esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 3 Advanced LSFP
### Advanced LSFP
In the MMSE pre-equalization, the corresponding weighting matrix \(W\) can be expressed as:  
\[W=\beta^{\prime}\times\left(H_{D}^{DL}\right)^{H}\left[H_{D}^{DL}\times\left(H_{ D}^{DL}\right)^{H}+\frac{\delta_{z}^{2}}{\delta_{x}^{2}}\times E\right]^{-1} \tag{30}\]  
\[\beta^{\prime}=\sqrt{\frac{M}{T\left[\left(H_{D}^{DL}\right)^{-1}\times\left(H _{D}^{DL}\right)^{H}\right]}} \tag{31}\]  
where \(\beta^{\prime}\) denotes a constant that keeps the total power constant after pre-equalization. By applying regular channel inversion technology to the LAS, substituting \(\beta^{\prime}\) into the LSFP matrix, and using the MMSE pre-equalization weighting matrix \(W\) to represent the O-LSFP matrix, the MMSE Advanced O-LSFP (MMSE-AO-LSFP) can be obtained as follows:  
\[W^{k}=\gamma_{A}\times\beta^{\prime}\times\left(B^{k}\right)^{H}\left[B^{k} \times\left(B^{k}\right)^{H}+\frac{\delta_{z}^{2}}{\delta_{x}^{2}}\times E \right]^{-1} \tag{32}\]  
\[\beta^{\prime}=\sqrt{\frac{M}{T\left[\left(B^{k}\right)^{-1}\times\left(B^{k} \right)^{H}\right]}} \tag{33}\]  
\[W^{\prime\prime}=\left[ \begin{array}{cc}\left(W^{1}\right)^{T}&\cdots&\left( W^{k}\right)^{T}\end{array} \right]^{T} \tag{34}\]  
where \(W^{k}\) denotes a matrix obtained by performing regular channel inversion processing on a large-scale fading factor matrix. \(W^{\prime\prime}\) denotes the MMSE-A-O-LSFP precoding matrix for regularized channel inversion processing. **Lemma 3**. : _In this work, the theorem presented in [10] was used, and it is as follows:_  
\[R_{D}^{kl}\geq log_{2}\left(1+SINR_{D}^{kl}\right) \tag{35}\]  
_The large-scale fading factor \(\beta_{j}^{kl}\) can be applied to obtain the data transmission rate, which is expressed as follows:_  
\[R^{kl}=log_{2}\left(1+SINR^{kl}\right) \tag{36}\]  
At the receiving end, in order to compensate for the amplification effect of a transmitter, the receiver uses the automatic gain control technology to adjust the received signal so that the received signal is multiplied by \(\frac{1}{\beta}\), and then the received signal of the same strength is restored.
###### Abstract  
The size, size distribution and magnetic properties of magnetite nanoparticles (NPs) prepared by co-precipitation without citrate, in the presence of citrate and citrate adsorbed post-synthesis were studied by X-ray Diffraction (XRD), Dynamic Light Scattering (DLS), Electron Paramagnetic Resonance (EPR) and magnetization measurements. The aim of this investigation was to clarify the effect of citrate ions on the size and magnetic properties of magnetite NPs. The size of the primary NPs, as determined by analysing the width of diffraction peaks using various methods, was ca. 10 nm for bare magnetite NPs and with citrate adsorbed post-synthesis, whereas it was around 5 nm for the NPs co-precipitated in the presence of citrate. DLS measurements show that the three types of NPs form aggregates (100-200 nm in diameter) but the dispersions of the citrate-coated NPs are more stable against sedimentation than those of bare NPs. The sizes and size distributions determined by XRD are in good agreement with those of the magnetic domains obtained by fitting of the magnetization vs. magnetic field intensity curves. Magnetization vs. magnetic field intensity curves show that the three kinds of sample are superparamagnetic.  
magnetic; nanoparticles; XRD; DLS; magnetic properties; superparamagnetism Article
## 2 Materials and Methods  
### Characteristic of End-Firing Optical Fiber  
The end-firing optical fiber (UM22-100, Thorlabs, NJ, USA) utilized in this study consists of three layers: buffer, cladding, and core. The outer diameter of the fiber is \(124\pm 3\) um. The buffer, composed of PI (polyimide) film, has a thickness of 7 um; the cladding, made of doped silica, has a thickness of 5 um; and the core, composed of high-OH silica, has a diameter of 100 um. The fiber has a numerical aperture of 0.22 \(\pm\) 0.02, which defines the range of angles over which it can accept light. The end-firing optical fiber transmits and emits light exclusively from the distal end of the fiber, maintaining its original structure without any modifications.  
A laboratory-fabricated prototype device was developed to integrate and deliver UV-C light through a thin optical fiber system. The 270 nm LED light source (XST-3535; Luminus Device, Sunnyvale, CA, USA) with a power output of 100 mW/cm\({}^{2}\) was used in this experiment. The optical fiber was connected to a UV-C LED (270 nm, XST-3535-UV-A60-CD270-00, Luminous Device, Sunnyvale, CA, USA) light source using a specialized double-lens (f = 50, WD = 33, Thorlabs, Newton, NJ, USA) optical system, as illustrated in Figure 1B. The divergent light is focused through a series of lenses to achieve a working distance of 65 mm and a numerical aperture (NA) of 0.22. Since the NA value of the lens in front of the optical fiber is smaller than the NA value of the single-mode optical fiber, using this double-lens optical system, all light passing through the first lens at a sub-horizontal angle can be condensed into the optical fiber. This controlled light path ensures precise and consistent delivery of UV light to the optical fiber.  
Figure 1: Schematic illustration of the optical fiber firing apparatus. (**A**) Diagram of the optical fiber, which consists of a core that transmits light, a cladding with a lower refractive index that surrounds the core to confine the light, and a buffer that protects the fragile fiber. (**B**) Newly developed laboratory-manufactured prototype device with a double-lens optical system for UV-C LED, designed to control light divergence and ensure precise beam direction. (**C**) A schematic representation of the model illustrating the application of the fiber optic system.
## 4 Experiments
### SVCs and Activations Spaces  
As outlined in Figure 4, we considered the feedforward layers of the encoder blocks, namely the dense layers that feed the succeeding encoder block. In the CuBERT transformer, there are 24 encoding blocks, each one composed by 1024 neurons with a 512-dimensional output. For each considered sub-concept, we started by collecting the activations obtained by flattening the output of the feedforward layers (see Section 3.2) for a balanced random sample, where 100 program instances were associated to the concept, and 100 did not represent it. Then, for both the fine-tuned models (i.e., the binary classifiers trained in the detection of the divide by zero and uncontrolled memory allocation vulnerabilities, respectively), and for each concept, we gathered the activations of the flattened layers as points of a \(512*1024\)-dimensional vector space, and for each of the 24 layers, we fit a linear support vector classifier (SVC) to correctly separate the points representing programs related to the chosen concept, and the points representing programs with no presence of the same concept.  
Ad the end of this procedure, besides the average accuracies obtained by the SVC over 10 runs, which are reported in Figures 5 and 6, for each concept, we identified the \"best\" layer (i.e., the layer that can most fruitfully used to distinguish programs that hold the concept from programs that do not, by considering its activation values) and the corresponding best SVM. The latter was chosen as a fitness evaluator for an evolutionary algorithm, as detailed in Section 4.4.  
Figure 4: An encoder block of the CuBERT transformer. The activation spaces are defined on the highlighted feedforward layers.  
Figure 5: Average accuracies obtained over 10 runs by the linear SVCs trained on different concepts.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 3 Function Finding Algorithm by Using Gene Expression Programming for Wireless Sensor Networks Data
### Coding of Gene Expression Programming (GEP)  
The gene is the basic unit of GEP [20]. In order to better describe GEP algorithm, the related definitions are given as follows.  
**Definition 1.**_Let string \(G\) be defined as a triplet \(G=<GHead,GTail,L>\), \(F\) be basic elementary function set and \(T\) be terminal set. Where \(GHead\), \(GTail\) and \(L\) represent head, tail, length of the \(G\) respectively. The elements of \(GHead\) randomly generates from \(F\) and \(T\), the elements of \(GTail\) randomly generates from \(T\). Then string \(G\) is called gene._  
**Property 1.**_Let the length of \(GHead\) be \(h\), the length of \(GTail\) be \(t\), maximum number of arguments of operator in the \(GHead\) be \(n\). Then, \(h\) and \(t\) follow the equation:_  
\[t=h\times(n-1)+1 \tag{1}\]  
**Definition 2.**_The string which is composed of one or more \(G\) is called the chromosome, and denoted as \(C\)._  
GEP adopts linear code of fixed length to represent an individual which is called a chromosome \(C\). However, the linear code can accurately show expression trees (ETs) of different shapes and sizes. During decoding, firstly, ETs is traversed from the upper to the bottom, the left to the right, and finally, function model is obtained.  
**Example 1.** Let function set be \(F=\{+,-,\times,Q\}\), terminal set be \(T=\{a,b\}\), length of gene head be \(h=5\), where \"\(Q\)\" represents the square root function. From function set \(F\), we know that maximum number of arguments of all operators is 2. According to Equation (1), length of gene tail is 6. The randomly generated chromosome is shown in Figure 2.  
The chromosome shown in Figure 2 consists of two genes. The corresponding expression trees (ETs) is shown in Figure 3.  
Figure 3: The corresponding expression trees.  
Figure 2: A random chromosome in gene expression programming (GEP).  
The decoding of Sub-ET\({}_{1}\) and Sub-ET\({}_{2}\) is respectively performed. The result of decoding is linked by addition function and simplified by mathematica software. The final function model is \(f\left(a,b\right)=3a+ab+\dfrac{1}{b}-\sqrt{b}\).
# Quantum Synchronization and Entanglement of Dissipative Qubits Coupled to a Resonator
## 2 Model Description
### Numerical Methods
The choice of the initial density matrix is not important for the final result, it only influences the convergence speed of the iterative method. As for any iterative numerical method, the initial guess for the interaction influences the number of iterations required for convergence, it will not influence the result, provided the solution is unique and the iteration converges (which is the case here). In practice, we use the guess provided by the perturbation theory and/or guesses by the steady-state density matrix already obtained for some neighboring parameter values (such guesses are easily available when computing a frequency dependence). It is also possible to find the steady state by the numerical integration of time-dependent Lindblad Equations (4), (5), or (6). We use a standard odeint integration library [42] for the integration of equations of motion using a sparse matrix representation for all operators acting on the density matrix, which is represented as a dense matrix. The main integrator is an adaptive Runge-Kutta-Dorpi fifth-order stepping algorithm, although other integration schemes are also tested with similar results. In the time-dependent case, it is possible to check the validity of the RWA approximation since we have access to the full-time dependent evolution of the density matrix, including its oscillations around the RWA steady state, while integrating the stationary Equation (6) converges to the solution of Equation (7). In principle, the two semi-analytical approaches to the solution of the RWA Lindblad equation at weak and strong dissipations are well known and have been used for a description of various systems. They are known as the rate equation (weak dissipation) and the semiclassical (strong dissipation) approach (see, e.g., the refs. in the subsection below). In this work, we use certain improvements of these approaches for our model system, which allow us to efficiently obtain the steady state of the Lindblad equation with a rather large number of components. Also, the comparison with the results obtained for the Lindblad equation allows us to determine the regions of validity of these approaches. We note that, in the validity regime of the semiclassical approach, there is no entanglement of qubits, even if they can be well synchronized.
## 4 Results
### Ablation Experiments
The P34 modification of the YOLOv5s model (YOLOv5-p34), which was created by removing the P5 head structure and leaving the P3 and P4 heads, offers a strong balance in terms of accuracy and speed. YOLOv5s-P34 shows high performance compared to YOLOv5s in terms of accuracy, with 0.79 [EMAIL_ADDRESS] and a speed with 175 FPS. At the same time, the number of parameters, file size, and computational load are low. The carbon footprint value was 0.143 gCO2e. In order to further lighten this structure, P34 and Ghost modifications were combined to create the YOLOv5-P34-Ghost model. Although the model showed a lower value than P34 and Ghost-ECA modifications, with an accuracy value of 0.769 [EMAIL_ADDRESS], and a decrease compared to YOLOv5-P34, with an extraction value of 158.7 FPS, it had the lowest value among all modifications, with a computational load of 7.3 GFLOPs, 2.80 M parameters, and 6.1 MB file size. The ECA mechanism was added to this structure in order to increase the accuracy by improving the feature extraction capability without losing the gains obtained with the YOLOv5-P34-Ghost model. The YOLOv5-P34-Ghost-ECA modification achieved the highest accuracy rates of 0.822 precision, 0.785 recall, and 0.806 [EMAIL_ADDRESS]. The model provides fast and efficient inference performance with an inference rate of 163.9 FPS and a computational load of 8.4 GFLOPs. With 3.96 M parameters and a 10.7 MB file size, it produced a carbon footprint of 0.145 gCO2e during inference. This proposed YOLOv5-P34-Ghost-ECA modification is named GLDD-YOLOv5 for the study. The box loss and object loss curve of the proposed model over 300 epochs during the training process are shown in Figure 8. Box loss refers to the deviation between the predicted bounding box and the actual value. Object loss is a type of confidence loss that determines whether an object is present in the grid and calculates the confidence level of the model. At around Epoch 250, both loss values stabilised and decreased to very low levels. The graph shows that the model has undergone an effective learning process, improving its ability to accurately predict object location and presence. This ablation study analysed the effects of each optimised modification on the accuracy, speed, and energy efficiency of LPG cylinder surface defect detection. As a result of the ablation experiments, it was found that the Ghost and ECA modifications provide positive effects on both accuracy and energy efficiency, while the P34 structure improves overall performance. All results obtained from the ablation experiments are shown in Figure 9.  
Figure 8: GLDD-YOLOv5 training box_loss and object_loss graph.
###### Abstract  
The comparison of tissues analyzed by LA-ICP-MS is challenging in many aspects, both medical and mathematical. The concept of distinguishing regions of interest (ROIs) was proposed in the literature, allowing for data reduction and targeted comparative analysis. ROIs can be drawn before any analysis, by indicating the anatomical parts of tissue, or after the first step of analysis, by using elemental distribution maps and characteristic regions of enrichment in selected elements. A simple method for identifying different regions, without the manual extraction of image fragments, is highly needed in biological experiments, where large groups of individuals (with samples taken from each of them) is very common. In the present study, two ROIs were distinguished: (1) tissue-rich in fat (and tissue-poor in water); and (2) tissue-rich in water (and tissue-poor in fat). ROIs were extracted mathematically, using an algorithm based on the relationship between \({}^{13}\)C and \({}^{23}\)Na signal intensities. A cut-off point was indicated in the point of the simultaneous decrease in \({}^{13}\)C and increase in \({}^{23}\)Na signal intensity. Separate analyses of chemically different ROIs allow for targeted comparison, which is a great advantage of laser ablation over liquid introductions to ICP-MS. In the present experiment, tissues were provided from animals with implanted prostate cancer cells as well as supplemented with mineral compounds particularly important both for prostate gland functions (Zn and Se) and neoplastic processes (Ca, Fe, and Cu). One of the goals was to try to determine whether dietary supplementation qualitatively and quantitatively affects the mineral composition of the prostate gland.  
laser ablation; ICP-MS; ROI; prostate gland; prostate cancer +
Footnote †: journal: Applied Sciences
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 4 Experiments  
This section investigates the changes in loss when certain input values are excluded from the learning process.  
Table 2 compares loss values by sequentially removing each feature used in training. The lowest loss occurred when \(x\), which had the lowest correlation with the level-set value,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Feature** & **Symbolic Meaning** \\ \hline \(x\) & \(X\)-position of particle \\ \(y\) & \(Y\)-position of particle \\ \(p\) & pressure \\ \(\rho\) & density \\ \(N\) & number of neighboring particles \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features used in training data.  
Figure 4: Surface tracking structure of the LSTM model (top: previous algorithm, bottom: LSTM model). It is used for prediction after calculation with previous algorithm.  
was removed from the input features. Although the model trained without \(x\) showed the smallest loss, it was determined that the difference between validation loss and training loss was not significantly meaningful. Furthermore, it cannot be assured that excluding \(x\) always results in the smallest loss across various scenes when calculating validation loss, so the paper opts to train the model using all features. The most significant change in loss occurred when density was removed from the input features. Based on these results, it was conjectured that density might be an essential element that must be included in learning for level-set inference. \"None\" in Table 2 refers to the results when all features were used as training data.  
During the model training process, the decrease in training loss is an important indicator for monitoring progress. However, to evaluate the model's performance, it is necessary to examine its generalizability through the validation loss and check for overfitting issues. Through heat map analysis, the correlation between level-set values and input features was examined, and the theoretical influence on training was experimentally verified (see Figure 3). Although the experiments were set for 100,000 epochs, the graph shows the range of 8000 to 10,000 epochs, making the visual differences quite apparent (see Figure 5). Table 2 presents the final loss values obtained by preventing an increase in loss through early stopping. Therefore, the loss values in Table 2 can have different epoch numbers.  
From the graph concerning validation and training loss, we can confirm that among all input features, density is the most critical factor for inferring fluid isolines (see purple line in Figure 5). Density varies with the distribution of fluid particles, and to represent fluid isolines, it is essential to determine the interior/exterior of the fluid based on the level set. Density serves as a key metric for dividing these criteria, and consequently, during the training process, the loss for density was the greatest. Compared to other input features, the loss variation for density showed a significant difference, which is clearly illustrated in the chart (see Figure 5).
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 3 Simulation Results and Discussion
### Comparison of Methods I, II, and III

Table 10: Validation and testing accuracy of Method II across various sampling rates. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resampling** & **Samples per Cycles** & **Validation Accuracy (\%)** & **Testing Accuracy (\%)** \\ \hline Unnecessary & 4 & 100.00 & 91.67 \\ \hline \multirow{4}{*}{Required} & 16 & 99.12 & 94.57 \\ \cline{2-4}  & 24 & 99.12 & 92.00 \\ \cline{1-1} \cline{2-4}  & 144 & 99.24 & 100.00 \\ \cline{1-1} \cline{2-4}  & 188.5 & 99.27 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Validation and testing accuracy of Method III across various sampling rates. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Method I**} & \multicolumn{2}{c}{**Method II**} & \multicolumn{2}{c}{**Method III**} \\ \hline
**Dataset** & **Accuracy (\%)** & **Loss** & **Accuracy (\%)** & **Loss** & **Accuracy (\%)** & **Loss** \\ \hline Training & 99.23 & 0.02395 & 99.53 & 0.02359 & 99.54 & 0.02326 \\ \hline Validation & 98.34 & 0.07797 & 100.00 & 0.00435 & 100.00 & 0.00467 \\ \hline Testing & 73.14 & - & 87.04 & - & 88.88 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of Methods I, II, and III. The testing accuracy of Methods II and III for various transmission line configurations is presented in Table 12. The testing accuracy for three- or four-terminal transmission lines remains notably low, regardless of whether the analysis includes all recorded events or only data from individual ends. Despite experimenting with various configurations, no improvement in validation or testing accuracy is observed. This suggests that our current consideration of different transmission impedances, fault distances, and fault inception angles may not adequately capture the complexities of potential faults in transmission lines. A thorough gap analysis is essential to identify factors or conditions present in real-world data that are absent in our simulations. Therefore, we need more comprehensive historical data to better simulate real-world fault conditions. Moreover, iteratively refining the model and simulation parameters based on real-world data feedback can progressively diminish the discrepancy between simulated and actual conditions. Overall, the low testing accuracy underscores the critical need for more comprehensive historical data to better mimic real-world fault conditions and to fully leverage the capabilities of big data for enhanced model performance.