esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 3 Proposed Architecture
### Architecture Implementation  
#### 3.2.1 Software Tools  
In this work, several open-source software tools were considered for implementation and testing of the proposed architecture. For the RAN simulation, notable options include UERANSIM [17], OpenAirInterface [18], and my5G-RANTester [19], while for the CN, Open5 GS [20], free5 GC [21], and Openair-cn [22] are prominent.  
In the context of RAN simulation, UERANSIM was the chosen option. Although OpenAirInterface offers more features, its usage demands significantly higher computational capacity and is also more complex to configure and use. Moreover, OpenAirInterface was unable to connect more than one UE to a gNB on the same virtual machine (VM), which brought the necessity to use more VMs and, consequently, higher computer resource demands. On the other hand, both UERANSIM and my5G-RANTester were found to be simpler to use and configure and are capable of running on VMs with lower computational capacity. The choice of the former was based on the considerably higher availability of information on the Internet about it, its use in various projects, and the clear separation between the UE and the gNB, unlike my5G-RANTester, which integrates both in the same terminal. Table 1 illustrates the most significant features taken into account in the choice of RAN simulator software for use in this work.  
In the context of the CN simulation, Open5 GS was selected. [PERSON] et al. [23] conducted a study comparing the features of open-source software for simulating the 5G CN. Regarding openair-cn, they concluded that it requires greater CPU capacity than the other options. Concerning free5 GC and Open5 GS, the authors concluded that both are easy to configure, use, and integrate with UERANSIM. However, the selection of Open5 GS was based on several key points. After testing both simulators, it was found that free5 GC had limitations in handling different PLMN IDs. Although it correctly accepted and interpreted multiple PLMN IDs in the configuration, it failed to authenticate different UEs with different PLMN IDs. Furthermore, Open5 GS has recently included support for roaming, which is crucial to this work and not available in the other two options.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RAN Simulator** & **Multiple PLMNs** & \begin{tabular}{c} **Computational** \\ **Resources** \\ \end{tabular} & \
\begin{tabular}{c} **Multiple UEs in** \\ **the Same VM** \\ \end{tabular} &
\begin{tabular}{c} **Minimal CPU Core** \\ **Capacity per VM** \\ \end{tabular} \\ \hline OpenAirInterface & Yes & High & No & 4 \\ UERANSIM & No & Low & Yes & 1 \\ My-5 GRANTester & No & Low & Yes & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the RAN simulator alternatives.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 4 Network Model CNN-ST
### Model Framework  
To accurately classify randomly overlapping dual-component radar signals under different SNRs, this paper presents a novel network model CNN-ST, which possesses robust feature-extraction capability. To improve the focusing and information expression ability of dual-component radar signals under different SNRs, the network architecture based on CNN and the swin transformer is designed for extracting in detail the radar signal features. Figure 2 shows the network architecture of the CNN-ST model.  
Figure 1: SPWVD transformation of four types of dual-component signals under different SNRs. (**a**) EQFM (\(-2\) dB)-SFM (\(-2\) dB); (**b**) EQFM (\(-2\) dB)-SFM (\(12\) dB); (**c**) EQFM (\(12\) dB)-SFM (\(-2\) dB); (**d**) EQFM (\(12\) dB)-SFM (\(12\) dB).  
Figure 2 demonstrates that TFIs obtained by time-frequency transformation are first entered into the designed CNN-ST model, the radar signal features are then extracted in detail, and the signal types are finally classified. The CNN-ST model consists of the first convolutional layer, an average pooling layer, three bottleneck convolution blocks, four swin transformer blocks, a second convolutional layer, a nonlinearity layer Hard swish (H-swish), a third convolutional layer, an average pooling layer, an 8-dimension fully connected layer, and a multi-label classifier. Within this, softmax is the activation function of the multi-label classifier. The bottleneck convolution block aims to increase expressive ability. The expansion ratio changes the output channel number. The swin transformer block possesses powerful global modeling capability. For the deepening of the network, the cost of applying the nonlinear activation function H-swish could be reduced, and could largely decrease the number of parameters. Table 1 lists the CNN-ST parameters.
## 2 Experimental Development
### Exposure to Environmental Conditions  
#### 2.1.1 Relative Humidity (RH)  
Samples were placed in a WEISS climatic chamber (Figure 1a) at the Civil Engineering Department of Pontificia Universidad Javeriana Bogota (PUJB) under 85% RH and 25 \({}^{\circ}\)C, simulating conditions of high-humidity, high-seismic-risk zones such as Choco, Colombia. Periodic mass changes were monitored using a precision balance (0.1 mg and 0.1 g accuracy). Water content (M\({}_{\text{t}}\)) was calculated as the percentage of mass gained (Equation (1)),  
\[\Delta\text{M}_{\text{t}}(\%)=\frac{\text{m}_{\text{t}}-\text{m}_{0}}{\text{ m}_{0}}\ *\ 100\% \tag{1}\]  
where m\({}_{\text{t}}\) is the mass of the sample after time t, and m\({}_{0}\) is the initial mass of the sample, with measurements taken daily for up to 30 days.
# Multi-View Masked Autoencoder for General Image Representation
## 3 Method
### Framework
#### 3.1.2 Patchify and Masking Strategy  
Since we adopt ViT for the encoder, we patchify the target views into a non-overlapping 14 \(\times\) 14 patch sequence. To retain spatial information about where each patch is located, we  
Figure 4: A framework utilizing contrastive learning for masked image modeling. We firstly generate two different target views \(x_{1}^{+}\) and \(x_{2}^{+}\) with simple augmentation. Given \(x_{1}^{\prime}\) and \(x_{2}^{\prime}\), generated by random masking operation \(h(\cdot)\), our encoder \(f(\cdot)\) converts patch sequences into latent representations \(z_{1}\) and \(z_{2}\). Finally, our decoder outputs \(y_{1}\) and \(y_{2}\), which are reconstructed images.  
Figure 5: A flowchart of how target views are generated. Simple augmentation is applied to an input image \(I_{i}\). Afterwards, augmented images are converted into patch sequence and then randomly masked.  
Figure 3: Overall architecture. Original image \(I_{i}\) is converted into randomly masked images \(x_{1}^{\prime}\) and \(x_{2}^{\prime}\) after augmentation process. The encoder compresses them into \(z_{1}\) and \(z_{2}\), which are used for contrastive learning. Given \(z_{1}\) and \(z_{2}\), the decoder predicts the masked parts, thus outputting reconstructed images \(y_{1}\) and \(y_{2}\).  
add positional embedding to them. For positional encoding, we used sine-cosine positional encoding. In addition, for augmented view, we apply random masking to the patches. Masking is simple; we generate numbers following a uniform distribution. Afterwards, we'mask' a specific ratio of the total number of patches in the embedded patch sequence using the generated random numbers, i.e., random masking. The randomly masked patch sequences are denoted as \(x_{i}^{\prime}\in\mathbb{R}^{m\times}\) and can be formulated as \(x_{i}^{\prime}=h(x_{i}^{+}),i\in\{1,2\}\), where \(h\) denotes the patchifying and masking operation.  
Conventionally, masked language models mask relatively low portion of tokens, because more masking would result in insufficient context to learn good representation [38]. However, because image pixels are continuous contrary to discrete language tokens, higher masking ratio should be applied to eliminate redundancy in image. We choose certain masking ratio through experiments; see Section 4.2.1.
###### Abstract  
Understanding the patterns of noncovalent adsorption of double-stranded nucleic acids (dsDNA) on gold nanoparticles (GNPs) was the aim of this study. It was found that the high-affinity motifs in DNA can and do act as an \"anchor\" for the fixation of the whole molecule on the GNP (up to 98 \(\pm\) 2 single-stranded (ss)DNA molecules per particle with diameter of 13 \(\pm\) 2 nm). At the same time, the involvement of an \"anchor\" in the intramolecular DNA interaction can negatively affect the efficiency of the formation of ssDNA-GNP structures. It has been shown that the interaction of GNP with DNA duplexes is accompanied by their dissociation and competitive adsorption of ssDNA on GNP, wherein the crucial factor of DNA adsorption efficiency is the intrinsic affinity of ssDNA to GNP. We propose a detailed scheme for the interaction of dsDNA with GNPs, which should be taken into account in studies of this type. Researchers focused on this field should accept the complicated nature of such objects and take into account the many competing processes, including the processes of adsorption and desorption of DNA on gold as well as the formation of secondary structures by individual DNA strands.  
DNA duplex; gold nanoparticle; noncovalent adsorption; affinity; complementary oligonucleotides; single-stranded overhang; blunt-ended duplex; secondary structure; nanoassociate formation Article
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### OCR Deep-Learning Works
#### 7.1.1 CNN-LSTM-CTC Based-Works [47; 48; 49; 50; 51; 52; 53]
Moreover, this indicates the need for more challenging datasets;
* CNN-LSTM-CTC-based works can take a long time to train, especially if BiLSTM is devised. Moreover, they achieve low word-recognition rates in challenging settings;
* CNN-LSTM-CTC is currently the modus operandi architecture due to the fact that it handles spatial relations, long textual dependencies, and text alignment.
## 7 AI Fairness in Practice
### Social Administration
#### 7.1.3 Criminal Justice and Sentencing  
Utilizing AI fairness to address bias in risk assessment tools ensures that criminal sentencing decisions are fair [88; 281]. In the criminal justice system, some AI-based risk assessment tools have been found to be racially biased, resulting in harsher sentences for some minorities [282]. Implementation of fairness awareness training resulted in a significant reduction in system bias [283].  
Current research predominantly addresses racial, socioeconomic, and recidivism prediction biases. Data-driven disparities may lead to biased arrests, bail decisions, and sentencing. Biased recidivism prediction algorithms misclassify groups as high risk, perpetuating unfair treatment and higher incarceration rates for marginalized groups.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application** & **Issues** & **Mechanism** & **Opportunities** & **Challenges** \\ \hline \multirow{4}{*}{Health Care} & Racial and gender biases in diagnosis and treatment. & diversifying representative datasets. & Enhancing healthcare access and outcomes for all individuals. & Ensuring patient privacy and data security. Addressing biases in data collection and data sources. \\  & Unequal healthcare due to socioeconomic factors. & & parans based on individual characteristics. & Reducing healthcare disparities. & Addressing biases in data collection and data sources. \\ \hline \multirow{4}{*}{Education} & Bias in admissions and resource allocation. & Fair criteria for admissions and resource allocation. & Reducing educational disparities. Enhancing learning for individual needs. Identifying and assisting at-risk students. & ethical considerations regarding data privacy in educational settings. \\  & Racial Bias in predictive policing and sentencing. & & focus on rehabilitation with regular auditing and updating the models with transparency in decision-making. & Reducing biased arrests and sentencing. & The ethical implications of using AI in criminal justice. Ensuring model accountability and avoiding “tech-washing”. \\ \hline \hline \end{tabular}
\end{table}
Table 13: AI fairness in social administration practices.  
Future efforts should gather diverse, representative training data to mitigate bias. Fairness-aware algorithms and AI models optimizing fairness should be developed to prevent differential treatment based on race or socioeconomic status. Transparent risk assessment models can enhance interpretability, aiding defendants and legal professionals. Regular model audits are essential to identify and rectify potential biases in model deployment.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
###### Abstract  
Nowadays there are many social networking services supporting three-party communication such as Skype, Line, and Facebook Messenger. To ensure the message security, a cryptographic encryption scheme is a commonly adopted measure. However, the traditional asymmetric encryption only allows one designated recipient to decrypt the ciphertext with his/her private key. It is thus difficult for two parties to share the same ciphertext without exposing their private keys. In this paper, the author comes up with a novel dual authenticated encryption (DAE) scheme designed for three-party communication environments. Specifically, a DAE scheme enables a party to generate a single ciphertext that could be solely decrypted by the other two participants without sharing their private keys. It is also formally shown that the proposed scheme achieves the crucial security properties using the random oracle proof model.  
Keywords:dual authenticated encryption; three-party communication; bilinear pairing; public key; cryptography +
Footnote †: journal: Accepted: 4 April 2019; Published: 6 April 2019
## 4 Discussion  
### Soil Characteristics of Study Area  
The Songnen Plain is located in a typical region with black soil. The physicochemical analyses of soils showed that the non-affected soil in the study area was in dark color due to the high organic matter content (TOC) in soil. As well as, the alkaline and saline soils were in low TOC content value and a light color. The soil color decreases with soil TOC content decrease and CO\({}_{3}\)\({}^{2-}\) content increase. Variation of soil color related to the alkalinity, salinity, and organic matter content provided the potential for detecting soil alkalinity and salinity with remotely sensed data.  
The analysis of soil spectra in the VIS-NIR region in this study provided a quantitative estimation of soil alkalinity and salinity. For the HSI image wavelengths (505-956 nm), high alkaline and saline soil had higher spectral reflectance than low alkaline and saline soil. This result can be attributed to the variation of soil color resulting from soil alkalinity and salinity that related to the organic matter in soil. The relationships among alkalinity, salinity, and organic matter agreed with the findings of [PERSON] et al. [45] in saline-alkali soils in the west of the Songnen Plain, China.
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 4 Experiment
### Determination of the Rotation Center
In order to obtain a more accurate rotation center, we adjusted the camera to \(\overline{O}_{r}\), employing the translation stages along the \(Y_{w}\) and \(Z_{w}\) directions, respectively, and rotated the camera 16 times to calculate the optical centers \(O^{\prime}{}_{r\!j}\), which are distributed over a small range, as shown in Figure 12. The average value of \(O^{\prime}{}_{r\!j}\) is \(O^{\prime}{}_{r}\) = (\(-54.59\), \(962.05\)) mm. The final rotation center \(O_{r}\) is calculated by employing \(O_{r1}\), \(O_{r2}\), \(O_{r3}\) and \(O^{\prime}{}_{r}\) using Equations (9)-(12), where \(O_{r}\) = (\(-54.69\), \(961.96\)) mm. The camera is moved to \(O_{r}\) and rotated 16 times to calculate the optical centers \(O^{\prime}{}_{r\!j}\). The standard deviation (STD) of \(O^{\prime}{}_{r\!j}\) relative to \(\overline{O}_{r}\) and \(O^{\prime}{}_{r\!j}\) relative to \(O_{r}\) in the \(Y_{w}\) and \(Z_{w}\) directions are shown in Table 4. It is obvious that \(O_{r}\) is more reliable than \(\overline{O}_{r}\). These results indicate that the camera's optical center has  
Figure 11: (**a**) Projection of the optical centers on the \(Y_{w}O_{w}Z_{w}\) plane; (**b**) fitted circular curve. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Initial Position of the Camera** & **Fitted Center/mm** & **Fitted Radius/mm** & **RMSE/mm** \\ \hline c\({}_{1}\) & \(O_{r1}\) = (\(-55.23\), \(961.58\)) & r\({}_{1}\) = 71.62 & 0.042 \\ c\({}_{2}\) & \(O_{r2}\) = (\(-55.38\), \(961.88\)) & r\({}_{2}\) = 53.32 & 0.054 \\ c\({}_{3}\) & \(O_{r3}\) = (\(-55.00\), \(961.53\)) & r\({}_{3}\) = 67.29 & 0.042 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting results. Figure 10: (**a**) Projection of the optical centers on the \(Y_{w}O_{w}Z_{w}\) plane; (**b**) fitted circular curve (red*, green* and blue* are the fitted centers of the three fitted curves marked by c\({}_{1}\), c\({}_{2}\) and c\({}_{3}\), respectively). been positioned accurately with the rotation center of the turntable and the systematic uncertainty in our method remains about 0.1 mm.
## 1 Introduction
It should be emphasized that there is a large number of possible machine learning approaches which can be used for the purpose of fault diagnosis and even structural health monitoring [7]. Indeed, recently Deep Neural Networks (DNN) have received a considerable research attention. Apart from their incontestable modeling appeal they usually contain a large number of parameters which increase the overall uncertainty of the resulting neural model. Thus, taking into account the well known rule from statistical and experimental design literature [22], among a spectrum of models with comparable quality the simplest one should be selected as the number of uncertain parameters is related to the overall uncertainty of the model. Taking into account the above discussion, a simple MLP is selected. Its wide popularity in numerous engineering applications results from its possibility of modeling any non-linear dynamic system [3]. Another advantage is that the several mature and effective training algorithms for such networks were developed. However, such a choice does not exclude more advanced structures which can be used instead. The paper is organized as follows. In Section 2 the active and passive approaches to fault detection related with the proposed method are described. Section 3 presents the overall scheme of the model-based RFD and the structure with the description of the ANNs used for the RFD purpose. In Section 4 a guaranteed convergence modified non-linear quasi OBE algorithm used for the gradual improvements of the MLP parameters estimation quality is developed. In Section 5, the quality of the MLP is additionally enhanced by the application of the developed SDED algorithm. The description of the MLP uncertainty calculated with the application of the developed approach is employed in Section 6 to design the adaptive thresholds. Section 7 contains examples of the adaptive thresholds application in the RFD of the three-screw spindle oil pump and two-tank system. Finally, the last section of this paper is devoted to conclusions and future research direction.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis
### Position Noise  
To verify the robustness of various star identification algorithms against positional noise, we added noise with a mean of 0 pixel and a variance ranging from 0 to 2 pixels to the star point coordinates in the simulated star images to observe changes in identification accuracy. As shown in Figure 8, under the influence of positional noise, the identification rates of the improved triangle algorithm, pyramid algorithm, and radial axial algorithm rapidly decline with increasing noise, while the grid algorithm performs slightly better, maintaining a identification rate above 90%. The algorithm based on dynamic angle matching exhibited the highest identification rate. At the same time, the dynamic distance ratio algorithm also showed no significant decrease in recognition rate, maintaining an average recognition rate of 97.26%, even when the star point deviation reached 2 pixels, demonstrating stronger robustness to position noise.
## 3 Results
### Regional Variation  
Exploring the regional suicide rates, significant variation was observed in both periods (Table 1). In the pre-crisis period, the regional SDRs ranged from 15.8 to 30.5/100,000, with significantly higher rates in the Ionian Islands (\(p<0.01\)), Crete (\(p<0.001\)), Peloponnese (\(p<0.001\)), and West Greece (\(p<0.05\)). The max/min SDR ratio was calculated at 1.92 and the coefficient of variance was 19.9%. In the exposed to crisis period, the regional SDRs ranged from 19.4 to 37.6/100,000. The rate remained significantly higher than the national average only in Crete (\(p<0.001\)). The max/min rate ratio of SMRs was found to be 1.93 and the CV was 16.5%. Focusing on the change of rates between the two periods, significant increase was observed in most regions, with a higher rise in Attica (58.6%), South Aegean Islands (37.9%), and Central Macedonia (33.4%). Sex-and-age-specific suicide rates (Table 2) revealed significantly higher rates in males 45-64 years old in Crete and East Macedonia-Thrace, and over 65 years in Crete, Epirus, Peloponnese, and East Macedonia-Thrace. In females, higher rates were observed in the age group of 45-64 in Epirus, and over 65 years in age in Epirus and the Ionian Islands.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Regions** & **SDR** & **SDR** & **Change of** & **CMF** & **95\% CI\({}_{\text{CMF}}\)** \\ \cline{2-6}  & **2005–2010** & **2011–2016** & **SDR *** & **2011–2016** & **Min** & **Max** \\ \hline East Macedonia/Thrace & 24.3 & 29.6 & **22.2\% 1** & 148.8 & 128.9 & 171.7 \\ Central Macedonia & **15.8 1** & **21.1 1** & **33.4\% 1** & 106.1 & 96.2 & 117.0 \\ West Macedonia & 18.6 & 24.0 & **29.0\% 2** & 120.5 & 95.3 & 152.4 \\ Epirus & 23.7 & 28.6 & **20.8\% 1** & 143.5 & 118.6 & 173.7 \\ Thessaly & 20.6 & 26.7 & **29.6 1** & 134.2 & 116.8 & 154.2 \\ Central Greece & 20.9 & **24.6 3** & **17.4\% 2** & 123.3 & 104.8 & 145.1 \\ Ionian Islands & **30.5 2** & 31.9 & 4.7\% & 160.1 & 125.8 & 203.9 \\ West Greece & **24.2 3** & 30.7 & **26.7\% 1** & 153.9 & 134.6 & 176.0 \\ Peloponnese & **27.5 1** & 28.9 & 5.2\% & 144.9 & 125.0 & 168.0 \\ Attica & **16.8 1** & 26.6 & **58.6\% 1** & 133.6 & 125.7 & 142.1 \\ North Aegean Islands & 19.9 & **19.4 1** & \(-\)2.2\% & 97.6 & 71.2 & 133.7 \\ South Aegean Islands & 20.0 & 27.6 & **37.9 1** & 138.6 & 112.2 & 171.3 \\ Crete & **29.9 1** & **37.6 1** & **25.8\% 1** & 188.6 & 165.8 & 214.5 \\ Greece & 19.8 & 26.8 & **35.1\% 1** & 134.4 & 129.6 & 139.3 \\ \hline \hline \end{tabular}  
* Significance of difference from national average: 1 \(p<0.001\), 2 \(p<0.01\), 3 \(p<0.05\).
* Change of SDR = (SDR\({}_{n+1}-SDR_{n}\)) \(\times\) 100/SDR\({}_{n}\). The \(p\)-values refer to the significance of the difference between the two periods.  
\end{table}
Table 1: Suicide standardized death rate (SDR) and comparative mortality figure (CMF) by region, 2005–2010, 2011–2016.  
Figure 1: Suicide SDR (Standardized Death Rate), 2000–2016.