esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [26; 27; 28; 29; 30; 31; 32], the ad-hoc-based CFO and SFO estimation strategy was developed calculating the phase difference from two consecutive pilot symbols with symmetric and uniform distribution across DC. In [33; 34; 35], a decoupled estimation of CFO and SFO was proposed, which is still computation-intensive for real-time processing. Hence, more efficient frequency estimation and its complete performance analysis in a multipath fading channel is a key challenge in realizing high-performance MIMO-OFDM receivers. In this paper, the performance analysis of joint CFO and SFO estimator for the MIMO-OFDM ATSC broadcast system is presented in a the multipath fading channel. The CPs are used for the purpose of synchronization, and an optimal CP subset is searched to form the basis of robust estimation. The choice of the optimal pilot subset plays an important role on removing a bias produced by using random-likely distributed CPs. To assess the usefulness of the proposed LSE method, the mean squared error (MSE) is theoretically derived for the MIMO-OFDM ATSC system in a frequency-selective fading channel. It is demonstrated from numerical analysis that the joint estimation scheme using optimal pilot subset offers a robust estimation performance with a low computational burden, in comparison to the existing method. This paper is structured as follows. Section 2 describes the signal model in the MIMO-OFDM ATSC system. In Section 3, the existing LSE method is applied to the MIMO-OFDM ATSC system. In Section 4, an improved LSE method is presented and the MSE performance is numerically analyzed in the MIMO-OFDM ATSC system. The simulation results and discussions showing the advantages of the proposed LSE method are presented in Section 5. Section 6 concludes this paper. The notations used in this paper are listed as follows. \(|\cdot|\), \((\cdot)^{*}\), \(E\{\cdot\}\), and \((\cdot)^{Q}\) denote the operations taking the absolute value, complex conjugation, expectation, and real part of enclosed quantity, respectively. \(\angle\) and \(\otimes\) indicate the radian angle and convolution operators. The imaginary unit is \(j=\sqrt{-1}\).
## 3 Proposed Method
### Malware Detection Algorithm
#### 3.3.3 Calculation of the Anomaly Score for the Final Decision  
The calculation of the anomaly score is inspired by the methods used in [5]. A sample file is determined as malware based on whether the calculated anomaly score \(M(0\leq M\leq 1)\) is greater than or equal to a predefined threshold \(T\). The anomaly score is calculated regardless of the malware category to avoid missing attacks even when category classification accuracy is unstable. Although this score is the same as that in [PERSON] et al. [5], the difference is that weights are used to add up \(V_{\text{m}}\) as shown in Algorithm 1, which includes all three steps.  
\[M=V_{\text{m}}/\left(V_{\text{m}}+V_{\text{b}}\right) \tag{3}\]  
```
1:functiondetection(\(file\))
2:\(V_{\text{m}}=0,V_{\text{b}}=0\)
3:for all\(k\gets K\)do
4:if\(k\)th anti-virus engine determines file is malicious then
5:\(w=w_{\text{low}}\)
6:if Determined category \(c\) is in the set of maintained categories then
7:\(w\gets W_{\text{ck}}\)
8:else
9:if\(k\)th anti-virus engine response includes the anomaly score \(s\)then
10:\(\delta\leftarrow\text{normalize}(s)\)
11:\(w\leftarrow\text{get\_weight}(\delta)\)\(\triangleright\) Assign the weight value by Equation (2)
12:endif
13:endif
14:\(V_{\text{m}}\gets V_{\text{m}}+1\times w\)\(\triangleright\) Apply the weight for a malicious judgment vote
15:else
16:\(V_{\text{b}}\gets V_{\text{b}}+1\)
17:endif
18:endif
19:\(M=V_{\text{m}}/(V_{\text{m}}+V_{\text{b}})\)\(\triangleright\) Calculate the anomaly score
20:if\(M\geq T\)then
21:return The final decision is \"malicious\"
22:else
23:return The final decision is \"benign\"
24:endif
25:endfunction
```  
**Algorithm 1** Proposed malware detection algorithm
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 1 Introduction
### Related Work
[15] proposed a method for fall detection based on dimensionality reduction using Kernel Discriminant Analysis (KDA) in combination with weighted support vector machines (WSVMs). This approach yielded an improvement of up to 5.6% in the F1-score compared to baseline models without feature reduction. The best performing model achieved an F1-score of 95.3% on the UniMiB-SHAR dataset. [PERSON] and [PERSON] [16] conducted a comprehensive review of publicly available datasets related to fall detection. They provided an analysis of these datasets and subsequently evaluated the performance of five classical machine learning algorithms: \(k\)-Nearest Neighbors (\(k\)-NN), logistic regression, decision tree, naive Bayes classifier, and linear discriminant analysis. Their results indicated that \(k\)-NN was the most effective (about 95% F1-score) in distinguishing fall events from activities of daily living on a custom dataset. However, the authors did not provide results for the UniMiB-SHAR dataset specifically. [PERSON] et al. [17] addressed multiclass classification task using the UniMiB-SHAR dataset by introducing a novel architecture termed Multi-ResAtt. This model employs a multi-resolution attention mechanism to extract features from sensor data, which are then processed by a Recurrent Neural Network (RNN) for activity classification. They achieved 85.35% accuracy for the multiclass classification task. However, the authors did not provide the results for the binary classification task, which is the focus of our study. In one of the most recent studies, [PERSON] et al. [18] utilized the UniMiB-SHAR dataset to train an optimized ResNet-based architecture. Their model demonstrated high performance in the binary fall detection task (achieveing 99.87% accuracy). Recent studies have explored deep learning-based approaches for fall detection using the UniMiB-SHAR dataset with remarkable success. [PERSON] et al. [19] proposed a Patch-Transformer Network (PTN), a hybrid CNN-Transformer model, achieving an accuracy of 99.14% in binary fall detection. In 2024 [20] authors introduced the Parallel CNN-Transformer (PCNN-Transformer) architecture, which attained 98.68% accuracy on UniMiB-SHAR, along with similarly high performance across other benchmark datasets with 0.282 s average inference time. An interesting approach was presented in [21], where the authors combined information gathered from two sources: an accelerometer and WiFi signals. Unfortunately, the solution (source codes) was not shared publicly like in the other studies.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Deployment Location  
As an acceleration engine, BoostTCP follows the network driver interface specification and is located between the protocol stack and the hardware network interface card (NIC). It is fully compatible with the standard TCP protocol and does not attempt to replace the original TCP protocol stack in the operating system. When an application continues to interact with the TCP stack of the operating system in which it resides, BoostTCP is completely transparent to the application. When traffic is routed through the BoostTCP module, BoostTCP accelerates it by changing the timing of data packet transmission and retransmission without changing the data content or TCP encapsulation format. The position of BoostTCP in a multi-layer network architecture is presented in Figure 3.
# Role of Cyclin-Dependent Kinase 1 in Translational Regulation in the M-Phase
## 5 CDK1 Regulates the Elongation Step of Translation  
In eukaryotes, the culmination of translation initiation coincides with the formation of an 80S initiation complex in which initiator methionine transfer RNA (Met-tRNAMet) is bound to the P (peptidyl) site of the ribosome. The anticodon of the Met-tRNAMet is base-paired with the start codon of the mRNA, and the second codon of the open reading frame (ORF) is localized on the A (aminoacyl) site of the ribosome. Elongation is initiated when the cognate elongating aminoacyl-tRNA is delivered to the A site of the ribosome. The eukaryotic translation elongation factor eEF1A is activated upon binding to GTP and creates a ternary complex upon binding to aminoacyl-tRNA [54, 85].  
CDK1 modulates the activity of elongation factors (Table 1). Translation elongation in eukaryotes is mediated by the determined actions of elongation factor 1A (eEF1A); elongation factor 1B (eEF1B) complex and elongation factor 2 (eEF2). In HeLa cells, and in sea urchin embryos, the translation elongation rate declines in synchrony with an increase of CDK1 activity [86, 87]. The eEF2 kinase (eEF2K), which inactivates eEF2, is inhibited by phosphorylation at Ser359 and CDK1 has been identified as the kinase phosphorylating eEF2K at Ser359, indicating that CDK1 stimulates the activity of eEF2 during mitosis [56].  
In human cells, a conserved consensus phosphorylation site for mitotic CDK1 is present on the catalytic \(\delta\) subunit of eEF1B (termed \"eEF1D\"). The eEF1D and eEF1 By subunits are physiological substrates for CDK1 during the resumption of meiosis in _Xenopus_ oocytes [55, 88].  
In monkey kidney epithelial cells, two eEF1D consensus CDK1 target sites were identified, Ser-133 and Thr-147, whilst eEF1D has been reported to be phosphorylated by CDK1 on Ser-133 in vitro [89]. Phosphorylation of Ser-133 during mitosis in HeLa cells is necessary for the reduced interaction of eEF1D with its substrate eEF1A and leads to a slowdown of translation elongation [54]. It has been proposed that phosphorylation of eEF1D by CDK1 leads to reduced interaction of the catalytic subunit eEF1D with its substrate eEF1A-GDP causing a decrease in the guanine nucleotide exchange rate by the eEF1B complex followed by a lower level of active eEF1A-GTP during mitosis. Subsequently, less eEF1A-GTP is available for binding and delivering aa-tRNA to ribosomes, inducing a translational slowdown [54]. The above-mentioned data provide clear evidence of the importance of CDK1 in the regulation of the elongation step of translation in eukaryotes.
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 5 Conclusions  
In this study, Lamb waves' propagation in an FG nano copper single wafer and in an FG nano copper layered wafer are investigated analytically. The power series method is employed for solving the ordinary differential equations with variable coefficients. The modified transfer matrix method based on the power series solution is then proposed to solve the wave propagation problem in a multilayered wafer. Compared with the Lamb waves in a regular homogenous copper wafer, those in the FG nano copper lead to the decreased in phase velocity. It can be obtained from the theoretical results that the gradient coefficients can be measured by using the change of phase velocity of Lamb waves. For both a single layer structure and a multilayered structure, the findings show that the variation in the phase velocity in Mode 2 is the least obvious among the four modes considered in the analysis, whereas that in Mode 4 is the most obvious. Therefore, Mode 4 is recommended to be used in nondestructive evaluation. For a multilayered FG nano copper wafer, if the number of layers is greater than four, the dispersion curves of the Lamb waves in this structure tend to coincide with those in an effective homogeneous structure. It is suggested that Lamb waves' technique is not suitable for testing a multilayered FG nano copper wafer in which the number of sub-layers is larger than four. These results provide theoretical guidance for the nondestructive evaluation of FG nano layered structures.  
Conceptualization, X.C.; data curation, Y.H. and Y.N.; funding acquisition, Y.H., X.C. and Y.R., J.S.; methodology, X.C. and Y.R.; software, X.C. and Y.N.; supervision, X.C.; Writing--original draft, Y.H., X.C. and Y.R.; writing--review and editing, X.C. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Nos. 11972285, 11572244, 11872300). It is also supported by the Natural Science Foundation of Shannxi Province, China (No. 2021 JZ-47, 2021 JQ-467) and NSAF (No. U1630144) and the Youth Innovation Team of Shaanxi Universities.  
The authors declare no conflict of interest.
## 2 Methods
### Spatial-Spectral Cooperative Attention Module (SSCA)  
As shown in Figure 2, the spatial-spectral cooperative attention module utilizes a dual-branch parallel structure, composed of spectral and spatial attention branches. The input feature maps are processed by the spectral attention branch and the spatial attention branch, respectively, to obtain the spectral and spatial feature maps. Then, the two feature maps are concatenated to form a spatial-spectral enhanced feature map that fuses spatial and spectral information. At the same time, the spectral attention branch and the spatial attention branch generate the spectral weight map \(M_{s}(X)\) and the spatial weight map \(M_{r}(X)\) respectively. The two attention maps are element-wise multiplied to obtain the final spatial spectral attention heatmap. Finally, the spliced spatial-spectral enhanced feature map is fused with the feature map weighted by the attention heat map to output the final feature representation.  
Figure 1: The framework of MDSSANet, comprising the SSCA module (performs spatial-spectral joint extraction and guides affine parameters for adaptive normalization) and the DMF module (fuses normalized multi-scale features to output denoised maps for clean image reconstruction).  
Figure 2: Illustration of the spatial–spectral co-attention module.  
As shown in the following equation, this module enables the collaborative enhancement of spatial and spectral features.  
\[Out=cat(M_{r}(X)\odot Y_{1}+M_{s}(X)\odot Y_{2})\odot(M_{r}(X)\odot M_{s}(X)), \tag{1}\]  
where \(M_{r}(X)\) represents the spectral dimension weight matrix learned by the spectral attention branch, \(Y_{1}\) denotes the feature representation extracted by the spectral attention branch, \(M_{s}(X)\) represents the spatial attention map generated by the spatial attention mechanism, and \(Y_{2}\) denotes the spatial features extracted by the spatial attention branch.
# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 4 Experimental Results
### Results
We focused on finding the optimal value for the number of epochs through multiple experiments and set the number of epochs to 150. As a result, the main model took 1 h and 14 min, the ADDA model took 42 min by pretraining a CNN on the source domain to reduce the time, and the DANN model took 55 min. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**MNIST \(\rightarrow\) USPS**} \\ \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline
**Source only** & 0.714 & 0.709 & 0.712 \\ \hline
**DANN** & 0.901 & 0.895 & 0.899 \\ \hline
**ADDA** & 0.920 & 0.905 & 0.911 \\ \hline
**BiFLP-AdvDA** & **0.989** & **0.989** & **0.989** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adversarial domain adaptation for MNIST (target domain) to USPS (source domain) dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MNIST \(\rightarrow\) USPS** & **SVHN \(\rightarrow\) MNIST** & **EMNIST \(\rightarrow\) MNIST** \\ \hline
**Source only** & 0.601 & 0.489 & 0.752 \\ \hline
**DANN** & 0.771 & 0.739 & 0.984 \\ \hline
**ADDA** & 0.894 & 0.760 & 0.955 \\ \hline
**BiFLP-AdvDA** & **0.961** & **0.859** & **0.990** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy of adversarial domain adaptation between other models for target to source domain. Figure 7: Visualization of data points without domain adaptation and with domain adaptation. The qualitative results also show the superiority of bidirectional feature learning in a visual quality evaluation. Figure 7 shows a visualization of the experimental results when only the source domain was trained and tested, on the left, and when the entire model was trained and tested, on the right. Comparing the two, it can be seen that the proposed model achieved excellent results in domain adaptation. This can be attributed to the fact that the generated network reflected the actual distribution of the data well. The effect of the bidirectional feature transformation also strengthened the interdependence between the two domains. Taken together, these experimental results show that the use of generative models and bidirectional feature learning can significantly improve the performance compared to various existing domain adaptation studies. It is an effective way to achieve better model adaptation capabilities. This suggests that using interaction between the generative network and the feature transformation process is a successful approach to reduce distributional differences in the data and enhance interdependence between the two domains.
# Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations
## 5 Experiments
### Scenario 3  
As depicted in Figure 9, \(Veh[1000]\), \(Veh[1001]\), and \(Veh[1002]\) drove on the left side of the reference direction on \(Road[2291]\). \(Veh[1000]\) was the foremost vehicle, traveling at a speed of 9 m/s in lane 3. \(Veh[1001]\) was in lane 2, approximately 40 m behind \([PERSON][1000]\). \(Veh[1002]\) was in the rear position, rapidly approaching \(Veh[1001]\) with the speed of 12 m/s in lane 3. After a short period, \(Veh[1000]\) believed that its speed might be affected by \(Veh[1001]\) in the adjacent lane and reminded \(Veh[1001]\) to change its current state. \(Veh[1001]\) received the reminder and attempted to increase the speed \(b_{limit}^{cs}\); however, the vehicle reached its speed limit. At approximately 8 s, \([PERSON][1002]\) approached \([PERSON][1000]\) and reminded \([PERSON][1000]\) to change its current state. Similar to \(Veh[1001]\), \([PERSON][1000]\) was unable to increase its speed despite receiving the signal from the rear vehicle. Consequently, \(Veh[1002]\) had to change lanes. As only two lanes existed in this road section, \(Veh[1002]\) could only select the adjacent lane 3 as the target lane. In this case, \(Veh[1002]\) reminded the rear vehicle \(Veh[1001]\) to cooperate for changing lanes successfully. Although \([PERSON][1001]\) agreed to cooperate, it was extremely close to \(Veh[1002]\). Therefore, \(Veh[1001]\) chose to slow down using \(b_{headway}^{cs}\) and \([PERSON][1002]\) merged into the lane from the front. After \(Veh[1002]\) changed the lane, \([PERSON][1001]\) entered the dominance motivation, adjusted speed appropriately, and returned to the default state \(m_{default}\). Figure 10 depicts the screenshots of the top views of the four scenarios indicating the entire process.  
Figure 8: Scenario 2: Top view screenshots of handling the unsignalized traffic junctions. **(a)** Initially, \(Veh[1000]\) and \(Veh[1001]\) are on the same road, whereas \(Veh[1002]\) is on another road. \(Veh[1000]\) and \(Veh[1002]\) share the same destination. The green arrows represent the direction of movement for the scenario vehicles. **(b)**\(Veh[1000]\) reaches the junction before \(Veh[1002]\). **(c)**\(Veh[1002]\) waits as \(Veh[1000]\) is passing the junction. **(d)**\(Veh[1001]\) waits while \(Veh[1002]\) is passing the junction. **(e)** All the three scenario vehicles pass the junction.  
Figure 9: Scenario 3: Cooperation between vehicles on rural roads. From **(a–f)**, the six figure panels represent motivation, action, lane ID, longitudinal displacement, speed, and lateral displacement versus time of the three scenario vehicles. The blue solid, orange dashed, and green dash-dotted lines represent \(Veh[1000]\), \(Veh[1001]\), \(Veh[1002]\), respectively.
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
## 3 Results
### RecG Is Mechanistically Suited for Fork Reversal and Fork Restoration  
Previously it has been shown that: (i) both RecG\({}_{Eco}\) and RecG preferentially bind branched structures, such as HJs [31; 45; 56]; and (ii) RecG\({}_{Eco}\) preferentially binds to stalled forks and catalyzes the formation of a HJ-like DNA structure (fork reversal), and upon binding to a reversed fork catalyzes its migration in the opposite direction to yield a restored fork [10; 11; 30; 34]. To test whether RecG works in a similar fashion to the well-characterized RecG\({}_{Eco}\) protein, different substrates were tested. Except the HJ-J4 DNA, the substrates have heterologous arms to prevent their spontaneous branch migration. HJ-J4 contains a central 12-bp homologous sequence so it can spontaneously migrate.  
In the presence of ATP (5 mM), RecG (15 to 30 nM) remodeled a HJ, a 3'-fork (a replication fork with a fully synthesized leading-strand and no synthesis in the lagging-strand), and a 5'-fork (a replication fork with a fully synthesized lagging-strand and no synthesis in the leading-strand) with apparent similar efficiency, yielding a flayed DNA product (an unreplicated fork) that was not further processed (Figure 2, lanes 1-3 and 7-12). Indeed, a flayed DNA substrate was not unwound by RecG to release the free labelled ssDNA strand (Figure 2, lanes 13-15). When a Y-fork (a replication fork with fully synthesized leading- and lagging-strands) with no complementary arms was tested (Figure 2, lanes 4-6), RecG mediated fork reversal, yielding a flapped and the radiolabeled nascent leading-strand, because the nascent strands are heterologous and cannot anneal, to avoid spontaneous branch migration (Figure 2, lanes 5-6). The indicated substrates,intermediates or products were run in parallel (Figure 2, lanes 1, 4, 7, 10, 13, 15 and 16) to infer their mobility.  
From these results, we conclude that: (i) RecG, as \(\text{RecG}_{Eo}\)[10; 30; 34], may catalyze both fork regression and fork restoration (Figure 2, lanes 1-12); and (ii) RecG may process partially replicated forks (3 '- and 5 'forks), which are isomers of a D-loop, with similar efficiency. However, not all activities associated with \(\text{RecG}_{Eo}\) are observed with RecG. For example, \(\text{RecG}_{Eo}\) unwinds R-loop structures, but this activity is not seen with RecG [40].
## 5 Experimental Results and Analysis
### Experiments Using the Multi-Object Tracking Algorithm
In vehicle 22 of frame 27 of the original algorithm, there is a situation where multiple vehicles are present in one frame, and some vehicles could not be detected as they were blocked by traffic lights, so the detection effect is not good. The algorithm presented in this paper does not have a situation where multiple vehicles are present in one frame, and vehicles can be correctly detected and tracked in images with severe occlusion and are less affected by the weather environment. However, the light in the room is reflected to the upper left corner, which seriously interferes with the detection of vehicles, there are obstacles blocking the vehicles in the left corner of Figures 17 and 18, and, in frame 27, only the left rear wheel of the vehicle in the upper right corner was exposed, and its area was small. Hence, neither the original algorithm nor the proposed algorithm could detect these vehicles.