esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 6 Sensitivity Analysis on Parameters
### Influence of the Stress Range  
The stress range varied from 120 MPa to 200 MPa in the analysis. The crack depth was plotted against time, as shown in Figure 10. According to the result, when the stress range increased from 120 MPa to 200 MPa, the whole C-F life in the two types of steel was reduced by similar rates, i.e., 92.4% in the HPS 70W and 88.7% in the 14 MnNbq. However, it should be noted that the C-F life of HPS 70W was far longer than that of 14 MnNbq steel due to postponing in the crack initiation.  
Figure 10: Crack development under various stress ranges: (**a**) HPS 70W and (**b**) 14 MnNbq.  
Figure 9: Crack development in the C-F process: (**a**) 20 mm and (**b**) 50 mm.  
For further comparison, the influence of the stress range on the initiation life and the propagation life are shown in Figure 11a,b, respectively. It can be found that, due to the increase in the stress range, the propagation life was reduced by 83.9% in the HPS 70W and 81.2% in the 14 MnNbq. Meanwhile, the initiation life in the HPS 70W steel was reduced by 94.0% while that in the 14 MnNbq steel was decreased by 92.2%. The result suggests that the stress range had a great effect on the C-F life of both types of steel at both the two stages. Meanwhile, it also could be found that the effect of the stress range on the C-F life of both two types of steel was highly similar.
# Opportunities for Early Detection and Prediction of
## 3 The Data Used for Ransomware Behavioral Analysis
The behavioral data collected during ransomware analysis can vary depending on the specific techniques and tools used, but some common types of behavioral data include:* File system activity: information on which files and directories the ransomware accesses, modifies, or encrypts during an attack;
* Network activity: Data on the servers with which the ransomware communicates, the ports it uses, and the protocols it employs;
* Registry modifications: changes made to the Windows registry by the ransomware, such as the creation or modification of registry keys;
* Process activity: information on the processes created or modified by the ransomware during an attack, as well as any child processes spawned by the malware;
* System configuration changes: changes made by the ransomware to system settings or configurations, such as changes to firewall rules or user account permissions;
* Memory analysis: the analysis of the ransomware's behavior in memory, such as code injection, process hollowing, or other memory-based attacks;
* Behavioral indicators: specific patterns or behaviors associated with ransomware attacks, such as attempts to disable security software, or the presence of specific file types or extensions commonly targeted by ransomware. By analyzing these behavioral data, security researchers can gain insight into the techniques and tactics used by ransomware attackers, as well as identify potential vulnerabilities and mitigation strategies.
## 4 Conclusions  
PLGA-PEG-FA copolymer was successfully constructed by two conjugation steps and was characterized by FTIR. Plasmid pVAX1-NH36 was propagated and efficiently encapsulated into modified polymer PLGA-PEG-FA nanoparticles. Size, polydispersity index, zeta potential, encapsulation efficiency and plasmid load of nanoparticles were determined. These results suggest that with an increase in TDL, diameter of pDNA-NPs, encapsulation efficiency and drug loading increase. However, zeta potential and polydispersity index did not present a significant statistical difference, suggesting stability of the particles. Experimental pVAX1-NH36 release profile shows a multiphase behavior for all three TDL investigated. A mathematical analysis was introduced to describe experimental release profile by considering simultaneous contributions of initial burst and degradation-relaxation of NPs. Fitting of the release model against experimental data presents an excellent correlation. This mathematical analysis could be used to describe and predict the release of plasmid DNA from biodegradable NPs. In addition, pVAX1-NH36 quality over encapsulation experiments was constant without any noticeable plasmid damage.  
Financial assistance from the National Council of Science and Technology of Mexico (CONACYT) is genuinely acknowledged with projects 10007-2013-02 No. 206489 and INFR-2014-01 No. 226208. [PERSON] thanks CONACYT for projects PDCPN201401-247326. We thank [PERSON] group for their assistance in the lyophilization of NP, [PERSON] for his support with the AFM micrographs, and also [PERSON] for his help with the SEM acquisition. [PERSON] thanks CONACYT for the Ph.D. scholarship.  
[PERSON] performed all experiments and contributed in the manuscript writing. [PERSON], [PERSON], and [PERSON] contributed with reagents/materials/analysis tools and discussion for all experiments. [PERSON] contributed in the manuscript discussion and writing. [PERSON] conceived and designed the experiments and contributed in the manuscript writing.  
The authors declare no conflict of interest.  
Figure 6: Agarose gel electrophoresis for pVAX1-NH36 before and after nanoparticle preparation. (1) Size marker for supercoiled plasmid form over the range 2972–12,138 bp; (2) Initial pVAX1-NH36 solution used to formulate the pDNA-NP; (3) Sample acquired from 0.5% theoretical pVAX1-NH36 loadings (TDL) solution after pDNA-NP purification; (4) Sample obtained from 0.8% TDL solution after pDNA-NP purification.
## 2 Subject, Device, Data Collection and Experiments  
As mentioned earlier, this study hopes to detect the plantar pressure of overweight patients with lower extremity edema through a set of self-made pressure sensor insoles. This set of self-made pressure sensor insoles is divided into left and right feet, and each inside is equipped with six pressure resistance sensors. In this study, a patient with severe edema of the lower extremities was invited to perform plantar pressure tests before and after surgery for the edema of the lower extremities under the guidance of the attending physician. This part was mainly to understand the subtle changes in the plantar pressure of the patient before and after the operation. In order to understand the foot pressure of the patient in different daily activities, this study further invited the subject to stand, walk and ride a bicycle. Finally, in the hope of improving the patient's foot pressure, this study tried to use insoles of different heights and arch insoles to explore their impact on the patient's foot pressure.  
In the following, we first introduce the characteristics of the invited participant. Then, we explain the induction insoles, insoles of different heights and arch shoes specially made for this patient. After that, we explain the procedures for data collection. Finally, we briefly discuss the experimental methods.
## 4 Evaluation
### Results and Discussion  
This section presents and discusses the simulation results of LAMeD, LARep and the replication techniques selected for comparison in two parts that are detailed in Sections 4.2.1 and 4.2.2. The first subsection evaluates the proposals under high degree of spatial locality in the Skudai scenario described in Section 4.1.1, while the second subsection evaluates them under increasing data traffic in the Helsinki simulation area (i.e., the medium scenario) described in Section 2.4.1. Throughput, average delivery delay and message transmission overhead are recorded in the presence of external messages that are generated according to the parameters given in Section 2.4.2. For recording energy consumption, the simulations are repeated without external messages.
## 2 Materials and Methods
### Cell Viability Assay  
The cytotoxicity of kaempfer on HeLa (Cervical cancer cell) and AC 16 (normal cell, Human cardiomyocyte) cells was assessed by MTT (thiazolyl blue tetrazolium bromide) assay. Both cell types were plated with \(\sim\)1 \(\times\) 10\({}^{4}\) cells/well in 96 well plates and kept for attachment at 37 \({}^{\circ}\)C. The following day, the treatment started for 24 and 48 h with various concentrations of kaempfer from 1-100 \(\upmu\)M in complete media; whereas AC-16 cells were treated using different concentrations of kaempfer between 1-200 \(\upmu\)M over 24 h. Treatments for each concentration were done in triplicates. After adding MTT solution (5 mg/mL, Sigma) in each well, the plate was kept in dark for 3 h, consequently dissolving the formazan in DMSO. After 30 min, the O.D. value was measured at 570 nm by a microplate reader (BioTek, Winooski, VT, USA). Images were captured for both DMSO control and treated HeLa cells by an inverted microscope (Labomed, Los Angeles, CA, USA).
## 2 Materials and Methods
### Data and Data Processing  
#### 2.2.1 Data Collection and Processing  
1. Data Acquisition: The model test data for this study consisted of hourly measurements of water quality indicators in the Huangyang Reservoir, Wuwei City, from 1 January 2017, to 1 October 2023. These indicators include pH value, Turbidity, and DO in the water body, among others.
2. Selection of Research Subjects: Upon analyzing the water quality data and comparing the data with the \"Environmental Quality Standards for Surface Water\", it was observed that the DO, Turbidity, and pH content in the Huangyang Reservoir, located in Wuwei City, had the most significant influence on its water quality classification. Therefore, this study focused on evaluating the water quality of the Huangyang Reservoir in Wuwei City by examining the changes in its DO, Turbidity, and pH levels during the study period.
3. Standardization: Different evaluation indicators often have different quantitative scales and units, which can impact the results of data analysis. To mitigate the influence of these differences in quantitative scales, data standardization is necessary to ensure the comparability of data indicators. By standardizing the raw data, the indicators are brought to the same scale, facilitating comprehensive comparative evaluation. Therefore, the DO, Turbidity, and pH values are standardized. This process involves dividing the raw data by the standard deviation after subtracting the mean value, resulting in data that are transformed into a distribution with a mean of 0 and a standard deviation of 1. The standardized processing formula is as follows:  
\[X_{\text{norm}}=\frac{X-\mu}{\delta} \tag{1}\]  
where \(X\) represents the original data, \(\mu\) represents the mean, \(\delta\) represents the standard deviation, and \(X_{norm}\) represents the normalized value.  
* Sample data should be divided into a training set, testing set, and validation set. The division of the training, testing, and validation sets follows a ratio of 7:2:1. Specifically, the initial 70% of the sample data are allocated for model training, the subsequent 20% are designated for testing the model, and the remaining 10% are utilized for validating the model's performance.
* Data reduction is a crucial step in the research process. During the evaluation of the model at the conclusion of the training process, the normalized data are subjected to reduction (inverse normalization) using an equation to assess the error in the predicted values of the model. The inverse normalization formula is as follows:  
Figure 1: System framework.  
\[X=X_{norm}\cdot\delta+\mu \tag{2}\]
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Communication System  
The communication system within the AIRDROP platform encompasses various critical aspects, including robust security measures, communication links, communication relaying, crowd counting, bandwidth management, network connectivity, and position accuracy. It employs robust security measures to safeguard sensitive data and establish secure channels between UAVs, UGVs, USVs, and the GCS to prevent unauthorized access and data breaches. It prioritizes command and control (C2) messages over telemetry messages while maintaining low latency between vehicles and the GCS. The system includes communication relaying capabilities, both tethered and non-tethered, to extend communication range and coverage when direct links are not feasible. Additionally, it enables crowd counting services through RSSI (received signal strength indicator)-based techniques and Wi-Fi connectivity, allowing for the estimation of crowd sizes and densities in affected areas, with a view to providing insights for resource allocation and emergency planning. With redundancy in data storage and bandwidth management strategies, such as data compression, the communication system ensures reliable and optimized data transfer. Integration of Wi-Fi access points, connectivity interfaces, and cloud server support fosters seamless information sharing and real-time data exchange, enhancing situational awareness and collaborative decision-making. Leveraging multiple GNSS (global navigation satellite system) receivers, RTK (real-time kinematic) GPS technology, and multi-frequency receivers, the system ensures high position accuracy for vehicles and first responders, thereby facilitating precise localization and mapping of affected areas for effective response coordination and mission planning.
## 4 Analysis and Discussion  
### Training Phase  
Figures 6 and 7 show that all models have smooth training curves during the training process, while the validation curves exhibit fluctuations. In particular, the models with larger parameters, such as AlexNet and the VGG architecture, show a trend of decreasing and then increasing validation loss curves, which is a typical overfitting phenomenon. Decreasing the batch size exacerbates the overfitting phenomenon. The InceptionV3 and DenseNet architectures exhibit slight overfitting. It is worth noting that residual networks and the three lightweight models (MobileNet, EfficientNet, and SqueezeNet) show minimal overfitting, which is almost negligible, and that batch size has little effect on these models.  
Several factors can lead to overfitting. Firstly, increasing the model complexity generally enhances its representational ability, but overfitting occurs when the dataset is small. Although the amount of data collected in this study is substantial in highway distress recognition, it remains small compared to several public natural image datasets. Therefore, the lack of training data is one of the reasons for the overfitting of the models. Secondly, the dataset used in this study has relatively simple features, so it may result in overfitting because an excessively complicated model may fully describe minor variations of data. ResNet and the three lightweight networks (MobileNet, EfficientNet, and SqueezeNet) exhibit minimal overfitting due to their specific network structures and relatively small models. Firstly, the residual connectivity mechanism of the ResNet allows for better information propagation and improved model performance and generalization by effectively addressing issues such as gradient vanishing and representation bottlenecks. Secondly, the lightweight network employs various techniques to decrease model complexity, including deep separable convolution, channel attention mechanism, and network pruning. These strategies can effectively reduce the number of parameters and the complexity of the model to mitigate the risk of overfitting.  
Meanwhile, batch size is also a factor affecting overfitting. A smaller batch size can increase the variance of the gradient estimate and make model training unstable due to the introduction of more randomness. Conversely, larger batches can speed up training and provide more accurate gradient estimates but require more memory. Choosing a larger batch size can help to reduce overfitting, but limited hardware capabilities prevent the large batch size. Data augmentation and optimization algorithms may solve the overfitting problem in the next study.
## 3 Results
### Graphene Fraction Dependence of Plume Characteristics  
Compared to the composite propellant sample doped with nano-carbon and graphite, the graphene-doped composite propellant sample exhibited a higher plume expansion  
Figure 8: Normalized relative luminosity of the axis distribution.  
rate and a smaller plume divergence angle. Therefore, the graphene-doped samples were chosen for further investigation into the impact of the doping ratio on plume characteristics.  
Figure 9 shows the dependence of plume length on the graphene doping ratios. It can be seen from the figure that the plume length of all composite propellant samples followed similar trends. Initially, it increased with the ablation time and then reached a maximum in an approximately linear manner. The composite propellant sample with higher doping ratios exhibited a greater plume length and a higher expansion rate. However, once the doping ratio exceeded 3%, the increment resulting from increasing the doping ratio became relatively small. Based on the order of doping ratios (from low to high), the maximum plume lengths of the graphene-doped composite propellant samples were 8, 11, 12, and 12.5 cm, respectively. The average plume expansion rates were 16, 22, 24, and 25 m/s, respectively. At the end of a laser pulse, the plume length gradually decreased. Nevertheless, the composite propellant samples with higher doping ratios decreased to 0 faster than those with lower doping ratios.  
Figure 10 shows the variations in the plume area of graphene-doped composite propellant samples with different doping ratios. The plume area of the composite propellant sample with a 1% doping ratio continuously increased to 4 cm\({}^{2}\) before 1 ms and then remained stable until the end of laser ablation, whereas the plume area of the composite propellant sample with a 7% doping ratio kept increasing until it reached a maximum of 16 cm\({}^{2}\) with slight fluctuations observed at 2-5 ms. As for the composite propellant samples with doping ratios of 3 and 5%, they displayed nearly identical changes in the plume area, but the area of the former consistently remained the larger.  
Figure 10: Plume areas of composite propellants with different graphene doping ratios.  
Figure 9: Plume lengths of composite propellants with different graphene doping ratios.  
As mentioned above, increasing the doping ratio of graphene promoted an increase in the plume length, expansion rate, and area of propellant samples due to the promising effect of graphene in promoting light absorption of PTFE. However, once the doping ratio surpassed 3%, improvement brought by the increase was insignificant. Considering the plume divergence angle and expansion rate, 5% remained the optimal doping ratio for the graphene-doped composite propellant sample.
# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 5 Discussion
### Effect of DropParts  
We analyzed the role of DropParts by comparing ALIGN with the ALIGN +DROP network. In Figure 6a, the loss of the ALIGN network can be very low, but the loss of ALIGN +DROP network is even lower, i.e., below 0.01. Another point is that the losses of ALIGN +DROP network during training are more stable than ones of ALIGN network which oscillate even at the end of training. These two points signify well-fitting and easy training of ALIGN +DROP network. This validates the first function of the DropParts method that it makes the four-branch convolutional neural network work properly when part missing occurs.  
In Figure 6b, CMC curve of ALIGN +DROP network is always above the CMC curve of BASE network. The addition of DropParts results in improvement in the training and recognition performance of person re-identification, by 1.37% at rank-1.  
In order to further analyze the role of DropParts, we investigate the performances on samples with missing part. Table 6 demonstrates statistics of part missing on Market-1501 [8], CUHK03 [17] and DukeMTMC-reID [9] datasets. As seen from Table 6, part missing does not always occur.  
We evaluate the performance of proposed method with DropParts and without DropParts on samples with missing parts. The results are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Head Missing** & **Torso Missing** & **Lower-Body Missing** \\ \hline Market-1501 & 12.78\% & 0.50\% & 0.65\% \\ CUHK03 & 0.77\% & 0.01\% & 0.59\% \\ DukeMTMC-reID & 2.59\% & 0.03\% & 0.53\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of part missing on Market-1501, CUHK03 and DukeMTMC-reID datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Market-1501**} & \multicolumn{3}{c}{**CUHK03**} & \multicolumn{3}{c}{**DukeMTMC-reID**} \\ \hline
**Methods** & **Rank-1** & **Rank-5** & **mAP** & **Rank-1** & **Rank-5** & **mAP** & **Rank-1** & **Rank-5** & **mAP** \\ \hline ALIGN & 0.7334 & 0.8599 & 0.6087 & 0.8589 & 0.9752 & 0.8356 & 0.7379 & 0.8564 & 0.5670 \\ ALIGN +DROP & 0.8510 & 0.9286 & 0.6603 & 0.8998 & 0.9803 & 0.8777 & 0.7991 & 0.8962 & 0.6212 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of ALIGN and ALIGN +DROP methods on part missing samples.  
Figure 6: Loss curves and CMC curves. (**a**) Loss curves of BASE, ALIGN and ALIGN +DROP network during training; (**b**) CMC curves of BASE, ALIGN and ALIGN +DROP network during testing.  
From Table 7, it can be seen that the proposed algorithm outperforms the method without DropParts by 7.32% at rank-1, 3.79% at rank-5 and 4.93% at mAP on average. On the Market-1501 dataset, our method outperforms our method without DropParts by 11.86% at rank-1. It validates that the DropParts method can improve the performance of person re-identification.