esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications of the Model  
The LSTM model was employed to accurately capture the temperature fluctuations at the cold energy storage source, the surface of the cold storage plates, the fresh products, and the air inlet and outlet of the container. It predicts the temperature changes at these locations, offers valuable insights into the timely replacement of cold storage plates, monitors the status of the CCB during transport, and ensures optimal temperatures are maintained in the fresh-keeping area.  
The training set of the model was set to the initial 80% of the samples, and the value of the CCB temperature of the PCM and fresh products was set to 5 \({}^{\circ}\)C and 10 \({}^{\circ}\)C, respectively. The training and prediction results are shown in Figure 12a-e and Table 6. As shown in Figure 12, the predicted temperature variations are all in agreement with the actual values. As shown in Table 6, the relative errors in predicting the status of CCB in cold energy storage sources and fresh products are 1.05% and 0.45%, respectively, indicating that the LSTM model can accurately predict the status of CCB. In addition, the RMSE and MAE of the temperatures of the fresh products, inlet, outlet, and surface of cold storage plates are all below 1.000, the MAPE is below 0.1000, and the R\({}^{2}\) of the temperatures of the fresh products, inlet, and outlet are slightly higher, with values exceeding 0.9400, which indicates that the LSTM model accurately predicts the temperatures of the fresh products, inlet, outlet, and the surface of cold storage plates.  
Figure 12: Training and prediction results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Training** & **Training** & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Time for Temperature**} & \multirow{2}{*}{**Relative**} \\  & **Sample/\%** & **Time/s** & & & & & **Predicted** & **Actual** & **Error/\%** \\ \hline
1 & 80 & 2487 & 0.073 & 0.066 & 0.007 & 0.994 & 57.11 & 56.74 & 0.66 \\
2 & 50 & 1740 & 0.102 & 0.087 & 0.010 & 0.993 & 57.26 & 56.74 & 0.93 \\
3 & 20 & 932 & 0.059 & 0.051 & 0.006 & 0.997 & 57.11 & 56.74 & 0.66 \\
4 & 10 & 515 & 0.651 & 0.580 & 0.073 & 0.631 & 56.62 & 56.74 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of prediction results for different training datasets.  
One of the reasons for the low R\({}^{2}\) of the temperature prediction of the cold energy storage source and surface was that the temperature data in the training set were maintained near the phase change temperature of 0 \({}^{\circ}\)C for a long time, and the increasing trends were not obvious, resulting in the predicted values in the test set not rising as drastically as the actual values.
## 3 Discussion
However, in this study, we report that their expression is enriched when mice receive pitavastatin treatment at high concentrations, which is indicative of the treatment efficacy. A previous independent study by some of the authors of the present paper demonstrated that the upregulation of ketones is correlated with antitumor effects induced by bone loading [34]. In vitro assays demonstrated that two specific ketones (2-pentanone and 2-HEP) reduce tumor cell viability, which was also correlated with increases in analkylamine N-acetyltransferase (AANAT) and tyrosine hydrogenase (TH). These analytes are enzymes involved in the syntheses of dopamine and melatonin, which have also previously been shown to play a role in the suppression of tumors [34]. Limitations of this study include that the results were based on a relatively small sample size, and there was no independent external validation performed for the multivariate analyses. Additionally, all mice were housed in the same environment and fed the same diet. Therefore, probing VOC differences due to breast cancer and antitumor therapies in humans will be more difficult, as they will present higher heterogeneity from varying diets, levels of activity, etc. Breast cancer treatments in humans may also significantly alter VOC expression, regardless of the efficacy of the treatment, which will make utilizing VOCs for monitoring treatment efficacy more difficult. Finally, there is also an array of breast cancer treatments (surgery, radiation, systemic, etc.) which may or may not be able to be monitored using VOCs in urine. Another potential downfall of this study is the fact that no healthy control mice were given pitavastatin treatments (only tumor-bearing mice). Pitavastatin alters the mevalonate pathway [29], which is a rich source of volatile terpenes (VTs). However, the LDA panels for both approaches excluded the use of VTs and included mainly ketones and aromatics. To the best of our knowledge, there is no direct correlation between ketones/aromatics and the mevalonate pathway. The origin of VOCs in this study was speculative, and VOCs were not correlated to other types of biomarkers, including but not limited to genes, proteins, other metabolites, etc. Future studies should aim to correlate VOCs to other types of biomarkers for validation and exploring the potential origin of these molecules. The long-term goal is to translate these results to women with breast cancer, potentially aiding in the decision-making processes during treatment and decreasing overdiagnosis/overtreatment. In the short-term, however, future investigations utilizing murine models to assess the efficacy of different therapeutics may analyze urinary VOCs as an alternative or complementary method to micro-CT, X-rays, and histological techniques.
## 3 FMMs for Discrete Channel Modeling
### FMM Parameters  
The three-state FMM used for discrete communication channel modeling is defined by three parameters: the \(N\times N\) state transition matrix \(A\), the \(M\times N\) error probability generation matrix \(B\), and the initial state distribution matrix \(P\). State transition matrix \(A\): For the three-state FMM adopted in this work, the initial state transition probability matrix and the initial values adopted for modeling shall be represented by the following:  
\[A=\begin{bmatrix}a_{1,1}&0&a_{1,3}\\ 0&a_{2,2}&a_{2,3}\\ a_{3,1}&a_{3,2}&a_{3,3}\end{bmatrix}=\begin{bmatrix}0.8&0&0.20\\ 0&0.70&0.30\\ 0.20&0.60&0.20\end{bmatrix} \tag{1}\]  
Although the initial values of the FMM parameters are assumed, it should be noted that the diagonal elements are carefully chosen to depict the error sequence. For the above \(A\)-matrix, transitions between good states are not possible, which makes the elements \(a_{1,2}\) and \(a_{2,1}\) zeros. As also seen in Equation (1), the probability of remaining in the two error-free (good) states is high (0.80 and 0.70), depicting the measured bit error sequences, while the probability of remaining in the error (bad) state is lower (0.20).  
The error generation matrix \(B\): This parameter provides an expression of the probability that a discrete error will take place in the state that has been specified. Taking into account the fact that error-free states are considered to be good states and that errors are always produced in bad states, the error generation matrix for a three-state FMM is as follows:  
\[B=\begin{bmatrix}1&1&0\\ 0&0&1\end{bmatrix} \tag{2}\]  
According to Equation (2), the first two columns represent good states, whereas the third represents a bad state. In each state, the probability that there will be no errors is depicted in the first row of the table, while the probability of errors is shown in the second row. Because of this, the model is known as a semi-hidden Markov model because it is only possible to determine which of the good states was responsible for the correctly received bits if there is an error. However, if there is no error, it is not possible to determine which of the good states was responsible for the correctly received bits.  
Figure 4: Three-state FMM.  
Initial state distribution matrix \(\pi\): This represents the initial probability of being in any state at any discrete time.  
\[\pi=\begin{bmatrix}0.45&0.45&0.10\end{bmatrix} \tag{3}\]  
Consequently, the entire set of parameters from Equations (1)-(3) that defines a discrete three-state FMM in compact notation is  
\[\Gamma=(A,B,\pi) \tag{4}\]
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods
#### 2.2.6. GAF-Net  
[PERSON] et al. [13] introduced the global attention-based fusion network (GAF-Net), which offers an innovative architecture for improving the results of remote sensing image analysis. GAF-Net incorporates global attention mechanisms and introduces cross-attention and self-attention learning techniques.  
GAF-Net comprises two types of primary sub-networks:  
1. Modality-specific classification networks: Two separate networks are designed for each modality. These networks specialize in feature extraction and classification for individual modalities;
2. Bi-stream fusion network: The bi-stream fusion network is responsible for combining the outputs of the modality-specific networks and facilitating effective fusion.  
GAF-Net introduces a novel self-attention block known as the non-local spectral-spatial self-attention block (SAB). This block plays a pivotal role in capturing long-range dependencies and spatial relationships within the data. GAF-Net also employs cross-attention learning, a technique that enables the network to learn from multiple viewpoints and perspectives. Cross-attention mechanisms help in integrating information from different sources, allowing the network to make well-informed decisions.
## 3 Design  
To mitigate the problems discussed in Section 1, we propose a novel Binary Code Similarity Detection (BCSD) approach called BlockMatch to solve the last-mile problem of BCSD tasks regarding matching among basic blocks. Figure 3 illustrates the overall architecture of BlockMatch, encompassing two key modules that enable its functionality:  
**Representation Generation Model**. This module is responsible for generating embeddings for each basic block present in the input functions. Initially, we extract basic blocks from the input functions and employ disassembly tools to convert machine code into instruction sequences for each basic block. Subsequently, we utilize a basic block model based on DeBETA [45] to capture the semantics of these basic blocks, thereby producing corresponding embeddings. To train our model, we employ a masked language model and a novel root operand model for pre-training. We then use a block similarity model to fine-tune our model, ensuring that similar basic block embeddings are closer together.  
**Contrastive Training Framework**. This module aims to generate diverse and high-quality samples for training the model, employing the NT-Xent loss objective function [31] to optimize the model parameters. During the training phase, given training basic blocks, we propose a block augmentation model to generate positive samples and negative samples based on their metadata. Specifically, for positive samples, the block augmentation model generates basic blocks originating from the same source code lines, but compiled using different compilers, architectures, and optimizations compared to the anchor basic block. Conversely, for negative samples, the block augmentation model generates basic blocks  
Figure 3: The overview of BlockMatch, which consists of a representation generation model and a contrastive training framework.  
with different I/O emulation results from different source code lines in relation to the anchor function. Subsequently, we utilize the NT-Xent loss objective function to optimize the representation generation model by considering their block similarities between large-batch samples.
# An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
## 3 Computational Experiments
### Non-Periodic Forcing Term
Through Figures 6, 8 and 9, we compared the results of the explicit adaptive time step method, the constant time step method, and the Crank-Nicolson method for a rapidly changing solution. From these results, the adaptive time step method proved to be better than the constant time step method in terms of both execution time and error. While the first-order adaptive time step method showed slightly larger errors compared to the second-order Crank-Nicolson method, it achieve a similar level of error with significantly less execution time. Furthermore, the proposed adaptive method has the advantage that the trial and error of finding a suitable time step is unnecessary. If a desired tolerance is given, the adaptive time step size is automatically given. However, when applying the Crank-Nicolson method, attempts for finding a suitable time step must be previously performed. Therefore, if we include this procedure in account, the proposed method has a superiority compared to the Crank-Nicolson method. Figure 8: Numerical solutions for the explicit method with constant time step \(\Delta t=1.3737\times 10^{-8}\) at (**a**) \(t=0.01\), (**b**) \(t=0.07\), and (**c**) \(t=0.1\). Numerical and analytic solutions of (**d**) \(u\), (**e**) \(v\), and (**f**) \(w\) at \(t=0.3\). The differences between the exact solution and the numerical solutions (**g**) \(u\), (**h**) \(v\), and (**i**) \(w\) at \(t=0.3\). In concluding this section, we shall provide the maximum error as a function of the tolerance in order to illustrate the behavior of the controller for many levels of accuracy. We used the exact solution and forcing term introduced in this section on the computational domain \((0,\,1)\) with \(h=1/128\) at final time \(T=0.1\). Under maximum step size \(\Delta t_{\max}=0.5h^{2}\), maximum errors at tolerance \(tol=10^{-7},1.2\times 10^{-6},2.3\times 10^{-6},2.3\times 10^{-6}\), \(3.4\times 10^{-6}\), \(4.5\times 10^{-6}\), \(5.6\times 10^{-6},6.7\times 10^{-6},7.8\times 10^{-6},8.9\times 10^{-6},10^{-5}\) are illustrated in Figure 10. We can see that after the first data, the maximum error linearly grows with the tolerance. Figure 10: Maximum errors as a function of the tolerance. Tolerance values range from \(10^{-7}\) to \(10^{-5}\). Figure 9: Numerical solutions for the Crank–Nicolson method with constant time step \(\Delta t=2\times 10^{-8}\) at (**a**) \(t=0.01\), (**b**) \(t=0.07\), and (**c**) \(t=0.1\). Numerical and analytic solutions of (**d**) \(u\), (**e**) \(v\), and (**f**) \(w\) at \(t=0.3\). The differences between the exact solution and the numerical solutions (**g**) \(u\), (**h**) \(v\), and (**i**) \(w\) at \(t=0.3\).
## 4 CNQF Prototype and Testbed Implementation
### Testbed Implementation  
The testbed used for development of CNQF and validation of its functionalities is implemented using Linux systems. The configuration is shown in Figure 2. The testbed consists of two Linux-based  
\begin{table}
\begin{tabular}{l l l} \hline
**MIB Object** & **Description** & **OID** \\ \hline ifInOctets & The total number of octets received on the interface, including framing characters & 1.3.6.1.2.1.2.2.1.10.2 \\ ifOutOctets & The total number of octets transmitted out of the interface, including framing characters & 1.3.6.1.2.1.2.2.1.16.1 \\ ifSpeed & An estimate of the interface’s current bandwidth in bits/sec & 1.3.6.1.2.1.2.2.1.5.1 \\ \hline \end{tabular}
\end{table}
Table 1: MIB OIDs used in CNQF _netmon_ class for bandwidth monitoring (RFC 1213).  
edge routers and two Linux-based core routers. These elements constitute the PEPs each having an instance of CNQF RC (ResCon) that interacts with the Linux router kernel to set various parameters that enable configuration/reconfiguration of QoS provisioning to suit the high-level policies processed by the RB. The Linux TC (traffic control) utility in the kernel provides commands for implementing packet marking, classification, queuing disciplines, and policing of flows (thus enabling the transport plane mechanisms required to provide QoS and resource control). In the testbed developed, the RCs employ TC commands for low level configuration which have equivalent mappings to RB Java code that implement the high level _policy actions_. The testbed elements include:  
* _CNQF management station_: houses central CNQF management application with the GUI policy editing tool and RMS CNRB implemented in Java which invokes policy actions via remote RCs (ResCon instances) installed at the PEPs (edge and core routers in the testbed).
* _Edge routers_: Ubuntu 10.0.4 Linux PC with 2.66 GHz Intel Xeon, 3 GB RAM, configured as edge routers with TC utility installed to enable configuration of the router interface(s) for ingress packet marking, and for egress classification, queuing and policing via RC's response to CNQF policy decisions.
* _Core routers_: Ubuntu 10.0.4 Linux PCs with 2.66 GHz Intel Xeon, 3 GB RAM, with TC utility installed to enable configuration of packet classifiers and filters through CNQF policies also via an RC.
* _Traffic generators_: The _ntools_ traffic generator is used to generate multi-client traffic with different flow characteristics including constant bit rate (CBR), On-Off traffic, and variable bit rate (VBR) traffic. _ntools_ is an open source versatile traffic generator, analyzer and network emulator package for Linux. The multi-client generation capability with packet field injection is used to emulate traffic from multiple access networks.  
Figure 2: Linux-based testbed for CNQF development and experimental evaluation.
## 3 Results
### Expressions of Genes Involved in Ethylene Production  
The genes _MdACS_ and _MdACO1_ encode the key enzymes for ethylene biosynthesis in GD apples. Over the storage period, the expression of _MdACS_ and _MdACO1_ in all tissues showed an upward trend (Figure 5A,B). However, compared with the air group, the 2 kPa O\({}_{2}\)-stored apples had lower levels of _MdACS_ and _MdACO1_ expression. During the shelf-life period, the expression of _MdACS_ and _MdACO1_ in 2 kPa O\({}_{2}\)-stored fruit peaked on day 56 + 23, while the peaks mostly occurred on day 56 + 9 in the air group (Figure 5A,B). Additionally, higher transcript levels of _MdACS_ and _MdACO1_ were observed in Flesh1 compared with Skin and Flesh2 (Figure 5A,B).  
Figure 5: Expressions of _MdACS_, _MdACO1_, and _MERFs_ in skin and flesh of ‘Golden Delicious’ apple fruit during 56-day storage under 2 kPa O\({}_{2}\) + 98 kPa N\({}_{2}\) at 4 °C and subsequent 44-day shelf life at 20 °C. The expressions of _MdACS_, _MdACO1_, _MERFa_, _MARFb_, _MARFc_, _MARFd_, _MARFe_ and _MdAFFR_ are shown in (**A\({}_{1}\)–A\({}_{3}\)**, **B\({}_{1}\)–B\({}_{3}\)**, **C\({}_{1}\)–C\({}_{3}\)**, **D\({}_{1}\)–D\({}_{3}\)**, **E\({}_{1}\)–E\({}_{2}\)**, **F\({}_{1}\)–F\({}_{3}\)**, **G\({}_{1}\)–G\({}_{3}\)** and **H\({}_{1}\)–H\({}_{3}\)**), respectively.  
Air represents apple fruit stored in air; 2 kPa O\({}_{2}\) represents fruit stored under 2 kPa O\({}_{2}\) + 98 kPa N\({}_{2}\). Skin indicates tissue obtained at 0\(-\)0.1 mm of top pericarp; Flesh1 and Flesh 2 indicate flesh tissues obtained, respectively, 0.1\(-\)1.0 cm and 2.0\(-\)3.0 cm below the exocarp. Bars indicate standard deviation for each mean value from at least three replicates. Asterisks indicate significant difference between air-stored and 2 kPa O\({}_{2}\)-stored fruit at \(p<\) 0.05 ([PERSON]'s multiple range test).  
The expression patterns of six ethylene responsive factor (ERF) VII genes in GD apples are shown in Figure 5. Compared with the air group, the expression of \(MdERFa\) in 2 kPa O\({}_{2}\)-stored fruit was upregulated during early storage, while the upregulations of \(MdERfb\) and \(MdERfc\) expressions were observed in late storage (Figure 5C-E). However, during storage, the expressions of \(MdERfd\), \(MdERFe\), and \(MdERf\) were downregulated in 2 kPa O\({}_{2}\)-stored fruit, especially in flesh tissue (Figure 5F-H). During the shelf-life period, the transcript levels of \(MdERfs\) in apple fruit peaked on day 56 + 9 or day 56 + 23 (Figure 5C-F), which is consistent with ethylene production in apples (Figure 1C).
## 2 Micro-Doppler Signals
### Pre-Processing for Raw Data  
The processing methods of previous research require mathematical equations for extracting feature vectors from I/Q signal shown in Figure 3. In these methods, after extracting a feature vector by feature extraction algorithms using mathematical equations, the classification algorithm (e.g., SVM or DNN) processes this generated feature vector. However, the optimization of this approach could be quite complicated because of the difficulty of finding an essential feature vector and it requires comparatively long pre-processing time due to pre-processing for this feature extraction. Meanwhile, this paper does not use complex mathematical equations but also aims to extract features and classify targets in one second of processing time. We do not use any pre-processing methods for feature extraction and we use only Fourier transform in pre-processing for classification. We use signals of frequency domain converted by Fourier transform as the input data of DNN. The trained DNN automatically extracts and generates the features and it predicts the optimal results.
## 4 Methodology  
### Statistical Transformation and Categorization  
The methodology employs statistical quartile analysis to establish categorical boundaries that add meaning to our clusters [37]. The data distribution of the three objects is given by the boxplot provided in Figure 1. The distributional characteristics of the synthetic dataset are illustrated through boxplot analysis for features \(f_{1}\) and \(f_{2}\) across entities \(X\), \(Y\), and \(Z\) in Figure 1. The visualization reveals distinct distributional patterns that serve as the basis for quartile-based categorical boundary determination.  
Entity \(X\) shows lower variability, with values concentrated in the lower quartiles for both features, reflecting stable and systematic progression. Entity \(Y\) exhibits a wider distribution with higher median values, particularly for \(f_{1}\), indicating greater variability around elevated baseline levels. Entity \(Z\) demonstrates intermediate characteristics with moderate spread, capturing balanced temporal dynamics between the extremes represented by entities \(X\) and \(Y\).  
These distributional differences establish the statistical foundation for the subsequent categorical transformation process, ensuring that the derived quartile boundaries capture meaningful variation across distinct evolutionary patterns.  
The clusters are created in a way where the end points represent two adjacent values in the boxplot. The clusters signify the magnitude of the absolute value of the feature as it ranges from _Below Lower Bound_ to _Above Upper Bound_.  
This transformation function  
Converts continuous numerical values into ordinal categories.  
Creates a standardized framework for comparative analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Years**} & \multicolumn{2}{c}{\(X\)} & \multicolumn{2}{c}{\(Y\)} & \multicolumn{2}{c}{\(Z\)} \\ \cline{2-7}  & \(f_{1}\) & \(f_{2}\) & \(f_{1}\) & \(f_{2}\) & \(f_{1}\) & \(f_{2}\) \\ \hline
1 & 1 & 3 & 24 & 8 & 8 & 12 \\
2 & 2 & 3 & 22 & 10 & 15 & 11 \\
3 & 3 & 4 & 20 & 6 & 3 & 10 \\
4 & 4 & 4 & 18 & 12 & 12 & 9 \\
5 & 5 & 7 & 16 & 4 & 7 & 8 \\
6 & 6 & 9 & 14 & 14 & 19 & 8 \\
7 & 7 & 14 & 12 & 2 & 5 & 8 \\
8 & 8 & 14 & 10 & 16 & 10 & 9 \\
9 & 9 & 15 & 8 & 1 & 2 & 10 \\
10 & 10 & 15 & 6 & 18 & 17 & 11 \\
11 & 11 & 16 & 4 & 3 & 6 & 12 \\
12 & 12 & 16 & 2 & 20 & 14 & 13 \\ \hline \end{tabular}
\end{table}
Table 1: Synthetic dataset for methodology demonstration.  
Figure 1: Distributional analysis of synthetic dataset.  
The categorical labels provide a parsimonious yet significant understanding of the representation provided by the various categories extrapolated from the boxplot.