esa-sceva/satcom-chunk-collection huggingface dataset:
# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
## 3 Results
### Results Analysis  
We presented the results of the proposed method in Table 1 on both databases, and the steps of our method to detect OD are shown in Figure 2. Some detection results are shown in Figures 3 and 4.  
Specifically, the success rate in Table 1 was 100% and 97.7% for both databases when we selected 4 images from DRIVE and 8 images from DIARETDB1 as templates, respectively. The success rate is the percentage of correct detection among all attempts. The error distance is the distance between the estimated OD center and the manually identified OD center. The average error distances are 10.4 and 12.9 pixel for the DRIVE and DIARETDB1 databases, respectively, and the value of each image is shown in Figure 5.  
The error distances and success rates of the different databases computed using different numbers of template images are shown in Figure 6. The results of our method show success rates/error distances of 87.5%/20 pixel, 100%/17.37 pixel, 100%/16.54 pixel, and 100%/10.4 pixel when we construct \(1,2,3,4\) template images from the DRIVE database, respectively. The success rates/error distances are 63%/20.58 pixel, 80.9%/13.1 pixel, 91%/12.9 pixel, and 97.7%/12.7 pixel when we construct \(2,4,6,8\) template images from the DIARETDB1 database, respectively. Therefore, as templates increase, the detection success rate of the proposed method increases, and the error distances drop. The results show that the proposed method is validated, has better robustness, and can obtain very precise detection results by increasing the number of templates. We can determine the number of templates according to the different requirements of the actual application.  
Table 2 shows the comparison of the success rate with different methods. From these data, we can easily conclude that the proposed method is a robust method and performs well on various public databases.  
Figure 3: Results of the proposed method (the red cross represents the estimated OD center). (**a,c,e,g,i,k**) The OD center annotation ground truth. (**b,d,f,h,j,l**) Results of the proposed method on the DRIVE database.  
Figure 4: Results of the proposed method (the red cross represents the estimated OD center). (a,c,e,g,i,k,m,o,q,s) The OD center annotation ground truth. (b,d,f,h,j,l,n,p,r,t) Results of the proposed method on the DIARETDB1 database.  
Figure 5: Error distances of OD detection for all databases. The numbers circled in red indicate images in (**b**) that failed to detect ODs.  
Figure 6: Error distances and success rates of OD detection using different numbers of templates.
###### Abstract
that minimize degradation of accuracy are being actively pursued as a major research topic. Fortunately, deep learning-based FER has progressed to the extent of being lightweight and producing fast inference on edge device based on hardware optimization and acceleration. Interactive systems that include multi-modal approaches for emotion recognition using camera and microphone sensors are being researched for driver monitoring and human-computer interaction systems for more advanced affective computing [8]. Therefore, the necessity of fast and accurate facial expression recognition is becoming an increasingly important research topic. In this study, we introduce a knowledge distillation (KD) approach for training a student model based on a state-of-the-art (SOTA)-level teacher model of more than 4 G (\(\times 10^{9}\)) multiply-accumulate operations (MAC) of computation, thereby reducing the computation to 0.3 G (\(\times 10^{9}\)) MAC. MAC is a metric used to assess the amount of computation required for model inference, and represents the number of multiplication and accumulation operations. According to this metric, our proposed method uses more than 50 times less computation than the existing models. This means that it can be applied to different types of on-device programs, making it universally applicable. Furthermore, because it can predict facial expressions in real time, it can observe instantaneous facial expressions such as micro-expressions. We propose a facial expression recognition learning method using a teacher bound that can improve both classification and regression performance by learning both loss of classification and regression problems, not only simple KD. The proposed method shows more than 10% performance improvement compared to SOTA methods, with improved performance in both valence and arousal measurements. Multi-task EfficientNet-B2 is known to display SOTA performance with an accuracy of 63.03% [9], according to the literature [10]. The proposed method has been confirmed to provide the best results considering model size and processing speed, and is an approach that can be executed in real-time on mobile and embedded systems with a computation time only 10% that of other SOTA-level approaches. Therefore, we conducted research to improve model performance and render the model lightweight enough that it can be used on mobile devices in real-time. The rest of this paper is organized as follows. Section 2 provides an overview of related research, including a description of the two KD methods and an explanation of the model adopted as the basic teacher model. In addition, this section discusses the two types of models selected as the student model, along with a brief discussion of the rationale for choosing them. Section 3 is dedicated to a detailed description of the proposed method, providing a comprehensive explanation of the approach. In Section 4, the performance of the proposed method is presented, demonstrating its effectiveness. Finally, the paper is concluded with a discussion and conclusions.
# Nonlinear Solid-Fluid Coupled Seismic Response Analysis of Layered Liquefiable Deposit
## 3 Numerical Analysis and Results
### Progressive Liquefaction of Site  
Compared with non-liquefied soils, the accumulation of EPWP during an earthquake is a typical seismic response of liquefiable soils. EPWP is a critical indicator for the initiation of liquefaction and the stress state in saturated soils. Figure 8 displays the EPWP along the soil depth at different moments under two input motions. The initial effective vertical stress, \(\sigma^{\prime}_{z0}=\gamma^{\prime}h\), is also plotted as a reference, where \(\gamma^{\prime}\) is the submerged unit weight of the loose sand and \(h\) is the burial depth. When EPWP at a specific depth reaches the initial effective vertical stress line, the soil loses its shear strength and behaves like a liquid. It can be seen from Figure 8a that due to the liquefiable loose sand layer with an overlying crust clay layer with extremely low permeability, the crust clay layer prevents the pore water from dissipating upward and causes EPWP to gradually accumulate at the interface between the non-liquefied crust clay layer and the liquefied loose sand layer. Therefore, the upper layer of saturated loose sandy soil firstly reaches the liquefaction initiation state within 3 s, and then the soil liquefaction gradually develops downward. The whole loose sand layer reaches the initiation of liquefaction at about 20 s. As shown in Figure 8b, it is observed that most of EPWP are developed during 6-20 s of the Kobe motion, which corresponds to most of its Arias intensity accumulation. The entire loose sand layer liquefies in about 10 s, after which the pore pressure begins to dissipate and the degree of soil liquefaction  
Figure 6: Shear stress–strain curves under the Kobe motion.  
Figure 7: Effective stress paths under the Kobe motion.  
is significantly less than in the case of the Kobe Motion. It is also worth noting that the excess pore water pressure of the dense sand layer is much lower than the initial effective stress under both input motions. Figure 9 illustrates the shear strain distribution along the soil depth at different moments under two input motions. Large shear strain mainly concentrates in the loose sand layer. Figure 9a demonstrates the maximum shear strain accumulation along the interface between the crust clay and loose sand layers after reaching an initial liquefaction at approximately 2 s until it reaches the maximum shear strain value (\(\gamma_{SA}=22.3\%\)) under the El-Centro motion. In comparison with the aforementioned position, the shear strain (\(\gamma_{SA}=16.5\%\)) of loose sand at the interface between the loose and dense sand layers is smaller. The difference may be attributed to the energy flux of two input motions. The distribution pattern of the shear strain of the Kobe case is similar to the El-Centro case, as shown in Figure 9b.
# Robust Optimization of Transport Organization for China Railway Express
## 4 Example Analysis
### Example Data  
Relevant parameters of freight train transportation, as presented in Table 2, are obtained through the freight information network platform of the China Railway Corporation, along with data from railway departments and surveys conducted on China-Europe freight trains.  
Based on the relevant research data, the cost data of loading, unloading, and shipping operations in the railway station are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Argument**} & \multirow{2}{*}{**Symbol**} & \multicolumn{2}{c}{**Type of Train**} \\ \cline{3-4}  & & **Domestic Road** & **Transnational Road** \\  & & **Transport Train** & **Transport Train** \\ \hline Average travel speed (km\(\cdot\)day\({}^{-1}\)) & \(S_{p}\) & 500 & 1000 \\ Packing capacity (TEU) & \(Q_{v}\) & 100 & 75 \\ Fixed cost of opening a column (CNY\(\cdot\)column\({}^{-1}\)) & \(C_{p}\) & 22,450 & 53,300 \\ Variable cost of opening a column (CNY\(\cdot\)km\({}^{-1}\)) & \(C_{ijp}\) & 250 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Train transport parameters.  
Figure 1: Diagram of the transport network of China–Europe freight trains.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Argument** & **Symbol** & **Data** \\ \hline Cost of staging center transfer waiting operation & & \\ (CNY\(\cdot\)TEU\({}^{-1}\)) & \(C_{i}^{3}\) & 580 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cost parameters of in-station loading and unloading operations.  
By referring to the relevant information regarding the Belt and Road network platform and combining it with the electronic map ranging, the distance parameters of each node are obtained, as shown in Table 4.  
Refer to the relevant information about the network platform of each train company to obtain the 2021 freight volume data of each node, as shown in Table 5. Among them, Urumqi only serves as a gathering center node and does not generate freight demand.  
In the determined environment, the optimal transportation organization scheme obtained by the solution is shown in Table 6, in which the total number of domestic section transport trains is 0, the total number of transnational section transport trains is 58, and the total transportation cost is CNY 86,478,780.
## 2 Physical and Mechanical Properties of Rock Mass Under THMC Coupling
### Theoretical Study of Multi-Field Coupling Mechanics in Rocks
#### 2.3.1 Rock Strength and Failure Mechanisms  
The \"rock strength\" refers to the inherent strength of the intact rock material, while the term \"rock mass\" denotes the in situ geological body, which includes the intact rock as well as discontinuities such as joints and fractures. The strength theories and failure mechanisms accurately reflect the differences in behavior between an intact rock and a larger rock mass.  
Rock strength is defined as the resistance a rock mass offers against failure, which is directly tied to its stress and strain conditions. The point at which a rock loses its load-bearing capacity, referred to as rock failure, marks a special deformation state, and the stress value at this point is considered the rock's strength. Rock failure and strength are intrinsically linked. When the rock reaches its failure threshold, its mechanical integrity collapses, leading to catastrophic results in engineering applications. Various strength theories have been developed to describe this failure process.  
Rock strength theories can be broadly categorized into two groups, as shown in (Figure 7): theoretical strength theories and empirical strength theories. Theoretical strength theories derive from fundamental principles of material mechanics and elastic mechanics. These include the maximum normal stress theory, maximum shear stress theory, maximum positive strain theory, octahedral shear stress theory, the widely used Mohr-Coulomb strength theory, [PERSON]'s fracture theory, its subsequent modifications, and double shear strength theory [145]. Conversely, empirical strength theories are derived from experimental observations and are used to approximate real-world failure processes. One notable example is the Hoek-Brown empirical strength criterion, which is widely employed in rock mechanics due to its applicability across a variety of geological settings.  
Figure 7: Theory of rock strength.  
In recent years, the Hoek-Brown strength criterion, Mohr-Coulomb strength criterion, Drucker-Prager strength criterion, and Griffith strength criterion have been the most commonly utilized models for assessing rock strength and failure [146; 147; 148; 149]. Each criterion offers insights into different failure modes, stress distributions, and fracture mechanisms, providing engineers and researchers with tools to predict rock mass behavior under various loading conditions.
## 5 Requirements of UWSNs
### Complexity  
The specification of sensor node placement at the position is also crucial for UWSN. Thus, a complexity factor need to be considered before setting up the networking platform which incorporates physical aspect, firmware and network configuration of nodes placement. Additionally, routing protocol selection and computing complexity contributes in identifying routes dynamically with no added information or prior knowledge about other nodes. Apart from that,  
Figure 4: Requirements of UWSNs.  
node algorithm complexity is another factor that need to be considered since it influences the energy optimization of the nodes. Local nodes' energy consumption is correspondingly depending on computational complexity and transmission power aspects. Underwater acoustic channel complexity such as multipath, Doppler shift, considerable attenuation and a high delay are also requirements that affect the performance of node localization methods.
## 3 Results
### Results for Step 2  
Having applied the correction algorithm, the peak values produced from the corrected accelerometer were highly reliable, with ICC values for repeat trials being above 0.95 for both forward and lateral bending.  
Error analysis for the corrected accelerometer angles compared to OEM in the forward-bending plane (rotation about Y) across different initial orientation offsets is presented in Figure 3D-F. Limits of agreement ranged from \(-0.49^{\circ}\) to \(1.52^{\circ}\). The most extreme offset measured (\(20^{\circ}\) tilt (rotate about Z) and \(20^{\circ}\) twist (rotate about X)) resulted in an average RMSE of \(0.75^{\circ}\) (uncorrected RMSE of \(5.86^{\circ}\)).  
In the lateral-bending plane (rotation about Z), the error for the corrected accelerometer across different initial orientation offsets is presented in Figure 4D-F. Limits of agreement ranged from \(-0.47^{\circ}\) to \(1.68^{\circ}\). At the most extreme offset (\(50^{\circ}\) pitch (rotation about Y) and \(20^{\circ}\) twist (rotation about X)), the average RMSE was \(0.79^{\circ}\) (uncorrected RMSE of \(7.54^{\circ}\)).  
Through-range analysis for corrected accelerometer angles demonstrated that with increasing tilt and twist offset and increasing velocity, error remains consistently below \(2.3^{\circ}\) (Figure 6). Average RMSE for the through-movement portions of the graph across offsets for slow (average \(49^{\circ}\) s\({}^{-1}\)\(\pm\)\(11^{\circ}\) s \({}^{-1}\)) and fast (average \(83^{\circ}\) s\({}^{-1}\)\(\pm\)\(24^{\circ}\) s\({}^{-1}\)) velocity are presented in Table 2 and are consistently below \(1.2^{\circ}\).
## 2 Related Work
### Geometric Information Content Measurement of Area Objects
Considering the subjectivity and uncertainty of manually determining the weights between features, some scholars have proposed various regression models for measuring geometric information content from a data-driven perspective. For instance, [PERSON] et al. considered 17 features such as angular Shannon entropy, mean difference between adjacent angles, length of the longest axis, and solidity of convex hulls when calculating graphical complexity [25]. They employed a regression analysis guided by human experience to construct a multiple linear regression equation for combining multiple features. Although this method considers numerous geometric features and comprehensively characterizes the complexity of area objects, it poses a new challenge. Some scalar features have variable ranges, such as the entropy of side lengths, increment of convex hulls, length of the longest axis, and the number of concave and convex nodes. Normalization based on the range of sample data often leads to information loss. In summary, there are three major shortcomings in the existing methods for measuring the geometric information of area objects: the lack of a unified standard for calculating feature values, the dependence on subjective experience for determining feature weights during multi-feature combination, and the need for normalization due to inconsistent feature value scales, which can lead to information loss.
# Banana Peels: A Genuine Waste or a Wonderful Opportunity?
## 4 Potentials of Banana Peels
### Banana Peel Extract as a Reducing Agent in the Formulation of Silver Nanoparticles  
Silver has a crucial function in metallic nanoparticles as an inorganic anti-bacterial component. Silver nanoparticles may be made for various purposes. The preparation techniques include electrochemical, green chemistry, simple procedure and sono-chemical breakdown of silver complexes [112]. The most common and simple method for producing silver nanoparticles is the reduction of metal salts. Several chemical reducing agents are used, such as hydrazine, sodium citrate, sodium borohydride, ascorbic acid, ammonium formate and dimethylformamide, to produce stable and variably structured silver nanoparticles in an aqueous solution [113; 114].  
Silver nanoparticles are produced using expensive, time-consuming, energy-intensive and hazardous physical and chemical procedures. Therefore, there is an unmet need for a practical alternative to the plant extract-based method of manufacturing silver nanoparticles [115]. Because it tends to change silver ions (Ag\({}^{+}\) to Ag\({}^{0}\)), banana peel extract can be utilised as a reducing agent to produce silver nanoparticles. Silver nitrate (AgNO\({}_{3}\)) was reduced using banana peel extract to create silver nanoparticles [116]. Surface plasmon resonance (SPR) bands, a unique visual property of noble metals, are present. Banana peel extract is reportedly utilised to biosynthesise silver nanoparticles, which have a clearly discernible SPR band. These biosynthesised nanoparticles have high anti-bacterial activity and successfully help against clinically isolated multidrug-resistant human infections [117]. Compared to Gram-positive bacteria, Gram-negative bacteria are more resistant to them. These nanoparticles also possess powerful anti-fungal and anti-bacterial capabilities [118].
# General Moving Object Localization from a Single Flying Camera
## 5 Implementation
### Test Environment  
We should choose a test environment where the test subjects can move freely in any direction. In our experiments, we choose to detect pedestrians as they are not confined to moving along fixed roads (as opposed to cars). Our algorithm is sufficiently general and it can work on other object classes if we replace our object detector with an appropriate one for the object class of interest.  
Ideally, we should allow our test subjects (i.e., pedestrians in this case) to walk around freely in the test environment, in any direction of their choice. However, regular people do not walk around aimlessly in an environment. Thus, instead of conducting a contrived experiment where we pay the same subject to walk around the test environment in fixed directions, we decided to choose a busy path where lots of pedestrians walk along it with varying walking speeds. To obtain the effect of the pedestrians walking in different directions, we make the drone fly in different directions with respect to the path. Figures 11 and 12 illustrate the ideal layout and the equivalent layout of the experiment.  
We decided to record data where the camera moves with five different slopes with respect to the path. As the test environment requires a sufficiently large area for the drone to fly, we selected a large open field (100 m wide and 50 m long, approximately) at the Gwangju Institute of Science and Technology with a path that is used frequently by pedestrians to cross from one side of the campus to the other. Figure 13 shows the layout from a Google map of the area. We capture over 70 image sequences with different pedestrians moving at their own natural pace.  
Figure 11: Ideal layout for subjects moving in an arbitrary direction.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)
### Equations of Motion  
As shown in Figure 4, a DDIS is incorporated into an SDOF frame structure having mass \(m\), stiffness \(k\), and damping coefficient \(c\). The governing equation for the present system can be expressed as follows:  
\[\left\{\begin{array}{l}m\ddot{u}(t)+c\dot{u}(t)+ku(t)+k_{t}\big{[}u(t)-u_{d}(t )\big{]}=-m\ddot{u}_{g}(t)\\ m_{\dot{m}}\ddot{u}_{d}(t)+F_{d}(t)=k_{t}\big{[}u(t)-u_{d}(t)\big{]}\end{array}\right. \tag{4}\]  
where \(\ddot{u}_{g}(t)\) represents the acceleration of the ground motion, and \(u(t)\) and \(u_{d}(t)\) are respectively the displacement of the primary structure relative to the ground and deformation of the DDE. The force \(F_{d}\) is given by Equation (2).