esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Linear-Stability Approach  
Steady-state CW solutions with common propagation constant \(Q\) are looked for as:  
\[u_{1}=A_{1}\exp(iQ\xi),\quad u_{2}=A_{2}\exp(iQ\xi), \tag{7}\]  
where \(A_{1}\), \(A_{2}\) are real amplitudes, which determine the total intensity and asymmetry ratio:  
\[P=A_{1}^{2}+A_{2}^{2},\ \eta=A_{1}/A_{2}. \tag{8}\]  
The substitution of Ansatz (7) in Equations (4) and (5) yields an expression for propagation constant \(Q\) and a relation between \(\eta\) and the phase velocity mismatch, \(\chi\):  
\[Q =\frac{P(\Gamma+\eta^{2})}{2(1+\eta^{2})}+\frac{\kappa(\eta^{2}+ 1)}{2\eta}, \tag{9}\] \[\chi =\frac{P(\Gamma-\eta^{2})}{2(1+\eta^{2})}+\frac{\kappa(\eta^{2}- 1)}{2\eta}, \tag{10}\]  
Next, we add infinitesimal perturbations \(a_{j}\) to the CW solutions, as:  
\[u_{1} =[A_{1}+a_{1}]\exp(iQ\xi), \tag{11}\] \[u_{2} =[A_{2}+a_{2}]\exp(iQ\xi).\]  
Substituting Expression (11) into Equations (4) and (5), we arrive at linearized equations for the complex perturbations:  
\[i\frac{\partial a_{1}}{\partial\xi}+\frac{\sigma_{1}}{2}\frac{ \partial^{2}a_{1}}{\partial\tau^{2}}+\eta^{2}\frac{P}{1+\eta^{2}}(a_{1}+a_{1} ^{*})+\kappa a_{2}-\kappa\eta^{-1}a_{1}=0, \tag{12}\] \[i\frac{\partial a_{2}}{\partial\xi}+i\rho\frac{\partial a_{2}}{ \partial\tau}+\alpha\frac{\sigma_{1}}{2}\frac{\partial^{2}a_{2}}{\partial\tau ^{2}}+\Gamma\frac{P}{1+\eta^{2}}(a_{2}+a_{2}^{*})+\kappa a_{1}-\kappa\eta a_{ 2}=0. \tag{13}\]  
Solutions to Equations (12) and (13) are looked for, in the usual form, as:  
\[a_{1} =F_{1}e^{i(K\xi-\Omega\tau)}+G_{1}e^{-i(K\xi-\Omega\tau)}, \tag{14}\] \[a_{2} =F_{2}e^{i(K\xi-\Omega\tau)}+G_{2}e^{-i(K\xi-\Omega\tau)}, \tag{15}\]  
where \(K\) and \(\Omega\) are a (generally, complex) wave number and an arbitrary frequency of the perturbation. A set of linear coupled equations for perturbation amplitudes \(F_{j}\) and \(G_{j}\) are derived by substituting Expressions (14) and (15) in Equations (12) and (13):  
\[\mathbf{M}\times(F_{1},F_{2},G_{1},G_{2})^{T}=0, \tag{16}\]  
where \(\mathbf{M}\) is a \(4\times 4\) matrix, whose elements are written in Appendix A. A nontrivial solution exists under condition \(\det\mathbf{M}=0\). Straightforward algebraic manipulations transform the latter condition into a dispersion relation, in the form of a quartic equation for \(K\) as a function of \(\Omega\):  
\[K^{4}-aK^{3}+bK^{2}+cK+d=0. \tag{17}\]  
Rather cumbersome expressions for coefficients \((a,b,c,d)\) are also given in Appendix A. The MI growth rate (gain), defined here for the amplitude of the waves (rather than for the power), is determined by the largest absolute value of the imaginary part of the wave number:  
\[G=\left\{\left|\text{Im}(K)\right|\right\}_{\text{max}}. \tag{18}\]
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 4 Conclusions  
This paper proposes and studies an advanced modulation format Apol-CRZ-FSK with high nonlinear resistance, to achieve a solution for 4 \(\times\) 100 Gbps computing optical networks. Simulation results indicate that when the input fiber power is low and the impact of nonlinear effects is negligible, the Apol-CRZ-FSK modulation format does not demonstrate significant performance advantages in the 4 \(\times\) 100 Gbps system. However, as the optical power gradually increases and the transmission distance extends, the Apol-CRZ-FSK signal exhibits better nonlinear resistance when nonlinear effects become the primary factor affecting system performance. The longest single-span transmission distance of the Apol-CRZ-FSK signal is 16 km longer than that of the CRZ-FSK and 23 km longer than that of the DQPSK modulation formats. At a transmission distance of 120 km, when the launch fiber power is 20 dBm, the Q-factors of the four channels for the 4 \(\times\) 100 Gbps Apol-CRZ-FSK signal are 2.9 dB, 0.8 dB, 4.5 dB, and 2.5 dB higher than those of the CRZ-FSK signal, respectively; and 8.8 dB, 6.9 dB, 9.4 dB, and 8.2 dB higher than those of the DQPSK signal, respectively. Under multi-span transmission conditions, the furthest transmission distance of the 4 \(\times\) 100 Gbps Apol-CRZ-FSK modulation signal is 980 km and 480 km farther than that of the CRZ-FSK and DQPSK modulation signal, respectively. In summary, the Apol-CRZ-FSK modulation signal has certain transmission performance advantages. The demodulation on the receiving side of the Apol-CRZ-FSK signal does not require complex coherent demodulation, effectively reducing system costs and saving energy with direct detection. Furthermore, in response to the transmission demands and signal perception recognition requirements in future optical networks, this study identifies three different modulation formats by the Inception-ResNet-v2 neural network model. Simulation results show that the Inception-ResNet-v2 model achieves the highest performance compared to other six methods, providing an effective solution for the related research and development of optical networks.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 4 Discussion
### Variability of Different Drought Indices  
Meteorological drought is the main cause of all types of drought and a precursor of other drought types [62]. MCI is a typical meteorological drought index. According to the drought transfer process of \"atmosphere-soil-vegetation\" by [PERSON] et al. [63], a change  
Figure 15: (**a**) NIR band before cloud detection, (**b**) IR band before cloud detection, (**c**) true color image of FY-4A, (**d**) NIR to IR ratio, (**e**) NDVI map, (**f**) CLM cloud detection product (the red square might be clouded).  
in vegetation growth conditions is transferred to the drought monitoring of TVDI, as a result of soil moisture content decreased. In the drought monitoring by TVDI, MCI, and SHRI, TVDI showed a lag, resulting in fewer pixels and stations in negative correlation and passing the significance test.  
The drought area on TVDI was generally greater than SHRI. There are two main reasons. On the one hand, the SHRI is a grid data calculated by assimilating through site data and remote sensing satellite data. And they are different in spatial resolution. The error is easy to occur during the unified resolution procedure. On the other hand, the equal interval method was used for drought grade division on TVDI. Therefore, the subsequent research can correspond the values of the two indices to the corresponding drought grades respectively, and apply statistical methods to analyze the TVDI threshold for each grade.
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 2 Related Works
### Knowledge Distillation (KD)  
The KD method proposed by [PERSON] et al. [21] was generalized by [PERSON] et al. [1]. This involves the use of softened labels, which is the most common approach to leveraging the knowledge of logits (i.e., logit-based KD) [22; 23; 24; 25; 26; 27]. However, the majority of studies on logit-based knowledge distillation focus more on proposing effective regularization and optimization methods rather than on new approaches. [PERSON] et al. [22] suggest a mutual learning strategy, where the student and teacher are trained simultaneously. [PERSON] et al. [23] propose a self-distillation method, where learning occurs within neural networks of the same size. [PERSON] et al. [26] introduce a method that utilizes an intermediary-sized assistant teacher to bridge the gap between the teacher and student networks. Nonetheless, logit-based knowledge can be challenging to generalize in certain datasets [25] (e.g., ImageNet [28]), and issues have been raised regarding the difficulty for the student to learn efficiently when there is a significant capacity difference between the teacher and student networks [29].  
Alternatively, a method exists that directly transfers the representations of intermediate layers, known as feature-based KD, to the student [30; 31; 32; 33; 34; 35; 36; 37; 38]. This approach aims to guide the student's learning process by leveraging the outputs of the teacher's hidden layers. Initially proposed by [PERSON] et al. [30], this method entails directing the student's learning based on the outputs of the teacher's hidden layers. Essentially, the student network learns to predict feature representations based on the intermediate representations of the teacher network. Although this approach demands more computational resources than the traditional logit-based KD, it has demonstrated promising results and generalization capabilities. Among the various methods of transferring feature knowledge from intermediate layers in KD, utilizing the attention mechanism [39; 40] has proven highly effective. However, these attention-based feature distillation methods lack a clear explanation of the role of attention.  
As discussed above, KD has undergone extensive study in the context of CNNs, primarily focusing on transferring knowledge at both the logit and feature levels. However, the application of KD techniques to CBMs has not been explored. Considering the promising results of KD in CNNs, investigating the potential of these methods in the context of CBMs could yield substantial improvements in their performance and explainability. Hence, this study aims to comprehensively explore training CBMs through KD, specifically proposing a method for transferring visual concept knowledge to enhance the interpretability of these models.
###### Abstract  
In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For this reason, it is often not performed regularly or not performed at all. In this study, we attempted to estimate the weights of cattle by using stereo vision and semantic segmentation methods used in the field of computer vision together. Images of 85 animals were taken from different angles with a stereo setup consisting of two identical cameras. The distances of the animals to the camera plane were calculated by stereo distance calculation, and the areas covered by the animals in the images were determined by semantic segmentation methods. Then, using all these data, different artificial neural network models were trained. As a result of the study, it was revealed that when stereo vision and semantic segmentation methods are used together, live animal weights can be predicted successfully.  
animal weight estimation; deep learning; image processing; semantic segmentation; stereo vision +
Footnote †: journal: Medical  
processing techniques using modern deep learning approaches to propose an effective solution to the problem considered. The main contributions and novelty of our study can be summarized as follows:  
1. We propose an effective indirect measurement method for determining the live weight of farm animals based on stereo vision and state-of-the-art semantic segmentation techniques using deep learning.
2. Our method is particularly important in that animals' body measurements are taken without the need for separating them from their natural environments and thus not adversely affecting their health and milk yield.
3. We propose a very simple yet effective system and setup composed of relatively cheaper hardware that is accessible and affordable for many farms of small to large scale.
4. We investigate and compare the performances of three different Artificial Neural Network (ANN) architectures in estimating live animal weight.  
The rest of this paper is organized as follows. The related work is reviewed in Section 2. In Section 3, we present the materials and methods used in the study. We present our experimental results and discussion in Sections 4 and 5, respectively. Finally, in Section 6, we conclude the paper.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods
### Performance Testing
In addition, lab testing can use channel simulators to generate non-line-of-sight communications. Testing performance in the case of obstacles does not require complex test scenarios, and only two communication terminals are required. Therefore, field testing is superior to lab testing. Figure 6: Terminal Performance Test System Architecture.
# Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP
###### Abstract
As examples, let us consider the \"maximum of posterior margins\" (MPM) defined with:  
\[[g(y_{1:N})=\hat{x}_{1:N}=(\hat{x}_{1},\dots,\hat{x}_{N})]\Longleftrightarrow [\forall n=1,\dots,N,p(\hat{x}_{n}|y_{1:N})=sup_{x_{n}}(p(x_{n}|y_{1:N}))] \tag{4}\]  
and the \"maximum a posteriori\" (MAP), defined with  
\[[g(y_{1:N})=\hat{x}_{1:N}]\Longleftrightarrow[p(\hat{x}_{1:N}|y_{1:N})=sup_{ x_{1:N}}(p(x_{1:N}|y_{1:N}))] \tag{5}\]  
Note that likely to any other Bayesian classifier, MPM and MAP are independent from \(p(y_{1:N})\). This means that in any generative model \(p(x_{1:N},y_{1:N})\), any related Bayesian classifier is strictly the same as the one related to the equivalent (in the meaning of Definition 1) CRF model \(p(x_{1:N}|y_{1:N})\). We see that the distinction between \"generative\" and \"discriminative\" classifiers is not justified: all Bayesian classifiers are discriminative. However, in HMM the related MPM and MAP classifiers are computed using \(p(y_{n}|x_{n})\), while this is not the case in LC-CRF. We show that both MPM and MAP in HMM can also be computed in a \"discriminative\" way, without using \(p(y_{n}|x_{n})\). Thus, the feasibility of using MPM and MAP in HMM is strictly the same as that of their use in LC-CRF, which is our second contribution. One of the consequences is that the use of MPM and MAP in the two families HMMs and LC-CRFs presents exactly the same interest, in particular in NLP. This shows that abandoning HMMs in favor of LC-CRFs in NLP because of the \"generative\" nature [6; 7; 8; 9; 15; 16; 17; 18] of their related Bayesian classifiers was not justified.
## 4 Results and Discussion
### Laminar Separation-Induced Transition
#### 4.2.2 Effects of the Separation Bubble
The results of the transition and standard \(k\)-\(\varepsilon\) models showed the C\({}_{\rm{I}}\)/C\({}_{\rm{d}}\) ratio underpredictions of 4.2% and 84.1%, respectively, at AoA of 5.13\({}^{\circ}\) when compared to the experimental value of 108.7, whereas the C\({}_{\rm{I}}\)/C\({}_{\rm{d}}\) ratio of the semi-transition model was 109, which showed overprediction of 0.3%. However, the semi-transition model approach is not currently useful for predicting the transition location because the experiments should be suggested in advance, prior to the simulation, as described in the previous section. Therefore, the C\({}_{\rm{I}}\)/C\({}_{\rm{d}}\) ratio values of the transition and standard \(k\)-\(\varepsilon\) models cause deviations of 0.17% and 25.4% in power, respectively, based on the BEM theory-based calculations [7], when rotational speed, local chord length, and local radius are 71.9 rpm, 0.59 m, and 2.71 m, respectively; the local twist angle, rotor radius, and tip speed ratio are 2.754\({}^{\circ}\), 5.029 m, and 2.915, respectively; and the local solidity, wind speed, density, and span length for the blade element are 0.0693, 7 m/s, 1.246 kg/m\({}^{3}\), and 0.59 m, respectively. A complete description of the BEM theory-based calculations has been reported elsewhere [7] because explaining in greater detail is out of the scope of this research. The bubble lengths at AoAs of 8.04\({}^{\circ}\), 9.22\({}^{\circ}\), 14.24\({}^{\circ}\), and 20.15\({}^{\circ}\) are 1.92%, 1.78%, 0.74%, and 0.84% of the chord length, respectively. According to [PERSON] et al. [16], a decrease in the bubble extent of 9% causes an improvement in the maximum lift-to-drag ratio of approximately 17%. Therefore, the aerodynamic performance of the airfoil can be considerably improved by the mitigation of the bubble by increasing the laminar region, thus decreasing the drag, and enhancing lift-to-drag ratio due to a smaller bubble size.
## 4 Coexistence Solutions for Sync-FT Operation
### Doubling the CW  
The next feature uses CW doubling as a penalty. Upon completion of a free-riding transmission, the CW of the corresponding link is doubled; consequently, the next backoff count to be picked for this link will have a higher value on average. We propose two solutions that use this feature, which we call _Double_ and _Double_ + _Repick_, respectively. Double allows an STA to continue its BO with its remaining backoff count, as illustrated in Figure 12. Once the STA transmits upon completion of the BO, its CW is then doubled as a penalty regardless of the transmission result, and the next backoff count is picked. As illustrated in Figure 13, under Double + Repick, an STA doubles its CW and immediately repicks a new backoff count after a free-riding transmission finishes. That is, the difference between the two solutions is the use of the feature Repick after the doubling of the CW. Therefore, Double+Repick will assign a larger penalty to a free-riding link.
# Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium
## 2 Data Page  
Holographic data storage systems theoretically allow recording information with a density of up to 1 Tb/cm\({}^{3}\)[1; 2]. The absence of contact with the storage medium during its data recovery significantly increases the service life of such devices. A holographic data writer records information in parallel and not bitwise. Therefore, the high speed of data writing and access can be achieved with more than 20 MB/s [28]. The data page is the main signal in the holographic memory system. Figure 1 shows an image of a binary amplitude data page with a size of 84 \(\times\) 240 pixels.  
Figure 1: Binary data page of archival holographic memory.  
The peculiarity of such pages is that one byte is encoded using a track of 16 elements. Three elements correspond to \"1,\" and thirteen ones correspond to \"0\". The format of the data page was adapted from the well-known ECMA-377 standard, the first standard for a holographic disk cartridge. The considered format provides equality of diffraction efficiencies for holograms containing different information. In addition, bits corresponding to \"1\" cannot be located on the track in neighboring cells. This provides an increase in the quality of the reconstructed data page and a decrease in bit errors.  
Random or pseudo-random phase masks can be added to achieve high diffraction efficiency of holograms that form images of binary or multilevel amplitude data pages [39]. Modern holographic data storage systems use multilevel complex light modulation methods to represent digital information in the form of a data page, which allows for increasing the recording density. For example, multilevel amplitude [40], ternary [41], multilevel phase, and multilevel amplitude-phase (quadrature) [20] modulation methods can be used to represent digital information with data page pixels. There are several methods available to reconstruct complex or bipolar amplitude data pages, which include the interferometric method [42], digital holography method [43], transport-of-intensity equation method [44], embedded phase shift method [45], etc.  
Analysis of data page images is usually performed by calculating the signal-to-noise ratio (SNR) for measured in-phase and quadrature components of the complex amplitude. As a result, the bit error rate (BER) value can be determined. For binary intensity or amplitude-modulated data pages, such as those shown in Figure 1, the BER value can be found as follows:  
\[BER=\frac{1}{2}Erf(SNR) \tag{1}\]  
where \(Erf(x)\) is the complementary error function. For quadrature-modulated data pages with \(M\) complex symbols, the bit error rate value can be found as:  
\[BER\sim\frac{M-1}{M\log_{2}M}Erf\bigg{(}\frac{SNR}{\sqrt{2}}\bigg{)} \tag{2}\]
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.8 Mitigation of Overfitting with Bayesian Interpretations  
The probabilistic interpretation of DDPM provides a natural means for Bayesian modeling. This, in turn, mitigates the risk of overfitting, a common concern in biomedical image segmentation tasks. Bayesian interpretations allow for the incorporation of prior knowledge and regularization, leading to more robust models [42; 53].  
In conclusion, the application of DDPM in biomedical image segmentation brings forth a multitude of advantages, ranging from robust handling of noisy data to uncertainty estimation and integration with advanced computational techniques. The cited references underscore the significance of these advantages and highlight the ongoing research effortsto harness the full potential of DDPM in addressing the unique challenges posed by biomedical imaging. As the field continues to evolve, DDPM stands as a promising tool for advancing the accuracy and reliability of biomedical image segmentation, with implications for improved diagnostics and treatment planning in medical practice.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 6 Structure, Function and Regulation of Pkc1
### Nuclear Targets  
Given the evidence discussed above that Pkc1 undergoes nucleocytoplasmic shuttling [249], it is perhaps not unexpected that substrates for Pkc1 that are confined to the nucleus have been described. Moreover, given the original observation that Pkc1-deficient cells display a cell cycle-specific arrest at the G2-M transition [38], it is perhaps satisfying that one nuclear target reportedly phosphorylated by Pkc1 is the Ndd1 subunit of the Mcm1-Fkh2-Ndd1 transactivator complex [241], which is part of the machinery that controls the periodic expression of B type cyclins (Clbs) in yeast. One of two reported sites in Ndd1 matches well the features of the minimal Pkc1 phospho-acceptor site consensus defined above (Figure 7). Absence of phosphorylation of Ndd1 at these sites causes late cell cycle defects due to changes in the timing of the expression of the _CLB2-CLB5_ cluster on chromosome XVI [241].  
In addition to transcription, evidence has been obtained that Pkc1 phosphorylates two nuclear targets that are necessary for efficient re-assembly of chromatin following DNA replication [240]. For this process, histone H3 (Hht1 and Hht2 in yeast) is phosphorylated at Thr46, which promotes its acetylation at Lys57. Under conditions of replicative stress, Pkc1 appears responsible for H3 T46 phosphorylation and Pkc1, by reportedly phosphorylating Thr46 (by coincidence at the same residue number) in the histone acetyltransferase Rtt109, also stimulates acetylation of H3 K57 [240]. The site in Hht1 (Hht2 is identical in sequence) matches some features of the pseudosubstrate sequence in Pkc1, whereas it matches the putative site in Rtt109 much less so (Figure 7). It would be unprecedented for an AGC kinase family member to have such a broad or lax phospho-acceptor sequence preference; hence, the conclusion that Thr46 in Rtt109 is a direct target of Pkc1 needs further corroboration.