esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
In addition, to further quantitatively evaluate the performance of the proposed algorithm, the following two performance indicators, integrated absolute error (IAE) and integrated time absolute error (ITAE), are introduced:  
\[IAE_{i}=\int_{0}^{t}\lvert e_{i}(t)\rvert dt \tag{40}\]  
\[ITAE_{i}=\int_{0}^{t}t\lvert e_{i}(t)\rvert dt \tag{41}\]  
IAE and ITAE can reflect the control scheme's transient and steady-state tracking accuracy. According to Tables 2-5, under the FT-AFTC control scheme proposed in this article, the minimum IAE and ITAE can be obtained, thus achieving the highest transient and steady-state tracking accuracy. Figure 3: Curves of tracking error in Case 1. Figure 2: Curves of surge velocity, drift velocity, and yaw angular velocity in Case 1. Figure 4: Curves of thruster control input in Case 1. Figure 5: The actual position and heading angle curves in Case 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{ITAE_{x}}\) & \(\mathbf{ITAE_{y}}\) & \(\mathbf{ITAE_{y}}\) & \(\mathbf{ITAE_{u}}\) & \(\mathbf{ITAE_{v}}\) & \(\mathbf{ITAE_{r}}\) \\ \hline FT-AFTC & 1.087 & 1.099 & 0.289 & 0.750 & 2.190 & 1.173 \\ ABSMC & 4.298 & 4.189 & 0.437 & 5.128 & 5.711 & 5.790 \\ NFTC & 50.11 & 50.29 & 31.09 & 18.55 & 27.21 & 13.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ITAE of the three controllers in Case 1. Figure 6: Curves of surge velocity, drift velocity, and yaw angular velocity in Case 2. Figure 7: Curves of tracking error in Case 2.
## 3 Establishment of YOLOv5-Based Optimized Detection Model and Evaluation Metrics
### Efficient Channel Attention (ECA) Mechanism  
To address the challenges in weld images, such as small defect sizes and low contrast, the ECA mechanism is introduced to automatically adjust the weights of each channel,  
Figure 4: Network structure for EMA module.  
highlighting key weld defect features while suppressing irrelevant information. The overall structure of the ECA model is shown in Figure 5. It captures cross-channel interactions and nonlinear information through an adaptive selection of a convolutional kernel of size k, using a one-dimensional convolution instead of a fully connected layer. This approach reduces the number of parameters and computational complexity while maintaining the original dimensionality. Initially, a global average pooling operation is applied to the input feature map of size \(H\times W\times C\), aggregating the information of each channel into a single value, resulting in a 1 \(\times\) 1 \(\times\) C feature map with the same number of channels, without any dimensionality reduction. Then, local interactions are performed through a one-dimensional convolution along the channel dimension, where the kernel size \(k\) is adaptively adjusted based on the number of channels \(\varphi\)(C), as follows:  
\[k=\varphi(C)=\left|\frac{\log_{2}(C)}{\gamma}+\frac{b}{\gamma}\right|_{old} \tag{2}\]  
where \(\left\lvert\left.t\right\rvert_{old}\right.\) represents the nearest odd number of \(t\). In this paper, the adjustment factors \(\gamma\) and \(b\) are set to 2 and 1, respectively.  
This allows each channel to interact with the channels in its local neighborhood, capturing finer dependencies between channels. After the one-dimensional convolution, attention weights are obtained, representing the importance of each channel in the feature vector. Finally, the computed weights are remapped to the channels of the original feature map, and channel-wise multiplication is applied for weighting, enhancing useful channel features and suppressing irrelevant ones, resulting in a new feature map of size \(H\times W\)\(\times\)\(C\). Incorporating the ECA attention mechanism into the layer preceding the SPPF effectively distinguishes between background and defect target features, enhancing the ability to extract detail features related to defects while simultaneously optimizing the feature fusion process. Therefore, this method can highlight the features of defect regions and reduce interference caused by low contrast or background noise, especially demonstrating remarkable performance in detecting low-quality weld images.
## 3 Architecture and Problem Formulation
### System Model
#### 3.2.4 Network Battery Lifetime
The TBS index denoted by \(\delta_{k}\in\{0\dots 33\}\) is dependent on the selected modulation and coding scheme (MCS) from Table 8.6.1-1 provided in [38]. \(P_{o}\) is a user-specific value, defined as \(P_{o}=\ \beta_{k}[\Gamma_{th}+P_{n}]+\ [1-\beta_{k}]P_{max}\) which depends on the required SINR level \(\Gamma_{th}\). \(P_{max}\) is defined as the maximum allowed transmitting power with \(P_{n}\) being the noise power in each resource block. The expected battery lifetime for a direct communication MTCD node \(i\) within its time-to-transmit interval (TTI) can be defined as:  
\[L_{k}(t^{o})\ =\ \frac{E_{i}(t^{o})T_{k}}{E_{s}^{k}+\ TTI[P_{e}+\ \eta P_{UL}(c_{k}, \delta_{k})]}. \tag{14}\]
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 1 Introduction
### Exosomes in Dermatology
#### 1.4.2 Rose Stem Cell-Derived Exosomes  
More recently, it has been demonstrated that rose stem cell exosomes (RSCEs) harbor anti-inflammatory and regenerative properties. Research on plant exosomes is still relatively new. Plant-derived exosomes are similar in structure and function to animal exosomes and they are studied for drug delivery, cancer treatment, inflammatory diseases, and neurodegenerative disorders, offering a novel, cell-free, and sustainable approach to various conditions [34]. Rose stem cells release their exosomes into the conditioned media during callus culture. The size and shape closely resemble exosomes derived from human stem cells. RSCEs are collected by isolating and purifying the supernatant of RSC cultures. Their lipid membrane properties and size, ranging from 30 to 200 nanometers, are verified using Nanoparticle Tracking Analysis (NTA) and Transmission Electron Microscopy (TEM). RSCEs have been shown to enhance collagen production by human dermal fibroblasts by 40-120% in a dose-dependent manner and to increase cellular migration by over 20%. Additionally, RSCEs exhibit anti-inflammatory properties, reducing IL-6 production by macrophages by 50-60%, depending on concentration. Furthermore, RSCEs are internalized by melanocytes, resulting in a decreased melanin content, which indicates a potential whitening effect. The precise molecular underlying this function remains unclear, but it is likely to involve various molecules present within the RSCEs cargo. In summary, RSCEs contain miRNA and peptides with anti-inflammatory properties, promoting fibroblast proliferation, collagen production, and dose-dependent reduction in melanin accumulation [35].
## 2 Properties of Fine Granular Area
### Influences Regarding the Formation Process of FGA
#### 2.1.1 Influence of the Cycles to Failure  
Since the 19 th century the basis for design against failure has been linked to the S-N relationship discovered by [PERSON]. The classic S-N curve describes an increasing lifetime with decreasing stress amplitudes with a transition to a horizontal plateau. The stress amplitude at this transition has been assumed to be the fatigue limit, defined as the stress that can be endured infinitely or at least up to a defined limit number of cycles (\(2\times 10^{6}\) or \(10^{7}\)) without macroscopic damage. However, it is well established that for different metallic materials fatigue failures occur far beyond \(10^{7}\) cycles at amplitudes below the conventional HCF limit. Those fatigue failures beyond \(10^{7}\) loading cycles are defined as VHCF. At load levels below this conventional HCF fatigue limit a distinction between two types of materials with different fatigue behaviors is described by [PERSON] [19]. He describes the different thresholds and fatigue mechanisms for type I materials, namely pure single-phase metallic materials without internal defects and type II materials such as steels or cast materials with internal defects such as inclusions or pores. For type I materials, he differentiates four different loading regimes. The first and the second range are defined with loading conditions above or at the strain localization threshold and correspond to fatigue failures in the HCF-regime, with cracks initiating from the surface. For the VHCF-regime two regimes are described. Regime III in which the load is below the persistent slip band (PSB)-threshold but above the irreversibility threshold, resulting in the significantly longer but finite fatigue life, since the cyclic slip is more or less homogenous and not localized but still partially irreversible. The regime IV with loads below the irreversibility threshold, leading to non-damaging slip or negligible slip irreversibility and infinite fatigue life for all practical purposes. However, recently [PERSON] and [PERSON] [20; 21] were able to show the formation of non-propagating internal cracks (length 20 \(\upmu\)m) in polycrystalline high-purity copper in the VHCF-regime. For type II materials containing defects such as inclusions or pores the crack initiation site changes from surface to inner defects [19]. In addition to the initiation at internal defects and the formation of the fish-eye, which can already be observed in the HCF-regime, a change in the microstructure in the immediate vicinity of the defect (non-metallic inclusions for high-strength steels) is commonly observed (for more than \(10^{6}\) cycles [22]) in the VHCF-regime [9; 11; 23]. As mentioned above this VHCF specific fracture surface area consists of a three-dimensional fine-grained layer and is therefore mostly referred to as FGA [12] which will be used from now on. The fatigue behavior of different materials will be discussed in more detail in the following chapter.
## 2 Methodology
### Haralick Features  
Haralick descriptors were derived from the co-occurrence matrix [30]. This matrix allowed us to verify the transition between two pixels in gray levels, \(u\) and \(v\), considering a distance measure \(\delta\) and an arbitrary angle \(\theta\). It is important to highlight that in the histology area, there have been studies exploring the interpixel distance variation GLCM textural parameter approach, such as the work proposed by [76]. This study has shown that Haralick descriptors present pattern differences between the cell nuclei for one interpixel distance. Moreover, the one interpixel distance allows the definition of fine-grained texture and regularity and demands reduced computational effort, especially when dealing with large images. Thus, in the proposed framework, the parameter values of distance \(\delta=1\) and direction \(\theta=0^{\circ},\theta=45^{\circ},\theta=90^{\circ}\) and \(\theta=135^{\circ}\)[13; 62] were applied. Considering the co-occurrence matrices and the widely known descriptors from [30], the features investigated here were: (1) Angular Second Moment; (2) Contrast; (3) Correlation; (4) Sum of Squares; (5) Inverse Difference Moment; (6) Sum Average; (7) Sum Variance; (8) Sum Entropy; (9) Entropy; (10) Difference Variance; (11) Difference Entropy; (12) and (13) are Information Measures of Correlation; and (14) Maximal Correlation Coefficient. For the final 14 descriptors, the averages of the descriptors obtained in the four matrices were considered.
## 4 Results
### Compression Ratio Comparison to Widely Used Algorithms  
The results of the CR comparison to widely used algorithms are shown in Table 3. SHT, LZ, gzip, and bzip 2 were selected as widely used algorithms for compression ratio comparisons. Fifteen data were randomly chosen for assessment. In cases where the data origin was not explicit in the literature, disease marker classifications were shown.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solutions** & **CR** & **Used Data** & **CR Average \(\pm\) SD** \\ \hline  & 2.22 & SRX2935589 \\  & 2.22 & SRX2935588 \\  & 2.22 & SRX2935587 \\  & 2.22 & SRX2935586 \\  & 2.22 & SRX2935856 \\  & 2.22 & SRX2528043 \\  & 2.21 & SRX2528042 \\ SHT & 2.21 & SRX2528041 \\  & 2.21 & SRX2528040 \\  & 3.44 & Cholerae \\  & 3.34 & Abscessus \\  & 3.34 & S. cerevisiae \\  & 3.27 & N. crassa \\  & 4.45 & Ch2Z \\ \hline  & 3.38 & SRR2916693 \\  & 2.92 & SRR2949468 \\  & 2.35 & SRR3211986 \\  & 2.22 & ERR799531 \\  & 3.39 & SRR3190692 \\  & 5.59 & ERR385912 \\  & 3.85 & ERR386131 \\ Gzip & 2.71 & SRR304509 \\  & 3.15 & ERR174310 \\  & 4.24 & ERR194147 \\  & 3.29 & Cholerae \\  & 3.31 & Abscessus \\  & 3.1 & S. cerevisiae \\  & 3.12 & N. crassa \\  & 4.57 & Ch2Z \\ \hline  & 11.22 & SRX2528041 \\  & 11.25 & SRX2528040 \\  & 11.18 & SRX2528039 \\  & 10.44 & ERX1005095 \\  & 11 & ERX1600594 \\  & 11.38 & ERX16060593 \\  & 13.63 & SRX2776809 \\ BAQALC & 13.36 & SRX2776808 \\  & 12.78 & SRX2776807 \\  & 9.85 & SRX293587 \\  & 10.17 & SRX2935586 \\  & 10.26 & SRX2935855 \\  & 17.08 & SRX2888193 \\  & 17.06 & SRX2388192 \\  & 16.94 & SRX2388191 \\ \hline  & 3.24 & SRX2935588 \\  & 3.23 & SRX293587 \\  & 3.24 & SRX2935586 \\  & 3.21 & SRX2528042 \\  & 2.88 & SRX2528041 \\ LZW & 3.21 & SRX2528040 \\  & 3.21 & SRX2528040 \\  & 3.2 & SRX2528039 \\  & 3.2 & ERX1605097 \\  & 3.38 & ERX1600596 \\  & 3.35 & ERX16005956 \\  & 3.34 & ERX1600594 \\  & 3.32 & ERX1605093 \\  & 3.31 & SRX2288195 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CR Comparison between BAQALC and Widely Used Algorithms SHT, LZW, Gzip, and Bzip 2.  
Comparison between BAQALC and widely used algorithm results showed that the average and SD CRs were 2.67 \(\pm\) 0.72, 3.24 \(\pm\) 0.12, 3.41 \(\pm\) 0.87, 3.99 \(\pm\) 1.15, and 12.51 \(\pm\) 2.58, for SHT, LZW, gzip, bzip 2, and BAQALC, respectively. The proposed BAQALC algorithm showed the highest CR performance (average 12.52). However, BAQALC's stability was the lowest (SD 2.58), whereas LZW's stability was the highest (SD 0.12).
# A Cell Counting Framework Based on Random Forest and Density Map
## 5 Conclusions  
In cell counting, the cell occlusions and scarce training data make the counting task difficult. To this end, we proposed a cell counting framework based on random forest (RF) and density map. In the proposed cell counting framework (CCF), the training data preparation and the detection framework are combined to provide an accurate and robust counting result. The training data preparation aims to make the cells can be better detected even when overlapping and reduce the computational overhead. The detection framework contains multiple RFs and each RF can predict a probability map where the cells can be detected robustly by Hessian matrix. To decrease the overall count error, we averaged all detection maps such that the false detection disapproved by most RFs can be suppressed. In this paper, we assigned 10 base RFs to the proposed CCF and proved that CCF outperforms the base RF in terms of MAE and MSE. The comparisons between CCF and some state-of-the-art methods show that CCF can achieve higher performance, especially when the training data are small. However, the cell images with annotations are usually difficult to collect. Therefore, the proposed CCF can serve as an automatic counting tool in cell image analysis. From training to testing, it does not require extra high hardware resources.  
Figure 15: The Evaluation for three datasets. (**a**) The evaluation for VGG cells. (**b**) The evaluation for MBM cells. (**c**) The evaluation for Adipocyte cells.  
In this paper, the CCF is trained by handcrafted features that are low-level. Further, the receptive field of the extracted feature vector is also small. These factors limit the performance improvement. In future studies, we will try to use some classic pre-trained CNN models as the feature extractor to enhance the feature representation. At the last step of CCF, the final detection result is the average of all detection results, which means each base RF contributes equally to the final count result. To make the count result more accurate, we will explore how to re-weight base RFs to perform a better count. Furthermore, we will also focus on speeding up the training and testing processes by parallelization.  
Conceptualization, N.J. and F.Y.; methodology, investigation and resources, N.J.; writing--original draft preparation and software, N.J.; formal analysis, N.J., writing--review and editing, N.J. and F.Y.; validation, data curation and visualization, N.J.; supervision, project administration, F.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Mechanisms of Rubber Toughening  
The mechanisms of rubber toughening in epoxy resins are diverse, often involving multiple mechanisms working in conjunction. The two primary mechanisms are shear yielding theory and cavitation theory.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 4 Experiment Results and Discussions
### Experiments of Image Reconstruction
However, the fluctuation of background gray values, mainly caused by the signal noise, increases compared to the area near the center, with an improved gray value standard deviation of 2.16. The reason is that the density of sampling points gradually decreases from the center to the outside. Since the PMT detection duration can be shorter than 1 microsecond, if a higher sampling frequency such as \(f_{0}=10\) MHz is employed, this problem will be mitigated, and a better noise level can be obtained. Apparently, in contrast to the nearest-neighbor interpolation method, our method has a better performance. In conclusion, the above experimental results show that with the proposed system calibration and image reconstruction methods, a complete defect image with low noise and low distortion can be obtained, thus improving the detection accuracy and reliability of the system. Figure 8: (**a**) The maximum straightness errors of scratches in the adjusted image with comparison to those in the original image; (**b**) The distance errors of dig pairs in the adjusted image.
# OTFS-Based Handover Triggering in UAV Networks
## 1 Introduction
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Definition** \\ \hline \(h_{i_{q}}\) & Complex amplitude of channel path \(i\) in the coherence period \(q\) \\ \hline Q & Number of coherence periods \\ \hline \(T\) & OFDM symbol duration \\ \hline \(M\) & Number of subcarriers in the OTFS symbol \\ \hline \(N\) & Number of Doppler taps in the OTFS symbol \\ \hline \(\Delta f\) & Subcarrier spacing \\ \hline \(n^{\prime}T\) & Duration of each coherence period \\ \hline \(NT\) & OTFS symbol duration \\ \hline \(P_{q}\) & Number of paths) in the \(q\)-th coherence period \\ \hline \(\alpha_{i_{q}}\) & Delay spread of channel path \(i\) in the coherence period \(q\) \\ \hline \(\beta_{i_{q}}\) & Doppler shift in channel path \(i\) in the coherence period \(q\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols. \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & \multicolumn{1}{c}{**Definition**} \\ \hline \(h_{QCD}\) & DD CIR of the \(q\)-th coherence period \\ \hline \(l\) & Normalized delay dimension in the DD grid \\ \hline \(k\) & Normalized Doppler dimension in the DD grid \\ \hline \(\beta_{\max}\) & Maximum Doppler frequency of the channel \\ \hline \(\alpha_{\max}\) & Maximum delay spread of the channel \\ \hline \(f\) & Operating frequency \\ \hline \(v\) & Speed of the object \\ \hline \(c\) & Speed of light \\ \hline \(s_{DD}\) & Tx DD QAM symbols \\ \hline \(\text{F}_{N}\) & N-point DFT matrix \\ \hline \(\text{F}_{M}\) & M-point DFT matrix \\ \hline \(s_{FT}\) & Tx FT symbols \\ \hline \(x_{DT}\) & Tx continuous DT signal \\ \hline \(b_{T\text{x}}\) & Tx windowing function \\ \hline \(\text{I}_{M}\) & Identity matrix \\ \hline \(g_{DT}\) & AWGN in DT domain \\ \hline \(r_{DT}\) & Rx continuous DT signal \\ \hline \(\text{T}^{\alpha_{i_{q}}}\) & Permutation matrix related \(\alpha_{i_{q}}\) \\ \hline \(\Sigma^{\beta_{i_{q}}}\) & Diagonal matrix containing the effect of \(\beta_{i_{q}}\) \\ \hline \(y_{FT}\) & Rx FT symbols \\ \hline \(H_{FT}\) & FT CIR \\ \hline \(b_{Rx}\) & Rx windowing function \\ \hline \(y_{DD}\) & Rx DD QAM symbols \\ \hline \(G_{DD}\) & AWGN in DD domain \\ \hline \(HO\) & Handover \\ \hline \(RSS\) & Received signal strength \\ \hline TTT & Time to trigger \\ \hline \(P_{th}\) & Threshold power \\ \hline \(NSym_{OFDM}\) & Number of OFDM symbols \\ \hline \(NSym_{th}\) & Number of threshold symbols \\ \hline \(NSym_{OTFS}\) & Number of OTFS symbols \\ \hline \(MSE\) & Minimum square error \\ \hline \(\psi_{\hat{R}}\) & Spectral efficiency \\ \hline \(P_{T\text{x}}\) & Tx power \\ \hline \(\sigma_{\hat{S}}^{2}\) & AWGN power \\ \hline \(\sigma_{h}^{2}\) & Channel power \\ \hline \(O_{HO}\) & Handover overhead \\ \hline \(\Psi\) & Average throughput \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussion
### Microstructural Observations  
The microstructure of the surface of the composites from compositions K, 80K20 MK, 80K20 MKm and 80K20 MKp is presented in Figure 11. While keeping the same liquid-to-solid ratio, the substitution of Koranel by metakaolinite led to a significant reduction in cracks and composites with smoother surfaces. The addition of both 2-methyl-2,4 pentanediol and polyethylene glycol, however, seemed to increase the void proportion at the surface. This is consistent with a previous study on the use of 2-methyl-2,4 pentanediol, which was observed to lead to an increased porosity by acting as air entrainer with drying shrinkage reduction [23], and may explain why the surfactants do not have a significant effect on the flexural strength and elastic modulus (Figure 10) of the composites. However, the appearance of voids in the samples with surfactants did not significantly influence the density, which was always in the range of (2.00 \(\pm\) 0.05) g/cm\({}^{3}\).  
The microstructure of the cross section of composite 80K20 MK shows alternating layers of matrix and carbon fiber (Figure 12). The matrix is not perfectly adhering to the carbon fiber and is not really penetrating in the fiber bundles. This is ascribed to the fiber's hydrophobic properties, which makes them incompatible with water-based systems [39]. However, methods including plasma treatment, silica or calcite deposition on carbon fiber, were suggested to improve the wettability and enhance the adhesion between the fiber and cementitious matrices [39; 40; 41; 42]. Fiber surface chemistry modification and nanomaterials'incorporation in the binder were also observed to enhance the fiber/matrix interface in alkali-activated composites [43; 44; 45; 46]. For instance, the addition of 2 wt.% nano titanium dioxide in alkali-activated fly ash reinforced by micro carbon fiber was observed to increase the fracture toughness by 29% in comparison to the control sample, due to possible filling behavior and enhancement of the carbon fiber-matrix interfacial zone, which induced a hindrance to crack formation [46]. However, actions to improve fiber-matrix interactions are also costly, so need to be analyzed considering the possible improvement in properties and the composite application. Since in this paper the focus is on proving that the matrix can be used for textile-reinforced composites, no effort was exerted to improve the fiber-matrix interaction. Anyway, it is worth reminding that the durability of textile-reinforced alkali-activated composites do not only depend on their resistance to the external environment, but also on the chemical interaction between the fibers and binder within the composite. For instance, due to the alkaline environment of the binder, alkali-activated composites reinforced with glass and basalt fibers were found to have significant reductions in tensile strength their over time, while the strengths of those reinforced with carbon fibers were sufficiently stable [26; 47].  
Figure 11: Microstructural surface of indicated composites (K), (80K20 MK), (80K20 MKm) and (80K20 MKp) (cf. Table 3).