esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Physical Layer of the Channel Model  
In the physical layer, we denote the transmitted signal during the \(t\)-th time slot as \(X[t]\), while the corresponding received signal at the receiver is denoted as \(Y[t]\). Let \(h[t]\) denote the channel coefficient. The relationship between the transmitted and received signals is expressed as  
\[Y[t]=h[t]X[t]+Z[t]. \tag{1}\]  
Herein, \(Z[t]\) denotes an additive white Gaussian noise process with a noise spectral density of \(N_{0}\). Meanwhile, \(g[t]=|h[t]|^{2}\) denotes the channel gain.  
In practical systems with limited transmit power, the deep fading phenomena inherent to wireless channels pose significant challenges for achieving the low latency and high reliability requirements of URLLC. To address these challenges, we propose the use of parallel channels to ensure more reliable transmission. For the convenience of our theoretical analysis, we adopt a transmission scheme that selects the best among these parallel channels. However, to achieve hard delay constraints, it is necessary to employ parallel channels to enhance reliability.  
Let \(M\) denote the number of parallel channels, with the channel gain denoted as \(\{g_{1}[t],g_{2}[t],\ldots,g_{M}[t]\}\). We assume that these parallel channels follow an independent and identically distributed (\(i.i.d.\)) Rayleigh distribution over time. That is, the probability density function (\(p.d.f.\)) of the channel gain is \(f_{g}(x)=\frac{x}{\sigma^{2}}e^{-\frac{x^{2}}{2\sigma^{2}}}\). Furthermore, the cumulative distribution function (\(c.d.f.\)) of the channel gain is denoted as \(f_{G}(x)\).  
In the interest of analytical representation, we turn to utilize the maximal channel gain, expressed as  
\[g[t]=\max\{g_{1}[t],g_{2}[t],...,g_{M}[t]\}. \tag{2}\]  
The \(c.d.f.\) corresponding to this maximal channel gain \(g[t]\) is given by  
Figure 2: System model.  
\[G_{max}(x) =\Pr\{\max\{g_{1}[t],g_{2}[t],...,g_{M}[t]\}\leq x\} \tag{3}\] \[=\prod_{i=1}^{M}\Pr\{g_{i}\leq x\}=f_{G}(x)^{M}.\]  
Subsequently, \(p.d.f.\) associated with the maximal channel gain \(g[t]\) is derived as  
\[f(x)=Mf_{G}(x)^{M-1}f_{g}(x). \tag{4}\]  
It is worth noting that our framework remains accommodating to diverse channel distribution and multiplexing schemes. The emphasis on maximal channel gain is strictly a matter of analytical convenience. The system model we propose is suitable for various wireless communication systems that require meeting hard delay constraints, where factors like interference and mobility can be normalized into the channel gain. Our proposed approach is applicable to different channel models.
## 2 Materials and Methods  
### Materials  
In this work, all starting materials for the preparation of Y2O3:Eu3+ and its nanosol were used without further purification, yttrium oxide (Samchun Chem., Seoul, Korea, 99.99%) and europium oxide (Sigma-Aldrich, Aldrich, MN, USA, 99.99%). HNO3 (Daejung Chem., Shiheung, Korea, 60%) was used as the dissolution solvent. Distilled water was used throughout the experiments. Acrylate copolymer (BYK Co., Wesel, Germany) was used as the dispersing agent. Mica flake (MF, CQV Co., Seongjung-Ro, Korea, particle size: 9-45 um) or TiO2 coated on mica flake (TMF, CQV Co., Seongjung-Ro, Korea, particle size:5-35 \(\upmu\)m) powders were supplied from CQV Co. Especially, TMF consists of the mica flake coated and covered completely by rutile phase TiO\({}_{2}\) along with 50-70 nm thickness.
# Tensor-Based Algorithms for Image Classification
## Appendix B Interpretation of ARR as ALS Ridge Regression  
The following reasoning will elucidate the relation between ARR, ridge regression, and kernel-based MANDy. We only outline the rough idea without concrete proofs. Let \(R_{\mu}\) denote the retraction operator, see [28], consisting of the fixed TT cores \(\boldsymbol{\Xi}^{(1)},\ldots,\boldsymbol{\Xi}^{(\mu-1)}\) and \(\boldsymbol{\Xi}^{(\mu+1)},\ldots,\boldsymbol{\Xi}^{(p)}\) of the solution \(\boldsymbol{\Xi}\) at any iteration step of Algorithm 2. Furthermore, assume that \(\boldsymbol{\Xi}^{(1)},\ldots,\boldsymbol{\Xi}^{(\mu-1)}\) are left- and \(\boldsymbol{\Xi}^{(\mu+1)},\ldots,\boldsymbol{\Xi}^{(p)}\) right-orthonormal. In Lines 8 and 13 of Algorithm 2, we consider the system (with a slight abuse of notation)  
\[y=M_{\mu}x=\left(\boldsymbol{\Psi}_{X}^{\top}\cdot R_{\mu}\right)x.\]  
The application of a truncated SVD to the matricization of \(\boldsymbol{\Psi}_{X}^{\top}\cdot R_{\mu}\) (as done in Algorithm 2) is then similar to a regularization in the form of  
\[\min_{x}\left\{\left\|y-M_{\mu}x\right\|_{2}^{2}+\varepsilon\left\|x\right\|_ {2}^{2}\right\} \tag{10}\]  
with appropriate regularization parameter \(\varepsilon\), i.e., \(x\approx M_{\mu}^{+}y\) for both approaches, see [17; 30]. The formulation (10) is known as [PERSON]'s smoothing functional, ridge regression, or \(\ell^{2}\) regularization (which, of course, could also directly be applied in Algorithm 2). The solution of (10) is also the solution of the regularized normal equation  
\[M_{\mu}^{\top}y=\left(M_{\mu}^{\top}M_{\mu}+\varepsilon\mathrm{Id}\right)x,\]  
see, e.g., [31]. As \(R_{\mu}^{\top}R_{\mu}=\mathrm{Id}\), it follows that  
\[\left(R_{\mu}^{\top}\boldsymbol{\Psi}_{X}\right)y=\left(R_{\mu}^{\top}\left( \boldsymbol{\Psi}_{X}\boldsymbol{\Psi}_{X}^{\top}+\varepsilon\mathrm{Id} \right)R_{\mu}\right)x.\]  
In fact, this is a subproblem corresponding to the application of ALS [28] to the tensor-based system  
\[\boldsymbol{\Psi}_{X}y=\left(\boldsymbol{\Psi}_{X}\boldsymbol{\Psi}_{X}^{\top }+\varepsilon\mathrm{Id}\right)\boldsymbol{\Xi}. \tag{11}\]  
Note that all requirements for the application of ALS are satisfied since \(\boldsymbol{\Psi}_{X}\boldsymbol{\Psi}_{X}^{\top}+\varepsilon\mathrm{Id}\) is a symmetric positive definite tensor operator and \(R_{\mu}\) is orthonormal. The system of linear equations given in (11) is then equivalent to the minimization problem  
\[\min_{\boldsymbol{\Xi}}\left\{\left\|y-\boldsymbol{\Psi}_{X}^{\top}\boldsymbol {\Xi}\right\|_{2}^{2}+\varepsilon\left\|\boldsymbol{\Xi}\right\|_{2}^{2} \right\}.\]  
For sufficiently small \(\varepsilon\), it holds that \(\boldsymbol{\Xi}\approx\boldsymbol{\Psi}_{X}^{+}y\), see [32], meaning Algorithm 2 computes an approximation of the coefficient tensor resulting from the application of kernel-based MANDy, see Section 3.2.
# High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism
## 1 Introduction
[19] proposed a multi-branch feature fusion convolutional neural network (Multi-branch Feature Fusion CNN, MFF-CNN) for the automatic classification of main bearing cover parts; [PERSON] et al. [20] proposed improved Inception V3 [21] and Xception [22] for the recognition of threaded connection parts; and [PERSON] et al. [23] proposed a method based on migration component analysis for workpiece recognition. (4) Workpiece defect recognition and detection. Workpiece defect recognition refers to the process of automatically detecting and identifying defects on the surface or inside of a workpiece during the machining process. [PERSON] et al. [24] proposed a real-time defect detection method for metal workpieces, and [PERSON] et al. [25] proposed an improved deep learning model for surface-defect detection for rectangular pipe workpieces. Although the aforementioned workpiece recognition methods can solve problems related to complex internal textures and small feature differences to some extent, their research subjects are relatively simple and not effectively applicable to the recognition of high-frequency workpieces with complex intra-class diversity, small inter-class differences, and varying poses and lighting. Thus, there remains significant research space for high-frequency workpiece image recognition under complex lighting. To address the challenges of difficult recognition and low precision under complex lighting, this paper proposes a high-frequency workpiece image recognition model based on a hybrid attention mechanism (Hybrid Attention EfficientNet, HAEN). The model is based on the base network model Efficient-b0 [26]. Data enhancement was used to improve the model's robustness. Then, a lightweight convolutional attention module was designed to extract robust features from workpiece images under strong lighting, reducing the impact of lighting variations on the recognition results of high-frequency workpieces. Finally, using lightweight re-estimation attention modules [27], the network's feature expression of workpiece images was further enhanced. Experimental results on a laboratory-produced high-frequency workpiece dataset show that the proposed model can automatically focus on and extract features and is robust against strong lighting, demonstrating significant advantages in recognition precision over other methods.
## 3 Results and Discussion
(2019), [PERSON] (2018), and the authors observed the tendency of the substrate's porosity to influence the mortar's characteristics, which reinforces the theory of active pores. However, the substrate's porosity not only influences the mortar's adhesion to the substrate, but also determines the physical and mechanical behavior of the applied mortar. These characteristics depend on the mortar's porosity. From this, the porosity of the substrate and its capillary absorption capacity are determining factors in the actual porosity of the mortar. The mortar-substrate interaction creates a surface with finer pores close to the interface. This water absorption from the substrate during the curing process makes the mortar less porous and mechanically more resistant. Therefore, the Feature Importance analysis indicates that the substrate porosity is important for mortar open porosity. Consequently, in practical terms, the formulation of the mortar must consider the influence of the substrate where it will be applied. This way, it is possible to obtain more sustainable mortars while reducing the use of natural resources in their production. Concerning the R\({}^{2}\) obtained with the ML models, [PERSON] et al. (2018) used the same algorithms (SVM and RF) to predict the compressive strength of cement mortars, obtaining 0.420 for SVM and 0.944 for RF using the testing set. [PERSON] et al. (2016) also predicted the compressive strength of cementitious composite with fly ash. Still, they used different algorithms, such as artificial neural networks (ANN), and obtained 0.894 for R\({}^{2}\) in the testing set. In our case, we obtained R\({}^{2}\) values ranging from 0.8890 to 0.9088, indicating that the model accurately predicts the open porosity of cement-based industrial mortar based on the substrate, showing a good match between the performance of the two MLAs and the database size. However, the fact that R\({}^{2}\) is not a perfect fit means that the model does not explain 100% of the phenomenon. The factors that influence the porosity of the mortar, such as temperature, humidity, and application methods, were not considered within the features used. This fact can be considered a limitation of the prediction model.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 2 Materials and Methods
### Multi-Task Spectral Analysis Framework  
The proposed network demonstrates a robust and efficient model for oil spill identification and thickness prediction, as illustrated in Figure 2. The Wavelet Transform CNN (WaveConv) block is used for multi-scale feature extraction from spectral data, reducing model parameters and improving computational efficiency. The scalar Long Short-Term Memory (sLSTM) block captures sequential dependencies, enhancing the model's understanding of spectral feature positions. The Kolmogorov-Arnold Network-Enhanced Transformer (KET) block captures global data relationships, improving the model's non linear representation capability. By integrating the WaveConv block, sLSTM block, and KET block, the model effectively captures both local and global spectral features. Moreover, the regression and classification tasks are seamlessly integrated into the core structure of the model. Instead of using separate fully connected layers, the features output by the KET block are directly mapped linearly to the classification and regression outputs. This design ensures that the core feature extraction modules also serve as predictive components, eliminating the need for additional fully connected layers while maintaining efficiency and simplicity.
## 5 Tamper-Proofing Instruction
We can clearly see that the last protection range is divided into two parts, the start address is in the middle of the TPI (n\(-\)1) and the end address is in the middle of the TPI1. Thus, how do we deal with this situation? Here is our solution. First, we calculate a checksum1 from the start address of the last TPI to the end of key code segment and then calculate a checksum2 from the start of the key code segment to the end address of the last TPI. After that, using the sum of checksum1 and checksum2 as the checksum of the last TPI, either checksum1 or checksum2 is tampered with, and TPI (n\(-\)1) or TPI1 can find it. Figure 5 shows the TPI handlers. In our implementation, there are three different Handlers used to achieve the goal of TPI--to detect and respond to the tamper behavior when an adversary is tampering with a protected program. During the program creation phase, they will be placed at random locations. Furthermore, during execution time, they will not be closely executed one by one; in other words, the execution of TPI_b may not immediately follow TPI_a and TPI_c's execution may not immediately follow TPI_b, but the relative execution order of TPI_a, TPI_b and TPI_c is fixed. When TPI_c is actually executed, these three Handlers do play the role of guards. The 'nop' among the virtual instructions will be filled up with the exact decryption virtual instructions during program creation time. For each para_1 in every TPI Handler, the function of them are:  
* Load the pre_checksum that we calculated before the program execution and stored it in a register (v_ebp) after some math operations. * Load the start address of the protection range and push it into a register (v_eax). * Load the end address of the protection range and push it into a register (v_ecx); during the real execution of the program, the program will dynamically recalculate the checksum from the relative start and end address one more time. In our implementation, during the running of the program, the value of v_ebp will be recovered by a relative mathematical operation. In addition, this checksum is compared with the stored pre_checksum. If they are equal, no tampering occurs; otherwise, the program will take some necessary measures to prevent tampering. For example, use a MessageBox to alert the user that his program is being tampered with, or just terminate the execution of the program. It is worth noting here that there is a para_2 after every TPI Handler; we have designed para_2 to implement the centralized and chained dispatching models. That is to say, after executing the TPI handler, it will jump to the next handler, which will be executed to continue execution or jump back to one of the Dispatchers. In fact, every normal Handler has at least one parameter to determine that the executing handler will jump to the next Handler or Dispatcher. Figure 4: The organization of TPI-Ring. To make sure every sensitive area at least has three TPI Handlers protecting it.
## 2 Materials and Methods
### Multi-Object Detection  
MOT (Multi-Object Tracking) is based on object detection and object re-identification (ReID) [54; 55]. Three different MOT techniques, StrongSORT [54], ByteTrack [56; 57], and DeepSORT [58], were adopted and compared based on their tracking performance. DeepSORT was one of the original methods to use a deep learning model for MOT. It is usually selected because of its generalization and effectiveness [59]. Although its tracking paradigm was valuable, the performance of DeepSORT was not comparable due to its outmoded techniques. StrongSORT was developed using the fundamental elements of DeepSORT and advanced components. For instance, Faster R-CNN was applied in DeepSORT while YOLOX-X was chosen for StrongSORT. Also, a superior appearance feature extractor, BoT (Bottleneck Transformer) [60], was selected rather than simple CNN [54]. ByteTrack is a tracking method based on the tracking-by-detection paradigm. A simple and efficient data association method called BYTE was proposed. The vital difference between it and other tracking algorithms is that it does not simply remove the low-score detection results but associates every detection box [57]. By using the similarity between the detection box and the trajectories, the background can be removed from the low-score detection results while retaining the high-score detection results, and real objects (difficult samples such as occlusion and blurring) are removed, thus reducing missed detections and improving the tracking coherence [56]. The resulting MOT method with the best performance from this section is applied in Section 2.6 Alarm Identification.  
A widely used method for evaluating the performance and generalizability of a machine learning model is 5-fold cross-validation. The basic concept was to split the original dataset into five parts of equal size, of which four were used to train the model and one was used to validate it. After carrying out this process five times, the final assessment results were calculated by averaging the outcomes of the five performance evaluations.
## 6 Results  
We focus our attention on rotation invariance. The input scene images are presented in Figure 6A. The reference image is shown in Figure 6A-a. Figure 6A-b,A-c are slightly laterally shifted and rotated versions of the reference image, whereas Figure 6A-d is a false image. The energy is normalized for the four images. To test the approach under noisy conditions, significant noise is voluntarily added to the input image. The noise energy is four times bigger than the energy of the useful signal. We designed a binary phase-only filter by projecting the phase profile onto the closest phase level among \(\{0,\pi\}\). These kinds of filters can be implemented by a spatial light modulator if a programmable system for pattern recognition is required.  
The simulation results on Matlab show that rotation invariance is ensured. Figure 6B also shows a high optical efficiency and a good correlation peak sharpness. The optical efficiency is defined as the amount of input light that will be detected for the determination of the correlation function. It is quantitatively measured by the Horner efficiency [17,18]. The energies of the peaks associated with the rotated images are less tha  
Figure 6: (A) Input images (a) reference (b) and (c) rotated version of the correct image (d) false image (**B**) Response of the correlator for the four images in (**A**).  
the reference. The approach is sensitive to lateral shift and gives drastic results if the lateral shift is in the range of the image size. Thus, it is necessary, for instance, to use the Fourier transform of the input image to ensure shift invariance.  
The diffractive compressing element was quantized onto four phase levels: {0, \(\pi\)/2, \(\pi\), \(3\pi\)/2}. Quantization has been performed by a simple projection onto the closest phase level. Because of quantization we noticed a non-negligible energy loss and it is worth using optimization methods [15].
## 2 Results and Discussion
This IL was tested as an additive to the SPME desorption phase, as it was previously done for urine samples [9]. Figure 4 shows the electropherograms obtained after the analysis of rat brain homogenates purified and enriched with the newly invented IL-SPME assisted method. A shift in the migration times (MTs) of the studied compounds was observed, but the extraction yields of SPME in comparison to DLLME were improved two times for DA and 3.5 times for NA (Table 1). In the case of A and L-Tyr, the signal intensities were slightly higher, while, for 5-HT, SPME worked less efficiently than DLLME. Moreover, with IL-SPME, the determination of L-Trp was possible (Figure 4a, Table 1). The improvement in matrix component elimination was superior when SPME was used instead of DLLME (Figure 4b vs. Figure 3b). Although the signal from the IL added to the desorbent was relatively extensive, it did not interfere with the signals of the analytes. When comparing the isolation of NTs using SPE SCX with MeOH, MeOH with NH4 OH (pH 9.3), or SPE CSDAU, the IL-SPME-assisted procedure was found to be more effective, showing higher signal intensities of the analytes (Table 1). Moreover, it was possible to omit the derivatization step, which is often applied during the analysis of rat brain NTs [21], for three of our tested methods. Nevertheless, the isolation of HVA and VMA was only achieved when SPE SCX with 0.1% FA in ACN/MeOH as an eluent was carried out. Figure 4: Electropherogram of SPME for the isolation of NTs from (**a**) the brain sample spiked with nine NTs (each analyte at the concentration of 10 \(\upmu\)g/g); (**b**) the blank brain control sample. Legend and CE conditions as in Figure 2.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 2 Related Works
In [23], an adaptive Lagrange multiplier was estimated in the \(\rho\)-domain, which defines \(\rho\) as the percentage of zero coefficients among quantized transformed residuals. Furthermore, in [24], a Laplace distribution of transformed residuals was constructed to capture the RD statistical properties, enabling the derivation of the Lagrange multiplier based on RDO. All of the above provide significant improvements in RDO performance, yet the Lagrange multiplier may not be sufficient to fully capture the coding properties. [PERSON] et al. [25] built an adaptive parameter model for \(\lambda\) that considered the residual correlation across successive frames. [PERSON] et al. [26] conducted a comprehensive analysis and showed that the original Lagrange multiplier did not perform well for inter blocks, prompting them to propose and evaluate a novel approach for determining the \(\lambda\). However, the methods mentioned above are designed under the premise of the identical characteristics of the luma and chroma, i.e., the source signal of the chroma is treated in the same manner as that of the luma. In fact, this is not suitable for accurately describing the RD characteristics of chroma intra mode.