esa-sceva/satcom-chunk-collection huggingface dataset:
# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 3 Method
### Lsdm
#### 3.3.2 Construction of a Correlation Matrix  
The adjacency matrix describes the correlations between nodes and guides the propagation of information between nodes in GCN. Typically, the adjacency matrix is predefined. However, in this study, we adopted a data-driven approach, following the method proposed by [12], to construct the adjacency matrix. Specifically, we constructed the adjacency matrix in the form of conditional probabilities. Firstly, we calculated the occurrences of label pairs in the training set to obtain the matrix \(\mathbf{D}\in\mathbb{R}^{C\times C}\), where \(C\) represents the number of label categories and \(D_{ij}\) denotes the number of occurrences of label \(i\) and label \(j\) appearing together. Then, based on the matrix \(D\), we can derive the conditional probability matrix:  
\[\mathbf{P}_{ij}=\mathbf{D}_{ij}/\mathbf{N}_{j}, \tag{22}\]  
where \(N_{j}\) represents the total occurrences of label \(j\) in the training set, and \(P_{ij}=P(L_{i}|L_{j})\) represents the probability of label \(i\) occurring when label \(j\) is present.  
The above method for obtaining the adjacency matrix may encounter two issues. Firstly, the data may exhibit a long-tail distribution. Secondly, the adjacency matrix computed from the training set may suffer from overfitting, affecting its generalization ability. To address these issues, we propose to binarize the correlation \(P\) and filter out noise using a threshold value \(\tau\). Therefore, the adjacency matrix can be represented as follows:  
\[A_{ij}=\begin{cases}0,&\text{if }\mathbf{P}_{ij}<\tau\\ 1,&\text{if }\mathbf{P}_{ij}\geq\tau\end{cases}. \tag{23}\]  
However, binarization can introduce the issue of over-smoothing. To mitigate this, we can employ a reweighting scheme, which is represented as follows:  
\[\mathbf{A}^{\prime}_{ij}=\begin{cases}p/\sum_{j=1}^{C}\mathbf{A}_{ij},&\text{if }i\
eq j\\ 1-p,&\text{if }i=j\end{cases}, \tag{24}\]  
where \(A^{\prime}\) represents the reweighted adjacency matrix, and \(p\) denotes the allocation weight to be set. By incorporating the weighting operation, the reweighted adjacency matrix retains both the information of the individual nodes and the information from the neighboring nodes, effectively addressing the issue of over-smoothing.
# Ambient LED Light Noise Reduction Using Adaptive Differential Equalization in Li-Fi Wireless Link
## 1 Introduction
In addition, only incandescent and fluorescent lamps were employed as ambient light sources, and no other LED light sources were used. In [24], an algorithm, which optimizes the SNR in the visible light communication system, was implemented assuming a multipass channel as an optical wireless channel. It was verified through simulation. Unfortunately, the noise generated by the ambient light source was not taken into account. [PERSON] proposed a technique to reduce the noise generated by the ambient light source by implementing the average voltage tracking technique as a circuit [25]. However, only fluorescent lamps were used as an ambient light source except for other LED lamps. The average voltage tracking method was verified using an NRZ-OOK signal of 1 Mb/s. In this paper, we propose a technique to increase the SNR of a received signal by reducing the noises generated from the ambient light sources that are simultaneously introduced during optical wireless transmission in a Li-Fi link based on white LED. As two ambient light sources, a fluorescent lamp and dimmable LED bulb light are used. A non-return to zero-on off keying (NRZ-OOK) signal is used as data for the optical wireless transmission experiment on the Li-Fi link. The noises from the above-mentioned light sources used in the experiment are shot noise with Poisson probability function and interference noise in the form of a composite sinusoidal function that changes with time. The proposed technique attenuates the magnitude of the noise by using the fact that the derivative function of the user data with digital waveform is very different from that of the noise with analog waveform. Furthermore, they change each other differently with time. In other words, in the case of a digital signal, its derivative value is very small in a range having an intensity of 1 or 0, whereas, in the case of an analog signal, its derivative value varies randomly. Moreover, the proposed technique increases the efficiency of reducing shot noise by using the Poisson function as a weight function. The proposed technique does not use matched RF filters and is implemented based on digital signal processing, so the intensity of the signal does not decrease due to insertion loss. In addition, since it is not necessary to know the header information of the input signal in advance to reduce the noise of the ambient light source, it can be applied to networks having various protocols. We implement the experimental optical wireless link for Li-Fi in order to verify the proposed technique. The SNR, bit error rate (BER), and eye diagram of the NRZ-OOKsignal are measured to find if the noises from the surrounding light sources are reduced using the proposed technique. Here, we call the proposed technique as adaptive differential equalization (ADE).
# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
### Cervical Cancer Screening Rates  
It can be seen that a certain form of gynecological examination is performed during cervical cancer screening. Such a diagnostic procedure can be painful [11; 12] and unpleasant [13] for a patient. The unpleasant nature of gynecological examination can cause delay and avoidance [14], which can prevent early diagnosis. Furthermore, a lack of proper public health policies in developing countries can cause low cervical cancer screening rates. Such circumstances are responsible for 18 times higher mortality [15]. In 9 out of 10 cervical, cancer-related deaths occur in low-income countries [16]. Due to the fact that early-stage cervical cancers have promising survival rates that can achieve 90% in a 5-year period [17], an increase in cervical cancer screening rates presents an absolute imperative. Cervical cancer screening rates are different for different countries, ranging from high in developed countries [18] to extremely low in developing countries. In the next few paragraphs, a brief overview of cervical cancer screening rates is presented. The authors in [19] have analyzed the data collected from women living in Olmsted County, Minnesota. The study was performed on data for each year from 2005 (47,203 women) to 2016 (49,510 women). The results show that in 2016, 64.5% of women between 30 and 65 were regular with cervical cancer screenings. Furthermore, 60.8% of women have performed combined Papanicolau (Pap) and HPV tests. The authors in [20] have performed a study that uses data from commercial insurance companies in the United States from 2003 to 2014. From the presented results it can be seen that less than 5% of women between 18 and 20 years of age are attending regular cervical cancer screenings. For women between 21 and 29, the number is around 16%. The highest rate of cervical cancer screening is in the age group between 30 and 39, where 44% of women attend regular cervical cancer screenings. For the age group 40-49 and 50-65 the rates are 38% and 28%, respectively. The rates of cervical cancer screening are significantly influenced by socio-economic circumstances, as well as ethnicity [2]. The authors in [21] have shown that women with unstable socio-economic backgrounds tend to skip PAP test. The lowest rate of Pap test can be noticed for the case of women without a usual source of healthcare and uninsured women, with rates of 65.1% and 63.8%, respectively. Furthermore, the study has shown that Asian (75.8%) and Hispanic (78.6%) women have lower cervical cancer screening rates than non-Hispanic White (83.7%) and Black women (85.3%). The low rate of cervical cancer and HPV screening can be particularly emphasized in middle and low-income countries. From the study presented in [22], it can be seen that the highest rate of cervical cancer screenings in middle- and low-income countries is achieved in the Caribbean area and Latin America (median 84.6%). The lowest rates of cervical cancer screenings are achieved in sub-Saharan Africa with a median of 16.9%.
# Steering Pull Model and Its Sensitivity Analysis
## 3 Steering System Model
### Rack and Pinion Gear Model  
A rack and pinion gear is connected to the knuckle arm through the tie rod. It transmits tire pulling forces to the universal joint. Since steering motions usually include repetitive direction changes, backlashes within the steering system should be avoided. Furthermore, due to these direction changes, uneven wear can easily occur in the rack teeth. In order to prevent these undesirable phenomena, a yoke spring is introduced in the rack and pinion gear. A plunger is placed on the yoke spring and against the rack gear while a yokenut is placed under the yoke spring [17]. The compressive force of the yoke spring is controlled by tightening the yokenut, and by this compressive force, the backlash of the rack and pinion gear can be prevented. On the other hand, the compressive force can also cause a friction force between the rack and the plunger, which may have an influence on the steering pull. In this study, Coulomb friction generated between the rack and the plunger is also considered. A rack and pinion gear can be modeled by the following Equation (13).  
\[M_{R}\bar{x}_{R}+B_{R}\Big{(}\dot{x}_{R}-\dot{x}_{U}\Big{)}+K_{R}(x_{R}-x_{U} )=\frac{T_{U}}{R_{pn}}-F_{F}\cdot sgn(\dot{x}_{U}),\]  
\[x_{U}=R_{pn}\theta_{U},\;F_{R}=K_{R}(x_{U}-x_{R}) \tag{13}\]  
where \(M_{R}\) = rack mass, \(x_{R}\) = rack displacement, \(x_{U}\) = displacement by rotation of universal joint, \(B_{R}\) = damping coefficient of rack, \(K_{R}\) = stiffness of rack, \(T_{U}\) = universal joint torque, \(R_{pn}\) = radius of pinion, \(F_{F}\) = Coulomb friction force of rack, \(\theta_{U}\) = universal joint angle, \(F_{R}\) = rack force.
###### Abstract  
The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10\({}^{\circ}\) to 40\({}^{\circ}\) and near river channels.  
Zhaotong area; landslide disasters; influencing factors; distribution pattern; Google Earth; statistical analysis 2024 14, 5093. [[https://doi.org/10.3390/app14125093](https://doi.org/10.3390/app14125093)]([https://doi.org/10.3390/app14125093](https://doi.org/10.3390/app14125093))
## 5 Features and Data Preparation
### Variable's Information and Features Analysis
#### 5.2.1 Driver Effect in the Driving Environment Classification  
The classifier should be robust enough so that accurate classification can still be possible for different drivers. Although different driving styles may affect the dynamics parameters of the vehicle such as longitudinal speed, lateral jerk, and braking frequency and intensity, the system must assess these differences and correctly classify the current driving environment based on the combined behavior of the available features. To include the effect of different drivers as a noise input to the system, the tests carried out for this work included eight different drivers.
## 4 BILSTM-Transformer Based Bems Model
### Offline Training Based on the BiLSTM-Transformer System Architecture
#### 4.3.4 Network Structure and Hyperparameter Settings  
To train the BiLSTM-Transformer model, we used the Adam optimizer with a learning rate set to 0.001, keeping other parameters at their default values. After comparing forecasting accuracy and network complexity, the model's structure adopts four stacked attention-BiLSTM modules. In the Transformer layer, the number of heads in the multi-head attention mechanism is set to four to fully capture global dependencies across different features, while the Transformer encoder layer is set to one to reduce computational overhead and improve modeling efficiency. The BiLSTM layer contains 64 hidden nodes. Experiments indicate that further increasing the network depth adds complexity with limited improvements in forecasting accuracy and significantly increases training time.  
Additionally, the model's time step is set to five, and the output layer is a fully connected layer. Mean-squared error (MSE) is used as the loss function to measure forecasting error (a regularization term for the microgrid optimization based on cognitive control is discussed in Section 4.5). With these hyperparameter settings, the model achieves optimal forecasting accuracy while maintaining a reasonable training time, avoiding overfitting due to excessive network depth.
## 2 Materials and Methods
### Data Sources and Processing  
(1) Baidu Migration Big Data  
This study utilized Baidu migration data. The migration data are gathered at the administrative unit level, offering a high temporal resolution, with data points collected on a daily basis \"[[https://qianxi.baidu.com](https://qianxi.baidu.com)]([https://qianxi.baidu.com](https://qianxi.baidu.com)) (accessed on 17 December 2023)\". Baidu's Location-Based Services (LBS) open platform provides positioning services to a diverse user base, spanning various age groups and demographics [46]. Consequently, it provides a more accurate depiction of individuals' daily travel trajectories, making it particularly well suited for analyzing population mobility. To derive the migration scale, a linear mapping function is applied to the scale index [45]. The flows between cities are then computed based on inflow and outflow ratios, thereby reflecting the strength of inter-city connections and forming the network structure of the UAs.  
Driven by the repercussions of COVID-19 on the network structure of the UAs, this study took a comprehensive approach by considering the temporal aspects of COVID-19, particularly focusing on key time nodes. The time series is divided into three distinct periods: before COVID-19, spanning from 1 December 2019 to 23 January 2020 (when Wuhan imposed citywide lockdown measures); the normalization stage of COVID-19, extending from 24 January 2020, to 7 January 2023; and after COVID-19, commencing from 8 January 2023 (following the State Council's approval to lift precautionary measures for Category A infectious diseases related to COVID-19) to 1 June 2023. To minimize the influence of factors such as the Spring Festival and major holidays on population migration patterns, each of these three time periods is constructed using data from 30 selected weekdays, providing an average statistical representation. This data selection process encompassed the Baidu migration data for a total of 235 prefecture-level cities within the nineteen UAs, ultimately resulting in the network structures for each UA in China.  
(2) Urban basic data  
In this paper, the basic panel data (GDP, physicians per 10,000 employees, urban highway mileage, and number of university education levels per 10,000 people) of each city in the UAs are derived from the statistical yearbooks of each province in China and are integrated with prefecture-level cities as the unit and standardized to be dimensionless by means of polar deviation normalization. Urban geographic data (geographic distance of cities, number of cross-provincial links) are measured based on the standard geographic coordinate system WGS1984.
## 2 Materials and Methods  
The SIAR-GAN includes two networks: sinogram inpainting generative adversarial network (SI-GAN) and artifact removal generative adversarial network (AR-GAN). The SI-GAN network is inspired by SIN, and the backbone network also adopts the U-net network structure, and the following improvements are made. Firstly, we designed the size of the input sinogram, so that the area of the network to be completed is not increased as much as possible while increasing the resolution of the sinogram, and the network structure was redesigned to ensure that the designed sinogram can be input into the network normally. The sinogram size design is explained in detail in Section 2.4. Secondly, the SI-GAN network also introduces a cross-layer attention mechanism between the encoders. By calculating the degree of region matching between the patches inside and outside the blank region of missing angles, the feature information is transferred from the outside to the inside of the region, and the performance of the network can be further improved. This part of the content is described in detail in Section 2.1. The AR-GAN network is an improved version of the Cycle-Consistent Generative Adversarial Network (Cycle-GAN), which maps the generative domain CT image to the artifact-free image domain to achieve artifact removal. The training data comes from the unpaired real CT image and the generative domain CT image, and the generative domain CT image is reconstructed from the output of the SI-GAN network with the FBP algorithm. The SI-GAN is introduced to obtain more accurate completed sinogram, based on which FBP algorithm can achieve images with most artifacts removed. AR-GAN can further map images with most artifacts removed to the artifact-free image. The flow chart of the SIAR-GAN algorithm is shown in Figure 1.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 4 The Fail Safe over EtherCAT Protocol Implemented on WiFi
### Experimental Assessment  
A test application has been developed in which the FSoE master application invokes a request primitive to the FSoE master stack to send a safety message to the FSoE slave application and, upon reception of the confirm primitive from this latter one, it immediately starts a new request. These tests were executed in a laboratory environment where in-band interference was controlled. In addition, in this direction, we accurately selected the WiFi channel that showed the lowest traffic. The experiments lasted 5 h. Two meaningful performance indicators were evaluated, namely the percentage of FSoE lost packets and the polling time of the slave.  
During the test, 10 packets were lost in total, resulting in a ratio of lost packets equal to \(1.04\times 10^{-6}\). Concerning the polling time, Figure 5 shows its empirical probability density function (to ease readability, the figure reports data comprised in the interval median \(\pm\) one standard deviation). As can be seen, the behavior is slightly multi-modal with the main peak centered at about 1200 \(\upmu\)s. It also denotes a considerable jitter, which might prevent the use of this network in the most demanding applications.  
The obtained results, particularly the percentage of lost packets, lead us to conclude that, unfortunately, the SIL3 cannot be reached. Indeed, SIL3 requires that an FSoE connection can be re-initialized at most once every 5 h. Since each lost packet corresponds to one re-initialization, in these experiments, 10 re-initializations occurred.  
The calculation of \(S\)_FRT_ is straightforward from Equation (4). Considering that the watchdog time has been set to 250,000 \(\upmu\)s on both master and slave, it results in  
Figure 4: Scheme of the Experimental Setup.  
Figure 5: Empirical Probability Density Function of the Polling Time.  
\[SFRT=17942.40+250,000\,\upmu\text{s}=267.942,40\,\upmu\text{s} \tag{5}\]
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 3 Design Technology of Additively Manufactured Metal Mirrors
### Voronoi
#### 3.4.2 2D Voronoi  
The team of [PERSON] from IOF finally chose a sandwich mirror with the honeycomb structure after an experimental comparison of the five structures introduced in Section 3.1. By summarizing the previous designing and manufacturing experience, they optimized the mirrors' internal structure. The honeycomb structure was replaced with the 2D Voronoi cell pattern to make the design of the mirror's support more effective. AlSi\({}_{40}\) was chosen as the substrate material, which CTE matches electroless nickel as plating. Thus, it was conducive to minimizing the influence of the bimetallic bending effect on the stability of mirrors [69; 70; 71; 72; 73; 74].  
The internal structure was obtained as follows. Firstly, the Poisson disk sampling method is used to distribute seeds, avoiding undesired clustering. However, the uniform Voronoi cannot satisfy the whole mechanical requirements of the mirror body, so the Voronoi should be further optimized through FEA and modal analysis. In order to make the interface of mounting structures stiffer, the 2D Voronoi was denser in the three areas (Figure 29). The wall thickness was set at 0.5 mm, and then the 2D Voronoi was pulled up to mirrors' 3D support by SLM. Finally, the lightweight rate of the secondary mirror was 60.5%, with a surface accuracy of RMS 7.3 nm and surface roughness of 1.1 nm [39].  
Then they machined the primary mirror and the third mirror on one substrate, drastically reducing the processing time and the number of alignment degrees of freedom to be fixed during the integration phase. The bounding box of the whole element was \(205\,\mathrm{mm}\times 339.5\,\mathrm{mm}\times 60\,\mathrm{mm}\). The mirrors had a surface accuracy of RMS 20 nm and surface roughness of RMS 1 nm, so all mirrors were at the visible level [75; 76].