumor growth prohibition, but also increased the survival rate, compared to PBS and D-melittin-treated mice [162]. Moreover, a nanoformulated endosomal disrupting peptide was also proposed, in recent studies, to be beneficial in the treatment of other diseases in the future [163; 164; 165].
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 3 Experiments
### Training of Object Detection and Embedding Model
#### 3.2.1 Image Reconstruction Losses  
The efficacy of image reconstruction serves as an indicator of a model's ability to encode states. A superior image reconstruction quality implies a richer representation of target image information within the embedding. This study predominantly focuses on L2 and L1 losses, omitting PSNR due to its strong correlation with L2. This research adopts the _SSIM_[21] for evaluating the similarity between reconstructed and original images. _SSIM_ diverges from traditional pixel-based quality assessments by prioritizing changes in image structure measured through luminance, contrast, and structure. The _SSIM_ value, ranging from \(-1\) to \(1\), signifies similarity, with values closer to 1 indicating greater resemblance. The _SSIM_ is computed as follows:  
\[SSIM(A,B)=\frac{(2\mu_{A}\mu_{B}+C_{1})(2\sigma_{AB}+C_{2})}{\big{(}\mu_{A}^{2} +\mu_{B}^{2}+C_{1}\big{)}\big{(}\sigma_{A}^{2}+\sigma_{B}^{2}+C_{2}\big{)}} \tag{7}\]  
where \(A\) and \(B\) represent two images for comparison, \(\mu_{A}\) and \(\mu_{B}\) denote their mean luminance values, \(\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) their variances, \(\sigma_{AB}\) Is the covariance, and \(C_{1}\) and \(C_{2}\) are constants to stabilize the denominator.  
As shown in Figure 9, this experiment shows that L1 loss facilitates superior image reconstruction, outperforming L2 loss, regardless of the employment of image scaling as a data augmentation technique. Furthermore, the models demonstrated enhanced capability in reconstructing target images without the utilization of image scaling during training.  
Figure 9: _SSIM_ change curve under different reconstruction loss functions and scaling strategies during the training phase.
## 2 Materials and Methods
### Model Calibration and Validation
#### 2.4.1 SUFI-2 Algorithm
The r-factor is the average thickness of the 95 PPU band divided by the standard deviation of measured data. A low r-factor describes less uncertainty and that approaching 1 indicates high uncertainty in a certain parameter [23]. SUFI-2 operates by performing several iterations. In each iteration, the parameter ranges get smaller, zooming in on the region of parameter space that produces better results than the previous iteration.
## 2 Materials and Methods
### System Components
#### 2.4.5 Battery Energy Storage (BES)
\[\frac{dA}{dt}=-I_{t}-k(A_{t}-c\times C_{0}),\quad\frac{dB}{dt}=-k(B_{t}-(1-c) \times C_{0}) \tag{16}\]  
where \(A_{t}\) is the available capacity at time \(t\), \(B_{t}\) is the bond capacity at time \(t\), \(C_{0}=A_{t}+B_{t}\) is the nominal total capacity, \(k\) is the rate constant (\(1/hr\)), \(c\) is the capacity ratio (0 to 1), and \(I_{t}\) is the BES current at time \(t\) (positive for discharge, negative for charge). In a discrete-time implementation, HOMER numerically integrates these equations at each step. The result is that at high discharge rates, the available portion \(A_{t}\) can deplete quickly, lowering effective capacity. As time passes, some energy from \(B\) flows into \(A\), replenishing it (provided the load is not too high). SOC in KiBaM is often interpreted as \(SOC=\frac{A+B}{C_{0}}\) after factoring out any inefficiencies or unusable reserves. HOMER can estimate BES lifetime or replacement intervals by tracking both cycle life and calendar age. In some cases, it uses cycle-counting techniques that relate the DOD and total cycles to eventual failure, while others rely on a strictly time-based approach over a certain number of years. Another method is a hybrid strategy, which replaces the BES when it reaches a specific time limit, or a defined total energy throughput refers to the total amount of energy discharged over the BES's lifetime. These considerations primarily affect cost calculations and system economics rather than the hour-by-hour state of charge equations. By iterating across all time steps for an entire year (or multiple years, if needed), HOMER finds an optimal BES size and operating strategy under the specified load, generation profiles, and economic parameters. The BES model, including its technical parameters and performance characteristics, is based on established methodologies described in the recent literature [47]. The BES utilized in this analysis is a generic 1 kWh lead-acid BES with a nominal voltage of 12 V, which leads to a maximum capacity of 83.4 Ah. The configuration includes a string size of 4 batteries, providing a total voltage of 48 V. It features a roundtrip efficiency of 80% and a capacity ratio of 0.403. The BES's maximum charge and discharge currents are 16.7 A and 24.3 A, respectively, with a maximum charge rate of 1 A/Ah, implying the BES can be fully charged or discharged in about 1 h at the mentioned currents. It has a throughput of 800 kWh, ensuring consistent energy storage and discharge capabilities over its lifespan. The initial state of charge or \(SOC_{0}\) is set at 50%, with a minimum allowable state of charge of 20%. This BES plays a crucial role in stabilizing the HES by storing excess energy and supplying it during peak demand or grid outages.
## 3 Functionality of Key Modules  
In the pre-processing stage, only the planar mesh containing the defect contour is needed as a user input for both methods. This first stage--executed by running either the Tcl script 'pre_submodel_LC_args.tcl' or 'pre_derived_LC_args'--processes the workflow depicted in Figure 4 automatically. During the scripted pre-processing in HyperMesh(r), the finite element model is completed: according to the specific requirements of each chosen method, the solver input files are written, and evaluation of the planar curvature and computation of control volume element fractions are also performed for each point along the defect circumference.  
In the post-processing stage, in the case of the sub-case approach (Method 2 as shown in Figure 2b), mainly the fatigue post-processing module 'tools_tcd_ps_esed_main.py' must be called. However, if the superposition of derived results is chosen as an approach (Method 3, Figure 2c), the derived sub-cases must be generated first by the three basic load steps, using the introduced transformation equations. The enlarged dataset is used for fatigue post-processing. This fatigue post-processing code module is independent of the chosen evaluation method (highlighted in Figure 4 as a common code block).  
The functionality of these key modules is outlined subsequently for Methods 2 and 3, and specific modifications are pointed out if one method needs distinct additional features.
## 2 Change Detection with GeoCDX
For the work presented herein, the GeoCDX system was used to perform change detection on imagery from a variety of geographic areas. One of the results (and the only one considered in this work) of the automated GeoCDX change detection processing is a prioritized list of tiles \(256\times 256\) meters in size that are ordered in terms of most change to least change. In typical usage, an analyst would interrogate these results in rank order, making a change _versus_ no change assessment for each tile. As a user progresses through the list of tiles, we seek to leverage knowledge from the tiles that have already been assessed to make predictions about the remaining tiles in the list that contain change or not.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 5 Conclusions  
Aiming to assess the shaft wall damage caused by ore impact, a theoretical model of shaft wall damage for high-depth inclined ore passes is constructed based on field engineering investigations, and the mathematical expression of the total shaft damage volume \(Q_{\text{tol}}\) is derived using the contact mechanics theory. The influences of the ore-pass depth \(H\), ore-pass dip angle \(\theta\), inclined angle of the chute \(\alpha\), shaft diameter \(D\), and ore block size \(P\) on \(Q_{\text{tol}}\) are discussed, and the mechanism of shaft wall damage is revealed. The rationality of the theoretical model is verified by means of numerical simulation and an actual engineering case.  
Under the impact wear behavior of an ore, the ore block size and the structural parameters of high-depth inclined ore passes have different effects on the damage to the shaft wall. With increases in \(\theta\) and \(\alpha\), \(Q_{\text{tol}}\) generally increases first and then decreases. \(Q_{\text{tol}}\) increases exponentially with \(P\) and increases steadily with \(D\). Furthermore, \(H\) affects \(Q_{\text{tol}}\) by influencing the collisions frequency between the ore and the shaft wall. As \(H\) increases, the collision frequency increases, leading to a larger \(Q_{\text{tol}}\).  
Additionally, based on the sensitivity analysis results of the influencing factors, the influence of the ore-pass dip angle \(\theta\) and the inclined angle of the chute \(\alpha\) on the shaft wall damage from ore passes should be mainly considered in the mining design of deep mines. Shaft wall damage can be reduced by reducing \(\theta\) and \(\alpha\) or increasing \(\theta\) and \(\alpha\) until close to \(90^{\circ}\). Subsequently, the mine managers should pay close attention to the damage caused by the ore block size \(P\) to the shaft wall and strictly control the ore block size to prevent shaft wall damage under the impact wear of large ore blocks.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON], [PERSON] and L.X.; formal analysis, [PERSON], H.J. and L.X.; investigation, [PERSON], H.J. andL.X.; resources, [PERSON]; writing--original draft preparation, [PERSON] and H.J.; writing--review and editing, [PERSON], H.J. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YFC2903802.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Thanks for the great efforts of editors and reviewers.  
The authors declare no conflict of interest.
## 7 Performance Analysis
### Simulation Analysis  
It can be seen from the above simulation results that, since the algorithm in this article uses centralized routing calculations, it has stronger load balancing capabilities than other distributed routing algorithms. Therefore, there are fewer ISLs with the highest level of load in the failure area. At the same time, the packet loss rate in the network is also lower. However, it does not have such an obvious advantage in terms of delay. This is partly because the higher packet loss rate leads to inaccurate delay statistics. In addition, since the algorithm in this article takes many detour paths, although it can avoid congested links and reduce queuing delay, the propagation delay is also increased. The increase in propagation delay is more significant when the sun outage lasts longer.  
The three alternative paths proposed in this article are the shortest path, the second shortest path and the detour path. The second shortest path is used as a substitute for the shortest path. Most of the links in the path coincide with the shortest path, so the probability of being selected is low. The detour path is quite different from the shortest path. By sacrificing part of the network delay performance in exchange for network load balancing, when the network load is high, the probability of it being selected is also higher. However, the selection of alternative paths can be further optimized. The detour path only considers the congestion status of the link before the sun outage, and does not consider whether it excessively overlaps with the paths of other services in the algorithm. The algorithm performance can be further improved by adding more detour paths, but this will also increase the computational complexity of this algorithm.
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 3 Basic Mechanical Properties
### Experiment Methods
#### 3.1.4 Bending  
Three-point bending tests are another testing method focusing on the fracture properties of NMMFs. Although tensile testing and micropillar compression are considered to be a direct and effective way determining the intrinsic plasticity/ductility properties, owing to difficulties with the experiments, three-point bending tests can be chosen to acquire the processes of deformation and fracture. Manufacturing a precrack, loading to induce crack propagation, measuring the critical stress needed to inflict fracture, and observing the fracture morphology is followed as a simple procedure to conduct. Figure 10 is the schematic illustration of three-point bending apparatus used for localized fracture experiment in NMMF [67]. It should be noted that the NMMF must be thin enough relative to the substrate in order to eliminate the effects of stress non-homogeneity at the crack tip. Figure 11 is a fractography of Cu/Au multilayer specimens [67]. During the bending processes, the energy difference (\(\Delta U_{e}\)) would represent the energy required to enable cracking of the film if the substrate remained elastic. The critical energy release rate \(\xi_{c}\) can be described as [68]:  
\[\xi_{c}=-\frac{dU_{e}}{dA_{c}}=-\frac{-\frac{1}{2}\Delta F^{2}\cdot dC}{2h \cdot dr} \tag{15}\]  
where \(U_{e}\) is the elastic energy, \(A_{c}\) is the area of the crack, \(h\) is the film thickness, \(dC/dr\) is the change in the compliance (\(C\)) of the film with respect to the change in crack length (\(r\)), \(\Delta F\) is the measured force difference. For NMMFs, fracture toughness can be calculated through:  
\[K_{c}=\sqrt{E_{c}^{\xi_{c}}} \tag{16}\]
## 4 Discussion
Future studies should also consider the use of high spatial and temporal resolution data from a constellation of satellites, especially if reflectance data from the NIR, SWIR-1 and SWIR-2 spectral regions are available for disturbance analysis. For instance, daily observations from the PlanetScope satellite constellation with spatial resolution of 3 m should be tested over the savannas.
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 2 Literature Review
### YOLO and Object Detection Techniques  
Object detection systems are generally categorized into two groups: two-stage and single-stage detectors. Two-stage detectors, which have a more complex architecture, first identify regions of interest and then pass these regions to a convolutional neural network for further analysis. Single-stage detectors, on the other hand, identify objects in one step using a simpler architecture [18].  
Typically, two-stage detection systems, such as R-CNNs, achieve a higher accuracy than single-stage systems. However, single-stage detectors perform faster detections. Notably, the advent of YOLO (you only look once) and its subsequent iterations has significantly enhanced the accuracy of single-stage systems, sometimes surpassing that of two-stage systems [20].  
Both YOLO and Faster R-CNN are transformative in precision agriculture for tasks like detecting and monitoring crop pests and diseases, thereby aiding in resource optimization and management. While both systems are effective for various environmental monitoring tasks, YOLO's flexible architecture makes it particularly adaptable to specific needs [21].  
Although R-CNNs generally offer better accuracy compared to YOLO, the latter also provides substantial accuracy and can sometimes outperform R-CNNs. In this study, YOLO was chosen, because R-CNNs require more advanced computational resources, such as state-of-the-art GPUs, making it less accessible for many users [18; 20].
## 1 Introduction
The adapted anisotropic Gaussian SIFT(AAG-SIFT) strategy [17], addressing the issue of edge detail blurring, modified the standard SIFT algorithm by incorporating an adaptive Gaussian blur filter, thereby enhancing the stability and reliability of feature points. [PERSON] et al. [18] proposed a SIFT algorithm based on structure tensors, utilizing tensor diffusion techniques to construct scale levels and extract features in the scale space. [PERSON] et al. [19] introduced a SAR-SIFT algorithm based on the Range-Doppler (RD) model, incorporating the RD model into feature matching and geometric transformation estimation, and designing a novel local matching approach based on the RD model. [PERSON] et al. [20] presented a two-step matching method based on SIFT, utilizing global matching to establish coarse mapping relationships and employing a refinement matching strategy for precise matching of SAR images. Currently, most existing research focuses primarily on the matching accuracy, overlooking efficiency and feasibility issues in practical application processes [21; 22]. Simultaneously, with the increase in algorithm complexity, matching accuracy may improve but will inevitably impose higher demands on computational resources [23]. Therefore, balancing the precision and efficiency of matching algorithms for better SAR image matching is of paramount significance. This paper is organized as follows: firstly, a brief introduction to the SAR-SIFT image-matching method is demonstrated in Section 2. In Section 3, a GPU-mapped implementation based on the SAR-SIFT algorithm and the corresponding multi-GPU collaborative acceleration strategy, emphasizing the enhancement of matching efficiency for corresponding feature points between satellite SAR images is proposed. Experiments and analysis on feature point extraction and matching are conducted using 21 scenes of high-resolution GF-3 SAR images as illustrated in Section 4, followed by a conclusion in Section 5.
# Fast Screening Algorithm for Satellites Based on Multi-Constellation System
## 4 Experimental Validation and Result Analysis
### GDOP Values of Fast Satellite Screening Algorithms  
The user is located in the Nanjing area. The GDOP values before and after the fast satellite screening algorithm were used in the experiment, and the experiment duration was 24 h with the observation frequency of 0.2 Hz, and the results of calculating GDOP values are shown in Figures 2-5.  
The results of the GDOP values for each different percentage of star selections were statistically analyzed to obtain Table 2.  
The following conclusions can be drawn from Figures 2-5 and Table 2:  
1. When 90%, 80%, 70%, and 60% of the satellites are screened, the GDOP value increases by 20.75%, 37.87%, 58.17%, and 109.44%, respectively.
2. When screening 70% of the satellites or more, this paper's algorithm maintains its GDOP value well, compared with the optimal GDOP value satellite selection scheme, and it is basically consistent with the satellite selection ratio relationship model obtained from Equation (15).
3. When screening 70% of the satellites or more, the GDOP value results show more sudden jump points on the  
The fast satellite screening algorithm in this paper will be compared with current mainstream satellite selection algorithms. The user is located in Nanjing, and the experiment duration is 6 h with an observation frequency of 0.5 Hz. The comparison algorithms used were the fast genetic star selection algorithm, the traditional GDOP-based selection algorithm, and the position error-assisted selection algorithm. The selection ratio for all satellite selection algorithms was set at 70%. The results of GDOP values are shown in Figures 6-9.  
Figure 4: GDOP value curve at 70% satellite screening.  
Figure 5: GDOP value curve at 60% satellite screening.  
Figure 3: GDOP value curve at 80% satellite screening.  
The results of GDOP values for each satellite selection algorithm were statistically analyzed to produce Table 3.  
The following conclusions can be drawn from Figures 6-9 and Table 3:  
1. When the satellite selection ratio is 70%, the GDOP values of the satellite selection algorithm, position error-aided GDOP algorithm, and fast genetic GDOP algorithm are improved by 8.56%, 5.70%, and 2.89%, respectively, compared with the traditional GDOP satellite selection algorithm. The GDOP increase of this paper's algorithm is slightly higher than the other two methods, but it also stays at a lower value.
2. The standard deviation of the above four algorithms is when the satellite selection ratio is controlled at 70%: 0.1586, 0.2390, 0.3076, and 0.1355. It is shown that the localization stability of the algorithm in this paper is worse than the traditional GDOP  
Figure 8: GDOP of position error-aided GDOP algorithm.  
Figure 6: GDOP of traditional GDOP algorithm.  
Figure 7: GDOP of fast satellite screening algorithm in this paper.  
Figure 9: GDOP of fast genetic star selection algorithm.  
algorithm and fast genetic GDOP algorithm, but better than the position error-aided GDOP algorithm.