esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasmonic Tweezers towards Biomolecular and Biomedical Applications
## 2 Optical Forces
### Chiral Optical Force  
Angular momentum also can be transferred to a non-chiral particle to make it spin or rotate. More important for chiral particles, a right-handed one and a left-handed one experience chiral optical forces in the opposite directions. This phenomenon is important to separate particles with different chiralities. For example, a protein could become toxic to a cell if it loses its original chirality and this process has been suggested as one of the underlying causes of Alzheimer's, Parkinson's and type II diabetes [54]. Besides sensing the chirality of these proteins, to separate the misfunction proteins at each stage of a disease could be meaningful to design specific drugs to target these proteins at different stages. In addition, for enantiopure drugs that target specific cellular receptors, the correct chirality is a guarantee to provide effective therapy at the molecular level. This separation mechanism could be used as quality control methodology.  
A chiral particle can be modelled as a pair of interacting electric and magnetic dipoles, and this interaction can be expressed with a polarizability \(\alpha_{em}\), as shown in Equation (10), where \(\kappa\) is the chirality of the particle, \(\mu\) and \(\mu_{b}\) is the relative permeability of the particle and background medium, respectively. Then, the third term in Equation (2) can be modified to Equation (11) for transverse optical force of the chiral optical force. Here, the curl-spin force and vortex force are negligible for nanoscale bioparticles when the trapping laser frequency is selected in the visible and near infrared regimes, which is most of the circumstances to avoid the background medium absorption. Since \(\kappa\) could be positive or negative, this chiral gradient force could be addition or subtraction to the intensity gradient force, and particles with difference chirality can be separated [55]:  
\[\alpha_{em}=-12\pi(\frac{d}{2})^{3}\frac{i\kappa\sqrt{\mu_{0}\varepsilon_{0}}}{ (\varepsilon+2\varepsilon_{b})(\mu+2\mu_{b})-\kappa^{2}}, \tag{10}\]  
\[F_{tr,ch}=\frac{1}{2}\text{Im}(\alpha_{em})\
abla\text{Im}(\vec{E}\cdot\vec{H} ^{*}). \tag{11}\]  
[PERSON] and [PERSON] theoretically proposed using chiral SPPs based on metal thin films to generate transverse spin angular momentum (SAM) for right- and left-handed molecule separation [56]. The proposed chiral molecule separation scheme is as follows. A left-handed circularly polarized dipole was used as the source. Figure 1a demonstrates the generated chiral transverse forces on two different chiral helix tubes. They have a radius of 2.5 nm, a period of 7.5 nm, five loops and different handedness. They were put 500 nm away from the near field chiral source along \(x\)-axis and 10 nm above the metal surface along the \(z\)-axis. In Figure 1b, the distribution of SAM in the \(x\)-\(y\) plane is demonstrated with
# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 3 Experiments and Results  
### Ceramic Defect Detection Model Based on Lightweight YOLOv10s  
#### 3.1.1 Evaluation Metrics  
During the evaluation of ceramic defect detection, metrics such as Precision (P), Recall (R), [EMAIL_ADDRESS] (mean Average Precision at an Intersection over Union (IoU) threshold of 0.5), and F1-score are employed to measure the accuracy of the detection model. Additionally, metrics like Params (number of parameters), GFLOPs (billions of floating-point operations), and size (model size) are used to assess the lightweight nature of the detection model. Precision (P) represents the proportion of correctly detected positive instances among all instances detected as positive, while Recall (R) denotes the proportion of real positive instances that are correctly detected as positive. The specific calculation methods are shown in Equations (7) and (8), respectively. [EMAIL_ADDRESS] refers to the mean average precision across all categories at an IoU threshold of 0.5, which is the area under the Precision-Recall (P-R) curve in the detection results. The specific calculation formula is presented in Equation (9). The F1-score is the harmonic mean of precision and recall, considering both metrics comprehensively, and its specific calculation formula is shown in Equation (10).  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{7}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{8}\]  
Figure 6: Flowchart of the adaptive Canny edge detection algorithm.  
\[mAP=\frac{1}{N}{\sum_{i=1}^{N}{\int_{0}^{1}{P_{i}(R)dR}}} \tag{9}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}\times 100\% \tag{10}\]  
Here, TP stands for True Positives, representing the number of positive samples correctly detected; FP stands for False Positives, indicating the number of negative samples incorrectly detected as positive; and FN stands for False Negatives, denoting the number of positive samples incorrectly detected as negative. The variable \"\(i\)\" signifies the serial number of a category, while \"\(N\)\" represents the total number of categories.
## 4 Implementation
### Data Augmentation  
We used various data augmentation techniques to enhance the diversity of the training dataset of the CK+ and KDEF and improve the generalization performances of the models. During the preprocessing phase, we applied augmentation methods such as rotation, horizontal shifting, and vertical shifting to the input images. Specifically, we configured the _rotation_range_ parameter to allow random rotations within a range of \(-20\) to \(+20\) degrees. Additionally, we used _width_shift_range_ and _height_shift_range_ to introduce random horizontal and vertical shifts to the images, respectively, with a maximum displacement of 20% of the total width and height. Furthermore, we enabled horizontal flipping using the _horizontal_flip_ parameter to further increase dataset variability. To ensure seamless augmentation, we used the 'nearest' fill mode to interpolate pixel values for newly created pixels. Lastly, we applied pixel normalization by rescaling the pixel values of all images to a range between 0 and 1 using the rescale parameter, aiding in the convergence of the model during training.  
For the FER2013 dataset, we employed different augmentation techniques compared to the KDEF and CK+ datasets. Specifically, we applied a rotation range of 10 degrees clockwise or counterclockwise, along with horizontal flipping. Additionally, we utilized a zoom range of [1.1, 1.2], allowing for random zooming between 1.1\(\times\) and 1.2\(\times\) the original size during training.  
Regarding the data split, we adopted an 80% training, 10% validation, and 10% test split for both the CK+ and KDEF datasets. For the KDEF dataset specifically, we used three facial postures instead of the original five, resulting in 420 images per class, and this yielded 2940 images in total. For the filtered FER2013 dataset, we preserved the identical data split as described by [PERSON] et al. [22], comprising training (27,310), validation (3410), and test (3420) sets. Table 1 presents the counts of training, testing, and validation samples for the datasets used in our experiment. All experiments were conducted using subject-independent data across all datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Training Size** & **Testing Size** & **Validation Size** \\ \hline KDEF & 2350 & 294 & 294 \\ CK+ & 784 & 99 & 98 \\ Filtered FER2013 & 27,310 & 3410 & 3420 \\ \hline \end{tabular}
\end{table}
Table 1: Data split for each dataset.
## 6 Results
### DTN Architecture
The Bundle layer, between the application and transport layer, implements a custody transport mechanism through a message persistence store to reduce the impact of a link down during the transmission. Zhang states that to design a routing algorithm for DTN, one should consider the network topology dynamic change rate, communication link capacity, node cache capacity, and the average traffic volume between nodes [21]. DTN can interface any network protocol, through convergence layers, including TCP, UDP, Ethernet, Negative Acknowledgment-Oriented Reliable Multicast (NACK-NORM), AX.25, UCL, Licklider Transmission Protocol (LTP), and IEEE 802.15.4. Voyiatzis says that WSN can benefit from DTN as they are constricted by energy as is the case of Zigbee and 6 LoWPAN--two key concepts in DTN. The first one is in network storage, which is provided for the application layer. The second one refers to being an agnostic network given the capacity of the protocol to integrate any kind of network protocol stack [22].
###### Abstract  
Modelling events that change over time is one of the most difficult problems in data analysis. Forecasting of time-varying electric power values is also an important problem in data analysis. Regression methods, machine learning, and deep learning methods are used to learn different patterns from data and develop a consumption prediction model. The aim of this study is to determine the most successful models for short-term power consumption prediction with deep learning and to achieve the highest prediction accuracy. In this study, firstly, the data was evaluated and organized with exploratory data analysis (EDA) on a ready dataset and the features of the data were extracted. Studies were carried out on long short-term memory (LSTM), gated recurrent unit (GRU), simple recurrent neural networks (SimpleRNN) and bidirectional long short-term memory (BiLSTM) architectures. First, four architectures were used with 11 different optimization methods. In this study, it was seen that a high success rate of 0.9972 was achieved according to the R\({}^{2}\) score index. In the following, the first study was tried with different epoch numbers. Afterwards, this study was carried out with 264 separate models produced using four architectures, 11 optimization methods, and six activation functions in order. The results of all these studies were obtained according to the root mean square error (RMSE), mean absolute error (MAE), and R\({}^{2}\)_score indexes. The R\({}^{2}\)_score indexes graphs are presented. Finally, the 10 most successful applications are listed.  
deep learning models; electricity consumption prediction; exploratory data analysis (EDA); long short-term memory (LSTM); gated recurrent unit (GRU); simple recurrent neural networks (Simple RNN); bidirectional long short-term memory (Bi-LSTM); optimization and activation functions +
Footnote †: journal: Applied sciences
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Algorithm
#### 2.4.1 Step 1: Nodes' Discovery  
During this step, each sensor node (HSN and CSN) receives a beacon message carrying a query from the base station and signals its presence in the network by selecting a potential parent (next hop to the base station) and replying to the query by sending to its parent node a hello packet containing its identification number (\(ID\)) and service type (\(SE\)), coordinates (\([X,Y,Z]\)), initial energy (\(IE\)) at start-up of the routing process and, later, its residual energy (\(E=\tau_{cgy}(ID)\)) during operation. Note that in our problem formulation, \(SE=\mathcal{S}(ID)\) and \(E=\tau_{cgy}(ID)\).
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 2 Modeling and Analysis of Corner
### Local Corner MEC Model  
Combining the branch reluctance of the limb joint part, corner, and core window inner corner leakage, a local corner MEC model is proposed, as shown in Figure 8.  
Figure 7: The flux density distribution comparison of line _Lyb_ and _La_: (**a**) the \(B\) distribution of line; (**b**) the maximum, minimum, and ratio of \(B\).  
\(R_{pc,i}\) and \(R_{c,i}\) are connected in series as a sub-branch and then in parallel with \(R_{uri}\). The \(R_{py}\) is connected in series with the parallel branch. There are three meshes in the MEC network, which make the flux density of the corner different in four branches. During the iterative calculation, the flux will automatically calculate the reluctance based on the branch length and permeability. The local corner MEC model fully describes the flux paths at the corner and the flux density distribution. It makes the flux density distribution consistent with the real distribution to improve the calculation accuracy.
## 4 Simulation and Analysis
### Incrementing Nodes Initial Energy  
Nodes' initial energy is the key factor in a WSN, since nodes are battery limited and this has the main role in whole network stability and performance and there is no possibility to recharge the battery once a node is placed in the field. We analyze the network behavior by increasing the nodes' initial energy \(E_{0}\) from 0.5 J to 0.8 J and observe our proposed protocol compared to other algorithms. It is obvious that increased network energy will affect the lifetime and stability period. We analyze the influence of added energy in advanced nodes on the whole network system in the process of cluster head election and the data delithe very to BS, since advanced nodes consume more energy than normal nodes and advanced nodes have different energy from each other after cluster heads are elected. Cluster heads are then formed based on the remaining residual energy. In Table 3(c), the networks energy consumption is demonstrated. According to the graphs in the figure, it is seen that proposed EZ-SEP protocol gives better results compared to the other three protocols, when the nodes' initial energy is changed. The lowest energy consumption is observed in EZ-SEP, while the highest is seen in LEACH. Nevertheless, as compared to the previous initial value, the new added value in SEP and LEACH gives an improved network lifetime. Thus, nodes are almost about to exhaust after the 2000 th round, while previously they became exhausted twice as earlier at round 1000. The same is true for Z-SEP at round 3000, while in EZ-SEP it happens after 3500 rounds. In the case of nodes life, in the EZ-SEP protocol nodes show the longest stability period almost until the end of round 9000 and still remain alive after with first node death at round 2467, while SEP performs the worst as illustrated by all the nodes being dead at the 4000 th round and the death of the first node at round 1707. In LEACH, the first death occurs at round 1610, which is the worst index. The parent protocol Z-SEP has its first node death at the 2489 th round, which is the best index, but is much more unstable than the proposed EZ-SEP. Thus, the proposed algorithm is the best when nodes have added initial energy. In data transmission ratio, EZ-SEP shows more packets delivered in the 2500-8500 rounds range and is close with Z-SEP afterwards, which means that EZ-SEP sends much more data in total than the Z-SEP protocol.
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
## 2 Formulation of the Propagation of OCLVs in Turbulent Atmosphere
According to the Huygens-Fresnel integral, \(S_{f}(-\rho/z)\) is obtained by:  
\[S_{f}(-\rho/z)=\left|\widetilde{A}(\rho/z)\right|^{2}/\lambda^{2}z^{2}, \tag{7}\]  
where \(A(\mathbf{r})=\tau(\mathbf{r})\exp(ik\mathbf{r}^{2}/2z)\) and the hat tilde denotes the two-dimensional Fourier transform, and [47]:  
\[p_{2}\left(\mathbf{v}^{\prime}\right)=\frac{3}{2\pi^{3}Tz}\exp\left(-\frac{3 \mathbf{v}^{\prime 2}}{2\pi^{2}Tz}\right), \tag{8}\]  
with:  
\[T=\int_{-\infty}^{\infty}\kappa^{3}\Phi_{n}(\kappa)d\kappa. \tag{9}\]  
Here \(\Phi_{n}(\kappa)\) is the power spectrum density of the atmospheric turbulence refractive index fluctuations, \(\kappa\) denotes the frequency in the space. Here we adopted the Tatarskii spectrum for the power spectrum density \(\Phi_{n}(\kappa)\), which is written as [48]:  
\[\Phi_{n}(\kappa)=0.033C_{n}^{2}\kappa^{-11/3}\exp\left(-\frac{\kappa^{2}}{ \kappa_{m}^{2}}\right). \tag{10}\]  
Here \(C_{n}^{2}\) denotes the structure parameter of the atmospheric turbulence and \(\kappa_{m}=5.92/l_{0}\) with \(l_{0}\) being the turbulence inner scale. In this paper, we set \(l_{0}=0.01\) m. Taking Equation (10) into Equation (9), we obtain:  
\[T=0.771C_{n}^{2}. \tag{11}\]  
Taking Equations (2), (4), (7), and (8) into Equation (6), and using the fast Fourier transform (FFT) algorithm, one can numerically analyze the spectral density of the OCVLs propagating in turbulent atmosphere by using MATLAB (The MathWorks, Natick, MA, USA). For further investigating the propagation properties of the OCVLs, we also study the \(M^{2}\)-factor and beam wander [49; 50; 51]. The beam's second-order moments in turbulent atmosphere in the receiver plane are obtained as follows [51]:  
\[\left\langle r^{2}\right\rangle=\left\langle r^{2}\right\rangle_{0}+2( \mathbf{r}\cdot\mathbf{\theta})_{0}z+\left\langle\theta^{2}\right\rangle_{0} \!\!\!z^{2}+\frac{4}{3}\pi^{2}Tz^{3}, \tag{12}\]  
Beam wander is characterized by \(\left\langle r_{\varepsilon}^{2}\right\rangle^{1/2}\) of the variance of off-axis displacement of the instantaneous beam center. Beam wander is defined as follows [54]:  
\[\left\langle r_{\varepsilon}^{2}\right\rangle=\frac{16\pi^{3}}{\lambda^{2}}W_{ FS}^{2}\int_{0}^{L}\int_{0}^{\infty}\kappa\Phi_{n}(\kappa)\exp\left(-\kappa^{2}W_{ LT}^{2}\right)\Bigg{\{}1-\exp\left[\frac{-2L^{2}\kappa^{2}(1-z/L)}{k^{2}W_{ FS}^{2}}\right]\Bigg{\}}dxdz, \tag{25}\]  
taking Equation (10) into Equation (25), Equation (25) is simplified as:  
\[\left\langle r_{\varepsilon}^{2}\right\rangle=\tfrac{0132}{\lambda}\pi^{3}W_{ FS}^{2}\Gamma(-5/6)\int_{0}^{L}\Bigg{\{}\left(2.85\times 10^{-6}+W_{LT}^{2}\right)^{5/6}- \left[\tfrac{2L^{2}(1-z/L)^{2}}{k^{2}W_{FS}^{2}}+2.85\times 10^{-6}+W_{LT}^{ 2}\right]^{5/6}\Bigg{\}}dz, \tag{26}\]  
the evolution properties of the beam wander in turbulent atmosphere of OCVLs can be calculated numerically by taking Equations (23) and (24) into Equation (26).
## 1 Introduction
### Safety Standards for Lithium Ion Batteries  
The widespread adoption of lithium ion battery technology with the concomitant risks of their use in vehicles has necessitated the development of global standards and regulations to ensure their safe introduction into automotive industry [6; 7; 11]. The United Nations Economic Commission for Europe (UNECE) issues type approval regulations, creating consistent technical guidelines for vehicles and components. In the US, the National Highway Traffic Safety Administration (NHTSA) sets safety requirements through the Federal Motor Vehicle Safety Standards (FMVSS) [6; 7].  
The standards focus on different aspects, such as the following [6; 7; 11; 12; 13; 14; 15]:  
* Electrical safety tests (e.g., IEC 62133, UL 2054, UL1642)--overcharge, short-circuit, over-discharge;
* Mechanical safety test [7]--vibration and shock tests (e.g., IEC 62133, UN 38.3, ISO 12405, ECE R100), the nail penetration test (e.g., IEC 62133, UL 1642), and drop testing (e.g., IEC 62133, UN 38.3;
* Thermal safety tests environmental (e.g., IEC 62133, IEC 61960, UL 1642, ECE R100, UN 38.3)--high/low temperature exposure, thermal cycling;  
Figure 1: Cell, module, and battery pack utilization of lithium ion batteries.  
* Other environmental safety--humidity tests (e.g., IEC 60068-2-78), low pressure (altitude) tests (e.g., IEC 62133, UL 1642), dust and water resistance (e.g., IEC 60529), and the salt spray test. These tests help to identify weak points in battery systems and evaluate their behavior in harsh situations. The result of revealing weaknesses in the batteries under investigation may be mechanical or electrical damage to the batteries and, as a result, initiation of a thermal runaway (TR) of the battery, ending in a battery fire. TR is a chain reaction causing overheating, combustion, or even explosion of batteries. TR can stem from both external factors, like abuse, and internal factors, like manufacturing impurities. The severity of the consequences depends on multiple variables, such as the state of charge, charging/discharging rate, cell type, history, and materials. International, national, and regional standards set pass/fail criteria for battery tests. For instance, some standards, like UN/ECE-R100.02 [13], UL 2580 [14], and ISO 12405-4 [16], require no fire, explosion, rupture, or leakage under foreseeable misuse.
# State Analysis of the Water Quality in Rivers in Consideration of Diffusion Phenomenon
## 2 Materials and Methods
### River Model Described by a Partial Differential Equation of Hyperbolic Type  
When considering a state vector whose coordinates are influenced by river length and time, the Streeter-Phelps equations transform to first-order hyperbolic differential equations once mass balance is taken into account [30]. These equations include a description of a part of the river called a section, e.g., for the i-th section, the equations take the form:  
\[\frac{\partial x_{i}}{\partial t}+V\frac{\partial x_{i}}{\partial z}=Ax_{i}+Bw \tag{15}\]  
where \(x_{i}\)--state vector, \(t\)[day]--time, \(z\)--length, \(A\)--state matrix, \(V=diag(v,v)\)--diagonal matrix with water flow velocities \(v\), \(B=diag(1,1)\)--interference interaction matrix, and \(w\)--interference vector. The diagonality of the flow velocity matrix \(V\) reflects the serial character of objects of this type in which the state vector \"travels\" only in one direction with the same velocity. The boundary conditions, included in the model, are described by a boundary condition concerning the beginning of the considered section in the time domain:  
\[x_{i}(0,t)=M_{i}x_{i-1}(1,t)+w_{bi}(t)+R_{bi}u_{bi}(t) \tag{16}\]  
where \(M_{i}\) is a matrix defining the boundary links between individual river sections, the \(w_{bi}\) vector represents the disturbance of the boundary values, and control \(u_{bi}\) only affects the coordinate of the state vector \(x_{2}\) and is located at the beginning of the river section. The second condition is the initial condition, which applies to the entire length of the section at time \(t=t_{0}\):  
\[x_{i}(z,t_{0})=x_{i0}(z),i=1,\ldots,N \tag{17}\]  
The first-order hyperbolic partial differential Equation (15) represents a general approach to the river modelling, in contrast to the equations described by the Streeter-Phelps model ((10), (13) and (14)). By having a velocity matrix whose values depend on the length of the river, we do not generalise the average velocity along the length of the river but factor in the natural variations in the velocity parameter. In further considerations of the mathematical modelling of rivers, a constant value was assumed for the velocity along a given section of the river.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 2 Background Research
### Semi-Supervised Learning for Object Detection  
The limited availability of labeled data in aerial imagery has driven research into semi-supervised learning approaches, which aim to utilize both labeled and unlabeled data effectively. Frameworks such as SOOD [19] and PSCD [20] have employed pseudo-labeling and consistency-based training strategies to improve model performance in low-label settings. These methods have demonstrated potential, particularly in enhancing generalization across diverse datasets.  
Despite their advantages, existing semi-supervised approaches often struggle with noisy pseudo-labels, a challenge that is especially pronounced in oriented object detection where precise rotational annotations are critical. RASST addresses this issue through its Pseudo-Label Guided Learning (PGL) framework, which employs RAW and GC losses to refine pseudo-labels. By reducing noise and enhancing the reliability of pseudo-labels, RASST ensures a more robust learning process, even in scenarios with limited labeled data.