esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link Quality Estimation Routing Protocol Design
### Dynamic Window Concept (\(m,k\))  
(\(m,k\)) is firstly proposed in [21], which is a sliding window with length \(k\) to record historical states of data. It is designated \(\Sigma\) as the alphabet {0,1}. \(\Sigma^{+}\) denotes the sequences of length greater than 0. \(\Sigma^{k}\) (\(k>1\)) denotes the sequences of length \(k\). For a given sequence \(\omega\), the notation \(l(\omega)\)=\(k\) is to denote the length of \(\omega\in\Sigma^{+}\). Additionally, \(l^{\alpha}(\omega)\)=\(m\), \(\alpha\in\Sigma\) denotes the number of \(\alpha\) in \(\omega\). We call a sequence \(\omega\in\Sigma^{k}\) a \(k\)-sequence. For \(i>1\), \(\omega(i)\) denotes the \(i\)th element of \(\omega\).  
Normally, a binary \(k\)-sequence \(\omega\)\((\omega(k),\ldots,\omega(2),\omega(1))\) is used to denote the context in the window.  
A word of \(k\) bits orders from the most recent to the oldest data in which each bit keeps memory of whether the data is transmitted unsuccessfully (bit=0) or successfully (bit=1). In this paper, the leftmost bit represents the oldest historical information. Each new transmitted data causes a shift of all the bits towards left, the leftmost exits from the word and is no longer considered, while the rightmost will be 1 if the packet has been transmitted successfully or 0 otherwise.  
So in the most recent window \(k\), \(m=l^{\alpha}(\omega)\), is the number of data transmitted successfully. The quality of link can be gotten by \(p=\frac{m}{k}\). The value \(p\) can be updated by the \(k\)-sequence. The current link quality can be deduced from the value \(p\). In the minimum hop field routing protocol, the hop is selected randomly from forward hop set. LQER will make decision according to link quality.  
**Example**: Given a \(\omega\) given with most current state 11100110, \(l(\omega)\)=\(k\)=8 and \(m=l^{\alpha}(\omega)=5\). If next packet is transmitted successfully, the sequence will be switched to 11001101, otherwise, 11001100.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 3 Model and Algorithm
### The Algorithm of RBF NN based on GA  
Genetic algorithm is an adaptive and global probabilistic search algorithm, which simulates the criterion of survival of the fittest. The genetic algorithm mainly includes five basic steps: Coding, generating initial population, setting function of the fitness, designing the genetic operator, and determining the operating parameters. The process of GA is shown in Figure 6. The steps are as follows:  
(1) Random numbers are generated by a random number generator, which is set to the initial set of RBF NN. At the same time, the GA run parameters are set.  
(2) Define the fitness function, judge the individual's ability to adapt to the environment through the fitness function, and set the individual fitness value \(f\).  
(3) Evaluate the fitness value. If the fitness value \(f<\delta\) (\(\delta\) is the set error value), the requirement is met, training is stopped, and the output parameter is sent to the RBF NN. These parameters are optimal.  
(4) If the fitness value \(f\geq\delta\), the selection, intersection, and mutation operations are performed to generate a new individual, that is to say, the parameters are updated, and the fitness value is calculated again.  
(5) It is judged as to whether the fitness value satisfies the condition. If it is not satisfied, step (4) is repeated, and if satisfied, the optimal solution is delivered.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 4 Results and Discussion  
### Experiment Results  
In the experiment, B15 (686 nm) was selected as the reference image to perform registration for all other bands. Several of the most common bands were selected for registration accuracy analysis due to the large number of spectral bands. These bands included: B03 (490 nm) and B14 (670 nm), B08 (560 nm) and B28 (880 nm), and B10 (596 nm) and B30 (910 nm). The two bands were overlaid in the form of a checkerboard to verify the registration algorithm. Figure 9 displays the registration results using the proposed method. Visually, misalignment was not identified for rivers, roads, fields, and other land cover features between bands. The overall registration result was satisfactory. Figure 10 further displays the true color (R: B14, G: B08, B: B03) and false-color (R: B28, G: B08, B: B03) composite images after registration.  
Figure 9: _Cont._  
Figure 9: Local comparison of registration results between different bands. (**a**) OHS–Arizona, USA; (**b**) OHS–Guangxi, China; (**c**) OHS–Xinjiang, China.  
Figure 10: True and false-color composite images after registration. Left column: true color (R: B14, G: B08, B: B03), Right column: false-color (R: B28, G: B08, B: B03). (**a**) OHS–Arizona, USA; (**b**) OHS–Guangxi, China; (**c**) OHS–Xinjiang, China.
# Effect of Seat Condition on Abdominal Injuries to Vehicle Occupants in Frontal Impact Accidents
## 3 Results  
Dynamic behaviors of the AM50 human model under two considered seat conditions are shown in Figure 10. Dynamic movements of the AM50 human model were similar for both seat conditions. Seat angle of the lean forward condition (\(-4^{\circ}\)) was smaller than that of the vertical movement condition (\(16^{\circ}\)) at 90 ms. Dynamic behaviors of the lap belt in the two seat conditions are shown in Figures 11 and 12. In the vertical movement condition, the lap belt detached from the right iliac crest at 80 ms (Figure 12a), but did not detach from the left iliac crest during impact. In the lean forward movement condition, the lap belt detached from the left iliac crest at 75 ms (Figure 12b) and then detached from the right iliac crest at 90 ms (Figure 11b). Thus, the lap belt detached from both iliac crests during the lean forward movement.  
Figure 10: Dynamic behaviors of AM50 dummies under two seat conditions: (**a**) vertical seat movement and (**b**) lean forward seat movement.  
Figure 9: Tensile force activated on various restraining belts.  
Figure 11: Dynamic behavior of the lap belt under two seat conditions (view from left): (**a**) vertical seat movement and (**b**) lean forward seat movement.  
Figure 12: Dynamic behavior of the lap belt under two seat conditions (view from right): (**a**) vertical seat movement and (**b**) lean forward seat movement.  
The time-series of seat height at the center of the seat, seat angle, and knee-panel displacement under the two seat conditions are shown in Figure 13. Maximum knee-panel displacement occurred at 75 ms for both vertical movement and lean forward movement (Figure 13c), which occurred 10 ms and 25 ms prior to the maximum seat-height displacement at 85 ms and 100 ms, respectively (Figure 13a). For the lean forward movement condition, the seat angle of 16\({}^{\circ}\) at 28 ms decreased to \(-4^{\circ}\) at 102 ms (Figure 13b). For the vertical movement condition, the seat angle remained stable at 16\({}^{\circ}\) during impact.  
Time histories of tensile forces on the retractor belt, shoulder belt, and lap belt are shown in Figure 14. Tensile forces of the retractor belt under two seat conditions were almost consistent (3.3 kN after 51 ms) because the force limiter in the retractor was regulated to 3.3 kN in the three-point seatbelt model. Tensile forces of the shoulder belt in two seat conditions were also almost equal (between 4.2 kN and 4.7 kN after 51 ms) because they were connected to the retractor belt. Tensile forces of the lap belt in two seat conditions were almost consistent prior to 75 ms. Furthermore, maximum tensile forces of the lap belt were similar under the two seat conditions: 9.0 kN for vertical movement and 8.9 kN for the lean forward movement.  
Figure 13: Time histories of (**a**) seat height, (**b**) seat angle, and (**c**) knee-panel displacement under two seat conditions.
## 2 Results
### MgPDI2 Is Involved in Parasitism  
To analyze whether _MgPDI2_ plays a role in _M. graminicola_ parasitism, in vitro RNAi of _MgPDI2_ was performed. The qRT-PCR (Figure 5A) results showed a decrease in the transcript abundance of _MgPDI2_ in the J2s that were soaked with _MgPDI2_ dsRNA. Then, rice roots were infected by J2s that were soaked in dsRNA targeting _MgPDI2_ or _GFP_ and the nematode multiplication factor (MF) ((number of egg masses \(\times\) number of eggs per egg mass) \(\div\) nematode inoculum level = MF) at 15 dpi was determined. Our analysis showed that the MF was reduced significantly in plants infected with juveniles treated with dsRNA against _MgPDI2_ (MF = 6.69) compared to _GFP_ (MF = 11.37) (Figure 5B).  
MgPDI2 Expression is Induced by \(H_{2}\)O\({}_{2}\) and Increases \(H_{2}\)O\({}_{2}\) Tolerance  
qRT-PCR was used to analyze the expression of _MgPDI2_ in response to H\({}_{2}\)O\({}_{2}\). A significant increase in transcript abundance of _MgPDI2_ was found in juveniles that were exposed to 40 mM H\({}_{2}\)O\({}_{2}\) compared to 0 mM H\({}_{2}\)O\({}_{2}\) for 30 min (Figure 6A). We examined the mortality rate of _MgPDI2_ or _GFP_ dsRNA-treated J2s after soaking them in different concentrations (0, 20, 40 mM) of H\({}_{2}\)O\({}_{2}\) (Figure 6B). We found that a significantly lower percentage of J2s with silenced _MgPDI2_ survived after exposure to 20 mM and 40 mM H\({}_{2}\)O\({}_{2}\) compared to _GFP_-silenced controls.  
Figure 5: Effect of _MgPDI2_ expression on rice susceptibility to _M. graminicola_ infection. (**A**) _MgPDI2_ expression in nematode after treatment with dsRNA targeting _MgPDI2_. Asterisks indicate significant differences based on [PERSON]’s test (*: \(p\) < 0.05; **: \(p\) < 0.01). (**B**) Average numbers of knots and the MF values at 15 dpi.  
Figure 6: _MgPDI2_ expression is triggered by H\({}_{2}\)O\({}_{2}\) and increases H\({}_{2}\)O\({}_{2}\) tolerance. (**A**) The relative mRNA expression levels of _MgPDI2_ in response to the H\({}_{2}\)O\({}_{2}\) stress. (**B**) Effect of _MgPDI2_ silencing on H\({}_{2}\)O\({}_{2}\) stress tolerance. Asterisks indicate significant differences based on [PERSON]’s test (*: \(p\) < 0.05; **: \(p\) < 0.01).
## 1 Introduction
It is also labor-intensive and time-consuming to collect ground images through radar- and camera-equipped vehicles, which are still inefficient methods. However, it is a feasible way to classify manhole covers after obtaining ground images by UAV. How to realize the classification of manhole covers is the main problem to be solved in this paper. Classification of manhole covers is mainly realized by recognizing the text on the cover; however, the manhole covers acquired from aerial images often lose the detail information of the text on the cover, so directly classifying manhole covers by target detection is not a feasible method. Image super-resolution reconstruction methods were designed to enhance the detail performance of low-resolution images. By recovering high-frequency detail information from low-resolution images, the image becomes clearer and has a higher detail resolution. These methods are widely used in the fields of aeronautics, medicine, and engineering [14; 15]. In the field of aviation, a variety of image super-resolution reconstruction algorithms had been proposed [16; 17; 18; 19; 20]. [PERSON] et al. [18] proposed a super-resolution reconstruction strategy based on self-attention generative adversarial networks, which improves the details of remote sensing images by adding self-attention modules. [PERSON] et al. [21] proposed an improved enhanced super-resolution generative adversarial network (IESRGAN) based on enhanced U-Net structure, which is used to perform a four-fold scale detail reconstruction of LR images using NaSC-TG2 remote sensing images. The above method can improve the detail of aerial images well. Therefore, it becomes possible to recover the details of manhole cover text by reconstructing the manhole cover from aerial photographs through super-resolution reconstruction network. Based on the above analysis, this paper proposed a method to localize and classify manhole covers based on super-resolution generative adversarial network reconstruction of UAV aerial images. Initially, the YOLOv8 object detection network was employed to localize manhole covers. Post-localization, these covers were segmented from the captured images. Further text localization was conducted on the segmented images using YOLOv8. This was followed by the application of the SRGAN super-resolution network to enhance the image quality of the manhole covers. The final step involved classifying the manhole covers using the VGG16_BN image classification network. The method used in this paper can greatly improve the efficiency of manhole cover survey and reduce the loss of manpower and material resources.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 4 Experimental Evaluation
### Data Visualization  
The deep learning model requires several data analysis and data preprocessing operations to build a secure intrusion detection model for the SDN data plane. Figure 3 illustrates the data distribution of the TTL feature for different attacks in the enriched NSL-KDD dataset.  
From the analysis of Figure 3, it is recognized that the TTL values are comparatively low for the normal class that is legitimate traffic flow compared to the various malicious network traffic in the SDN. TTL criteriaal modeling is based on the work in [30]. The TTL value is described for normal and attack behaviors with the unit as a number of hops passed by the packets before discard, irrespective of time during the IP packet transmission in the SDN rather than the Domain Name System (DNS), which indicates the TTL value in seconds. In the SDN environment, the default value of TTL is set to 64, and it is near integer values for normal traffic. Hence, the range from 50 to 80 is assigned for the normal traffic in this work. TTL is based on the number of hops and routers, which indicates the count in its traversing path. According to Figure 3, the malicious traffic, including the SDN attacks, exists above 80, indicating that the packets traverse through a higher number of hops or routers before being discarded. As a result, in contrast to the default TTL range, traversing through numerous hops or routers implies the occurrence of malicious activity.  
In the enriched NSL-KDD dataset, the correlation between the features of detecting legitimate and malicious traffic flows in the SDN is illustrated in Figure 4. The need for feature selection is proved in Figure 4 due to the existence of an uncorrelated feature set in the NSL-KDD dataset, which misleads the attack detection. Moreover, to illustrate the significance of each set of correlated features on the discrimination of normal and malicious traffic, Figure 4 plots the correlative relationships among the features, proved through the proposed feature selection mechanism.  
To understand the data distribution, the visualization of high-dimensional data provides the guidelines for initiating the required data handling operations. Figure 5 shows that the attacks in the enriched NSL-KDD dataset are not spatially and temporally separated. In conclusion, traditional machine learning algorithms are unsuitable for dealing with this data.  
Figure 3: TTL Distribution over SDN attacks.  
Figure 4: Feature Correlation in Enriched NSL-KDD Dataset.  
Figure 5: _Cont._
###### Abstract  
The use of 360\({}^{\circ}\) omnidirectional images has occurred widely in areas where comprehensive visual information is required due to their large visual field coverage. However, many extant convolutional neural networks based on 360\({}^{\circ}\) omnidirectional images have not performed well in computer vision tasks. This occurs because 360\({}^{\circ}\) omnidirectional images are processed into plane images by equirectangular projection, which generates discontinuities at the edges and can result in serious distortion. At present, most methods to alleviate these problems are based on multi-projection and resampling, which can result in huge computational overhead. Therefore, a novel edge continuity distortion-aware block (ECDAB) for 360\({}^{\circ}\) omnidirectional images is proposed here, which prevents the discontinuity of edges and distortion by recombining and segmenting features. To further improve the performance of the network, a novel convolutional row-column attention block (CRCAB) is also proposed. CRCAB captures row-to-row and column-to-column dependencies to aggregate global information, enabling stronger representation of the extracted features. Moreover, to reduce the memory overhead of CRCAB, we propose an improved convolutional row-column attention block (ICRCAB), which can adjust the number of vectors in the row-column direction. Finally, to verify the effectiveness of the proposed networks, we conducted experiments on both traditional images and 360\({}^{\circ}\) omnidirectional image datasets. The experimental results demonstrated that better performance than for the baseline model was obtained by the network using ECDAB or CRCAB.  
Keywords:computer vision; object detection; 360\({}^{\circ}\) omnidirectional images; row-column attention mechanism +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of Surfactant Type
The decrease of the strength improvement factor is higher for the surfactant with larger molecule size (Amber 2001, IF decreases from 1.33 to 0.81), which may be explained by two factors: (i) surfactants with larger molecules potentially form micelles for lower surfactants concentrations, making the occurrence of cementitious reactions more difficult; (ii) surfactants with larger molecules adsorbed on the surface of solid particles may prevent the establishment of some cementitious bonds, thus promoting a solid matrix with less strength. Nevertheless, more tests should be performed for other surfactants and concentrations in order to deeper understand the surfactant effect over time.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 1 Introduction
Typical quantum gate fidelity requirements pose a significant challenge to the RFIC designers [10; 30; 31], who should therefore be aware of what quantum gate fidelity is, in order to properly account for it alongside other design specifications. In addition, being aware of quantum gate fidelity also facilitates discussion and information exchange between RFIC designers and physicists. Collaboration between these two professional figures is of paramount importance for successfully addressing the fabrication of quantum microprocessors. Nevertheless, the literature typically addresses quantum gate fidelity by definition, without providing a rationalized explanation that aids understanding. Moreover, it employs advanced abstract mathematical formalism that does not help electronics engineers grasp the concept. This paper intends to contribute to overcoming these obstacles. To achieve this, Section 2 of this paper introduces quantum gate fidelity by leveraging the concept of fidelity as found in information theory, which is generally familiar to electronics engineers. Moreover, Section 3 introduces an analogy between a quantum gate and a transmission channel, thus linking the input and output states of the quantum gate to, respectively, the transmitted and received signals on the channel. Section 4 reports a step-by-step, detailed mathematical proof of a relatively well-known formula for quantum gate fidelity [32]. This formula is particularly relevant for RFIC designers because it reduces the calculation of the fidelity to matrix analysis, a domain familiar to electronics engineers. Regrettably, in [32], as it often occurs in other papers on fidelity, see, for instance, refs. [33; 34; 35], the mathematics is often presented using an abstract formalism characteristic of theoretical physics. This makes it difficult for electronics engineers to interpret the formulation of fidelity, thereby discouraging its use, despite fidelity being a relevant design specification. In contrast, the mathematical [PERSON] proposed in Section 4 adopts a more accessible mathematical formalism, aligning with the mathematical background of electronics engineers. It is the authors' opinion that addressing mathematical issues in this way helps to demystify concepts that may remain otherwise obscure. With the aim of further clarifying the relevance of the formula above for RFIC designers, Section 5 discusses brief case studies that exemplify how the RFIC non-idealities impact quantum gate fidelity, and Section 6 addresses the relationship between quantum gate fidelity and the energy efficiency of the quantum microprocessor. Section 7 discusses quantum gate fidelity in the more involved case of mixed states[PERSON] Eventually, Section 8 closes this paper by summarizing the obtained results and drawing some conclusions.
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 4 Evaluation and Analysis
### Experimental Analysis
#### 4.2.3 Ablation Experiment

Table 3: NDCG@10 performance under the method of calculating transfer probability threshold. uses the K-means++ clustering algorithm to determine the initial clustering center of the fuzzy C-means clustering algorithm and then uses the fuzzy C-means clustering algorithm for clustering, which can achieve better clustering performance compared to other models. Therefore, it always has the best performance compared to other models, indicating that the clustering algorithm proposed in DCFECF has better clustering performance than the K-means clustering algorithm. The spatial complexity of the K-means clustering algorithm is O(d(N + M + K)), while the spatial complexity of the fuzzy C-means clustering algorithm optimized based on the K-means++ clustering algorithm is O(d(N + M)K), where d is the vector embedding dimension, N + M is the total number of nodes, and K is the number of clusters. In practical large-scale data scenarios, N + M is usually much larger than K, indicating that the improved clustering effect also increases some spatial overhead. (3) The Ablation Experiment of the Time Factor  
We deconstruct the structure of the DCFECF model by not using the method of obtaining the transition probability threshold but only using the K-means clustering algorithm to construct a global interaction graph and introducing a time factor. The model that introduces the time factor \(TF1\) is the DCFECF1 model, the model that introduces the time factor \(TF2\) is DCFECF2, and the model that introduces the time factor \(TF3\) is DCFECF3. The experimental results are shown in Tables 6 and 7. From these tables, it can be seen that the DCFECF model has better performance than the DCCF model, indicating that introducing a time factor can improve the predictive ability of the model. In addition, among the three DCFECF models that introduced time factors, the DCFECF3 model achieved the best performance on all three datasets, indicating that the improvement of the time factor expression in this paper is effective, and it is reasonable to consider adding the item scoring density factor to the time factor, which can further improve the predictive ability of the model.
# CFD Analysis on the Heat Dissipation of a Dry-Lubricated Gear Stage
## 2 Methods and Materials
### Numerical Modeling and Calculation
#### 2.3.4 Turbulence Model  
Intense interaction between rotating gears and fluid results in a turbulent fluid flow, especially at high circumferential speeds. For the numerical modeling of fluid flow in gearboxes, the k-\(\varepsilon\) turbulence model has proven to be suitable [3; 34]. It describes turbulence by a velocity and a length scale and is applied in this study. The k-\(\varepsilon\) turbulence model is further complemented by renormalization operations (renormalization group, RNG) for enhanced performance regarding the modeling of complex turbulent flow [35].