esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructure Evolution  
Figure 3a shows the microstructure of the Al-5 Si-1 Cu-0.5 Mg alloy as cast-fabricated while using the electromagnetic stirring method. The base alloy microstructure consists of an \(\upalpha\)-Al (white) matrix surrounded by the eutectic Si (grey) phase of the needle or lamellar shape and the Fe-rich phase (\(\upgamma\) phase) (sepia) of the intensive bone shape. The coarse grains and their inhomogeneous distribution produce stress concentration during loading and deformation, thus reducing the mechanical properties of the cast alloy. Figure 3b shows that the appropriate addition of SiCp (1.5 wt %) can refine the primary \(\upalpha\)-Al and the eutectic Si and improve their distribution. Under the environment of the semi-solid slurry, SiCp inhibits the diffusion of solute elements, such as Si, Fe, and other elements, thereby limiting the nucleation and growth of second phase [18]. On the other hand, SiCp provides a nucleation point for Si eutectic phase. Thus, the nucleation rate increases and the size of the phase reduces. Moreover, the aspect ratio of the eutectic Si phase can also be reduced due to the addition of a proper amount of SiCp [19]. The Fe-rich phase was also changed from an intensive bone shape into a dispersion type. Figure 3c illustrates the microstructure of SiCp/Al-5 Si-1 Cu-0.5 Mg composites with heat-treatment. Spheroidization of the eutectic Si occurred during the solution treatment. After aging, there were some light grey \(\upgamma\) phases precipitated. Table 2 shows the results of the mechanical tests of the samples. It could be seen that the addition of SiCp effectively improved the mechanical  
properties of the cast Al-5 Si-1 Cu-0.5 Mg. Meanwhile, as Table 2 indicating, the mechanical properties of the SiCp/Al-5 Si-1 Cu-0.5 Mg composites treated with T6 were the best of all the tested alloys. After T6 treatment, the hardness, ultimate strength, and elongation of the composite were 101.86 HV, 273.64 MPa, and 6.12%, respectively. When compared to the ones of the samples not treated with T6, these values were increased by 17%, 14.68%, and 17.02%, respectively.  
Figure 4a is the TEM morphology of SiCp/Al-5 Si-1 Cu-0.5 Mg composite after aging treatment. There are some needle-like precipitate phases (as the arrows shown in Figure 4a) with length of about 100 nm around the dislocation. Furthermore, EDS analysis (as shown in Figure 4b) of the precipitate phase revealed that aluminum and copper were the main compositions of the phase. According to [PERSON], [PERSON] et al. [20], the precipitation phase could be Al\({}_{2}\)Cu. The Al\({}_{2}\)Cu phase contribute to improve the hardness of the alloy. Moreover, SiCp pinning on the boundaries that could stabilize the sub-microstructure and speed up the aging reaction and increase the rate of work hardening [21].
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies
### Reputation and Trust-Based Systems  
Reputation-based IFA defenses assign trust scores or reputations to consumers (or prefixes) and use these scores to throttle or filter traffic. The intuition is that legitimate users will mostly fetch existing content (earning a good reputation), while attackers who constantly request bogus names will accumulate a bad reputation [44]. One representative example is the combination of the Interest Satisfaction Ratio with a user reputation score: if a user's interests result in very few data returns, their reputation decreases, causing the network to limit the subsequent request rate of that user.  
The Poseidon framework combines ISR with per interface state to isolate and penalize malicious interfaces in the network. The router dynamically updates a \"goodness score\" for each interface based on ISR and PIT occupancy to throttle or block misbehaving traffic sources [28]. In addition, a more intelligent approach using a hybrid radial basis function neural network optimized by particle swarm optimization (RBF-PSO) was introduced.  
Their model evaluates the trustworthiness of traffic sources based on several features, including ISR, name entropy, and request timing. This scoring informs a real-time mitigation strategy that outperformed traditional rule-based systems in simulation [34].  
A credit-based accounting model was proposed that assigns virtual \"tokens\" to each consumer. Consumers gain or refund tokens when interests are satisfied, and they lose tokens when interests time out. Once a consumer exhausts its token balance, its interests are deprioritized or blocked. This approach mimics economic deterrents and has shown success in preserving PIT space under attack [40].  
In distributed scenarios, trust scores can be shared among routers or managed by a central authority, forming a collaborative trust system. The paper further extended this by incorporating Cumulative Sum (CUSUM)-based anomaly detection into trust scoring, demonstrating resilience against traditional and collusive IFAs in IoT environments [9], while some proposals employed fuzzy logic or Bayesian inference to smooth reputation changes over time and reduce false positives due to transient network issues or bursty traffic.
## 5 Conclusions  
This study focuses on classifying Kirmizi and Siirt pistachios using deep learning-based approaches, with k-fold cross-validation employed to enhance model generalization and prevent overfitting. For classification, seven convolutional neural network models trained with transfer learning, along with the proposed MSU-CNN model, were used. The dataset consists of 2148 images, with 1232 from Kirmizi and 916 from Siirt. The models, including The AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50, achieved average classification accuracies of 94.88%, 96.79%, 96.79%, 97.90%, 98.88%, 99.02%, 99.21%, and 99.63%, respectively, for 5-fold cross-validation. The highest accuracy was attained by ResNet-50 for 5-fold cross-validation. The proposed MSU-CNN model demonstrates significant competitiveness compared to the pre-trained models and exhibits remarkable performance in accurately classifying pistachio species. Since 5-fold cross-validation splits the dataset into 80% training and 20% testing, the highest classification accuracies obtained from each fold for the models were as follows: AlexNet, GoogLeNet, proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 achieved average classification accuracies of 98.84%, 98.37%, 97.21%, 99.07%, 99.53%, 99.53%, 99.77%, and 100%, respectively. The highest accuracy was attained by ResNet-50 for the 80% training-20% testing split. Performance evaluation was carried out using sensitivity, specificity, precision, F1-score, ROC curves, and AUC values. These findings demonstrate that the proposed models can classify pistachio species most effectively.  
The future work could involve applying different artificial intelligence methods and models based on the total number of images in the dataset to achieve higher classification accuracies. The proposed model can be further adapted with more complex layer configurations, making it suitable for both pistachio classification and other classification tasks. A broader classification study could be carried out by gathering images of different pistachio varieties. Additionally, the implementation may be developed into a mobile platform for pistachio species identification in agricultural fields.  
This research received no external funding.
## 3 Analysis of Simulation Data Based on D-S Evidence Theory
### D-S Evidence Matrix  
The basic principle of the D-S evidence matrix is as follows: based on multiple sources of heterogeneous data, multiple mechanical parameter sensitivity discriminators are identified independently, and the basic probability assignment function is determined by the probability assignment of each mechanical parameter sensitivity discriminator. The final discriminatory results are obtained by fusion using D-S synthesis rules [32].  
Assume that \(\Omega\) is the discriminative framework for the sensitivity of the mechanical parameters of the prestressed cable members, which is expressed by Equation (10), and the specific expression is:  
\[\Omega=(e_{1},e_{2},\cdots,e_{n}) \tag{10}\]  
In Equation (10), \(e_{i}(i=1,2,...,n)\) expresses the change in the mechanical properties of the \(i\)th node of the prestressed cable.  
All possible working conditions of the prestressed cable element are the power set \"\(2^{\Omega\)-\", which is expressed as Equation (11):  
\[2^{\Omega}=\left(\varnothing,e_{1},e_{2},\cdots,e_{n},e_{1}\cup e_{2},e_{i} \cup e_{j}\cup\cdots\cup e_{k}\cdots\right) \tag{11}\]  
In Equation (11), \(\varnothing\) indicates no change in the mechanical properties of the prestressed cable, and \(e_{i}\) indicates a change in the mechanical properties of a single node of the prestressed cable. \(e_{i}\cup e_{j}\cup\cdots\cup e_{k}\) indicates the change of mechanical properties of multiple nodes of the prestressed cable.  
The basic probability distribution function (mass function) can be expressed as m: \(2^{\Omega}\)\(\rightarrow\) [0,1], satisfying Equation (12):  
\[\left\{\begin{array}{c}m(\varnothing)=0\\ \sum_{A\subset\Omega}m(A)=1\end{array}\right. \tag{12}\]  
In Equation (12), \(A\) is a certain working condition of the prestressed cable, and \(m(A)\) is the basic probability distribution function of \(A\).  
Assume that \(m_{j}(A_{i})\) is the basic probability distribution function for the \(j\)th mechanical parameter sensitivity indicator. To determine the change in the mechanical properties of the \(i\)th node, the synthesis rule of the D-S evidence matrix is Equation (13):  
\[DF(A)=m_{1}\oplus m_{2}\oplus\cdots\oplus m_{n}(A)\left\{\begin{array}{c} \frac{\sum_{A_{i}=A}\prod_{j\leq n}m_{j}(A_{i})}{K}(A\
eq\varnothing)\\ 0\;(A=\varnothing)\end{array}\right. \tag{13}\]  
In Equation (13), \(K=\sum_{A_{1}\cap A_{2}\cap\cdots\cap A_{n}\
eq\varnothing}m_{1}(A)_{1}m_{2}( A)_{2}\cdots m_{n}(A)_{n}\)\(=1-\sum_{A_{1}\cap A_{2}\cap\cdots\cap A_{n}=\varnothing}m_{1}(A_{1})m_{2}(A_{2}) \cdots m_{n}(A_{n})\).
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
###### Abstract
Similar to SPH, the FPM is a grid-free approach that utilizes a collection of Lagrangian particles to represent the fluid state. FPM employs a smoothing function and its derivatives to approximate function values, making it an enhanced version of SPH. However, conventional SPH suffers from lower order accuracy in boundary regions due to its lack of first-order accuracy. Additionally, SPH is computationally expensive and is not suitable, particularly for large-scale simulations. The movement of fuel in a fuel tank under excitation is a complex and highly nonlinear free surface phenomenon with randomness. [PERSON] et al. [17] simulated the interaction between fuel and the fuel tank during unmanned aerial vehicle launch using the Arbitrary Lagrange Euler (ALE) method. [PERSON] et al. [18] used the finite element software ABAQUS 2018 and a traditional Volume of Fluid (VOF) method to obtain the shape changes of fuel during a semi-roll process. [PERSON] [19] simulated the fuel sloshing of a wing fuel tank under steady axis rotation using the ALE method, while [PERSON] et al. [20] used the Smoothed Particle Hydrodynamic (SPH) method to calculate the fuel movement characteristics of an auxiliary fuel tank during pitch motion. The above-mentioned studies explored the free surface problem of fuel under simple working conditions using methods such as ALE, SPH, and VOF, but did not consider the movement of fuel under complex overloads. In this study, the FPM is employed to simulate the fuel movement characteristics of an aircraft under short-term composite acceleration excitation. The fuel tank model used in this research closely approximates the actual tank shape and takes into full consideration the influence of the internal tank structure on fuel movement. The investigation focuses on obtaining the free surface characteristics of fuel in such scenarios, with the acquired data providing support for aircraft development. For fuel tanks with a high-liquid filling ratio, such as spacecraft propellant tanks, aircraft fuel tanks, and tanker trucks, the substantial liquid movement within a short period during motion can lead to changes in the system's center of gravity. Consequently, this may have adverse effects on the motion trajectory or attitude of liquid-carrying systems such as spacecraft, aircraft, and tanker trucks. The research findings presented in this paper offer valuable guidance and serve as a reference for the structural design of aircraft fuel tanks, overall aircraft design, optimization of spacecraft propellant tanks, and the design of liquid-carrying ships and other related liquid-carrying systems.
## 3 Results
### The Results of Joint Angle Prediction
#### 3.3.4. Prediction Results of Different Loads  
Table 5 delineates that as the weight of the load on the subject's hand increased, the accuracy of joint angle prediction improved to a certain extent. This was because the load increased the degree of muscle contraction, thereby increasing the amplitude of the MMG signal, which ultimately led to an improvement in the accuracy of joint angle prediction.  
Experiments on real-time prediction of joint angles were also conducted. The results illustrate that when the hand load increases within a certain range, the prediction accuracy of the joint angle increases. However, if the load level increases further, it may cause rapid muscle fatigue, resulting in a decrease in prediction accuracy. During arm side raise, the RMSE of the shoulder joint angle prediction result was 0.0418, 0.0394, and 0.0368, respectively, when the subject's hand load was 0, 0.5 and 1 KG; during arm front raises, the RMSE of the shoulder joint angle prediction result was 0.0441, 0.0409, and 0.0376, respectively, when the subject's hand load was 0, 0.5, and 1 KG; during elbow curls, the RMSE of the elbow joint angle prediction result was 0.0485, 0.0443, and 0.0408, respectively, when the subject's hand load was 0, 0.5, and 1 KG.
# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
## 2 Methods
### Design of RLS-PSO Parameter Identification Algorithm
#### 2.2.3 Flowchart of RLS-PSO  
Based on the established RLS and PSO, RLS-PSO was designed. The hip and knee joints of APAL swing according to the hip and knee trajectories \(q_{1}\) and \(q_{2}\), respectively. The sensors measure hip and knee torques represented by \(T_{1\_m}\) and \(T_{2\_m}\), respectively. Hip and knee angles respectively represented by \(q_{1\_m}\) and \(q_{2\_m}\) are also measured by sensors. The hip and knee joints angular velocities respectively represented by \(\hat{q}_{1\_m}\) and \(\hat{q}_{2\_m}\) are calculated by forward difference. The hip and knee joints angular accelerations respectively represented by \(\ddot{q}_{1\_m}\) and \(\ddot{q}_{2\_m}\) are calculated by quadratic forward difference. Figure 4 is the flowchart of RLS-PSO.  
The steps of RLS-PSO are as follows:  
1. RLS identifies the fluctuation range of each parameter in \(X\) by Equation (6), thereby defining the search space of PSO;
2. Within the search space defined by the RLS, the PSO optimizes \(X\) by Equations (7) and (8). The estimated values of the hip and knee torques respectively represented by \(T_{1\_est}\) and \(T_{2\_est}\) are calculated by substituting \(X\) identified in each iteration into Equation (4). \(T_{1\_est}\) and \(T_{2\_est}\) are subtracted from \(T_{1\_m}\) and \(T_{2\_m}\), respectively. The absolute values of the differences are \(\Delta T_{1}\) and \(\Delta T_{2}\), respectively. In each iteration, the optimization goal of the PSO is: \(\Delta T_{1}+\Delta T_{2}<\varepsilon,\ \varepsilon\to 0+\) ;
3. When the iteration reaches \(G\) times or \(\Delta T_{1}+\Delta T_{2}<\varepsilon\ (\varepsilon=10^{-2}\), \(G=10000)\), the PSO stops searching and the global minima \(X_{Gbest}\) is the identified parameter vector.
# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 3 Damage Diagnosis Model and Process
### Model Parameters  
The parameters of the TICNN model were modified in the proposed new model. Firstly, the size of the input data was changed to 1024. Secondly, under the same experimental conditions, the influence of different step size of the first convolutional layer on the model's accuracy was studied, and the appropriate step size was selected. The influence of the step size of the first convolutional layer on the model's accuracy and loss functions are shown in Table 1. It shows that when the step size of the first convolutional layer is eight, the accuracy of the proposed new model is the highest, so the step size of the first convolutional layer was reduced from sixteen to eight. Thirdly, the convolution kernel size and step size of the second convolutional layer of the TICNN model were also adjusted. Then, a three-layer small convolution kernel structure and a five-layer SE-Res2 Net module were added into the new model. Finally, an alternating distribution structure of three fully-connected layers and a Dropout layer were added after the SE-Res2 Net modules to increase the classification accuracy of the proposed model. The convolution kernel size, step size, channels, output size and other parameters of the new model are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Step Size** & **4** & **8** & **12** & **16** & **20** & **24** & **32** \\ \hline Accuracy & 0.9823 & 0.9976 & 0.9914 & 0.9862 & 0.9907 & 0.9842 & 0.9855 \\ Loss & 0.0082 & 0.0034 & 0.0110 & 0.0093 & 0.0106 & 0.0163 & 0.0156 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection basis of the step size of the first convolutional layer.  
Figure 5: Structure of the proposed new model (The colors represent different feature values).
## 2 Results and Discussion
### AMP Type
#### 2.5.3 AMP Models  
Several AMP publications were associated with interesting in vitro or in vivo models. Chitosan-derived gels loaded with the novel AMPs ASP-1 and ASP-2 were found to be effective on monospecies biofilms grown for three days on ex vivo porcine skin and in vitro (polymer mesh) models. Furthermore, over seven days, 70 to 80% of AMPs were released from the chitosan [133]. Synergistic interactions between species (e.g., _C. albicans_ and _S. aureus_) can increase biofilm formation and AMR. Although AMP L18R was able to reduce the biomass of monicrobial _C. albicans_ and _S. aureus_ early-stage and mature biofilms, no reduction in biomass was observed against a polymicrobial biofilm in the Lubbock chronic wound biofilm model. However, L18R caused a moderate reduction in total CFU number and decreased the number of _C. albicans_ in the same model [134]. A novel murine osteomyelitis model using the infected femoral bone canals of laboratory rats was used by [PERSON] et al. to study bone implant-related infections [135]. A 12 amino acid synthetic analogue of the AMP halcitine-2 was mixed into polymethylmethacrylate bone cement, preventing the subsequent formation of MRSA biofilm on the surfaces of more than 80% of these implants. In another interesting murine model, [PERSON] et al. studied two synthetic AMPs (P5 and P6.2) that had been earlier designed via combined computer-assisted and rational approaches [136]. Both peptides were investigated using a _P. aeruginosa_ lung infection model of neutropenic mice, with the lungs being extracted to assess both bacterial load and pro-inflammatory cytokine activity. A significant reduction in bacterial load and a concomitant decrease in pro-inflammatory cytokines IL-1\(\beta\), IL-6, and TNF-\(\alpha\) was observed. The bacterium _Desulfoibrio vulgaris_ and a corrosion biofilm model (using steel rives as a surface for bacterial growth in a 96-well format) was developed by [PERSON] et al., who showed that AMPs S6L3-33 (bacterial reduction), bacteremia (total biomass reduction), and DASamP1 (biofilm inhibition up to 21 days) were the most useful anti-biocorrosive AMPs of those tested [137]. Finally, AMPs may be important in protecting foodstuffs from spoilage bacteria. In this respect, [PERSON] et al. utilized three food matrices (cooked meat sauce, raw fish, and fruit juice) to investigate the AMPs As-CATH4, and As-CATH5 and Hc-CATH (from the sea snake _Hydrophis cyanocinctus_ and Chinese alligator _Alligator sinensis_, respectively) that had previously been identified to exhibit broad-spectrum antimicrobial activities. Sodium benzoate and potassium sorbate were used as positive controls. Synergistic interactions were observed in combination with butyl paraben in fruit juice [138].  
Published research into novel AMPs and their variants included AMPs obtained from a wide variety of (micro)organisms, including humans, mice, aquatic life, insects, plants, and even bacteria. Several interesting models for evaluating AMP efficacy were also published including mesh, bacterial/fungal synergistic, murine, and food preserving models.
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 1 Introduction
The original YOLO algorithm proposed by [PERSON] et al. [26] demonstrated impressive speed in object detection; however, its bounding box regression accuracy proved inadequate when detecting PCB defects in complex backgrounds. [PERSON] et al. [27] focused on the challenge of small object detection within PCB inspection, yet the proposed model suffered from limited feature extraction capacity and low classification efficiency, rendering it unsuitable for intricate industrial classification tasks. [PERSON] et al. [28] enhanced the YOLOv5s algorithm to improve PCB defect classification, but limitations in feature extraction and multi-scale fusion reduced its effectiveness in handling defects across scales. [PERSON] et al. [29] introduced an improved YOLOv7-tiny model to enhance small object detection, achieving performance gains; however, further reduction in model size and computational complexity remains necessary. [PERSON] et al. [30] proposed modifications to the YOLOv8 architecture by introducing a more effective feature fusion mechanism, improving detection accuracy for surface-level PCB defects, but challenges persist regarding computational complexity and frame rate optimization. To address the aforementioned limitations, this paper proposes a PCB defect detection model based on an enhanced YOLOv12 framework. Specifically, a Median-enhanced Channel and Spatial Attention Block (MECS) is integrated into the Backbone to enhance feature extraction performance and robustness through an improved attention mechanism that expands the receptive field using median enhancement and depthwise convolution. In the Neck, an Adaptive Hierarchical Feature Integration Network (AHFIN) is introduced, which employs adaptive Deformable Convolution and gradient compression to fuse multi-scale features, thereby strengthening the model's focus on critical defect regions. In addition, the original YOLOv12 loss function is replaced with the Slide Alignment Loss (SAL) to optimize bounding box regression and address sample imbalance. Experimental results confirm that the proposed MAS-YOLO model consistently delivers superior performance across multiple PCB defect datasets, effectively meeting the requirements for real-time industrial detection.