esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 2 Relate Work
### Deep Learning Based Steganography  
In 2017, [PERSON] et al. [27] proposed the HayesGAN model, which adopts an adversarial architecture including embedding, extraction, and steganalysis networks. The embedding network is responsible for embedding secret information into images, the extraction network recovers hidden information, and the steganalysis network detects whether images contain secret information, providing a novel generative adversarial approach to steganographic technology. In 2018, [PERSON] et al. [13] proposed an end-to-end steganographic model HiDDeN, implementing data hiding through encoder and decoder networks and enhancing model robustness by adding noise layers to simulate transmission attacks. To further increase hiding capacity, in 2019, [PERSON] et al. [17] proposed the SteganoGAN model, whose encoding-decoding network design includes three structures: basic network, residual network, and dense network. The evaluator determines whether images are carrier or stego images, and the model incorporates Reed-Solomon code, with its DenseNet-based encoder achieving a hiding capacity of 4.4 bpp.  
[PERSON] et al. [28] proposed the HCISNet model based on SteganoGAN, combining the ASPP spatial pyramid pooling module with DenseNet to build an encoder, further increasing hiding capacity to 5.68 bpp. [PERSON] et al. [18] also improved SteganoGAN by introducing channel attention modules to identify beneficial channels in feature maps and error correction codes for hiding information bits. [PERSON] et al. proposed the Adaptive Frequency Domain Channel Attention Network (AFcaNet) [29], enhancing frequency feature utilization through fine-grained weight allocation, though it improved feature extraction capability without significantly enhancing steganographic capacity. [PERSON] et al. [30] proposed a reversible image-hiding network HiNet, significantly improving image-hiding security and recovery accuracy through shared network parameters and frequency domain hiding techniques. [PERSON] et al. proposed Fixed Neural Network Steganography (FNNS) [31], which utilizes neural networks' sensitivity to minor perturbations by optimizing subtle modifications to input images for information hiding. It can reliably hide information without compromising image visual quality, achieving nearly 0% error rate in information recovery (supporting up to 3 bpp). However, this method relies on fixed decoder networks, limiting further enhancement of steganographic capabilities. [PERSON] et al. [32] proposed an innovative method for robust steganography using autoencoder latent space. The core of this method is training a simple lightweight network while freezing the pre-trained encoder.
## 2 Operation Principle
### Frequency Offset Compensation  
Next, we proceed to the frequency offset compensation phase. This algorithm is based on a hardware-implemented parallel scheme, where the frequency offset is calculated once for every \(M\) symbol (i.e., the symbol block length is \(M\)). As shown in Figure 6, we will next focus on the \(k-th\) symbol block for a detailed explanation, taking the \(m-th\) symbol of the \(k-th\) symbol block as an example. From the second phase, the frequency offset \(f_{k}\) for each symbol block was calculated with the following formula:  
\[FO\_sum(m)=2\pi Tm\cdot f_{k}+FO\_sum(M\cdot(k-1)) \tag{13}\]  
Here, m represents the \(m-th\) signal of the \(k-th\) symbol block. \(m\cdot f_{k}\) refers to the frequency offset basic error. \(FO\_sum(M\cdot(k-1))\) represents the actual frequency offset accumulation of the last signal in the previous symbol block. The summation represents the actual frequency offset accumulation for the current symbol, denoted as \(FO\_sum\).  
The last stage is the frequency offset compensation stage; the formula is as follows:  
\[S^{\prime}_{n}=S_{n}\cdot e^{-j(FO\_sum(n))} \tag{14}\]
## 2 Materials
### Jilin-1 GP01/02 Data  
The Jilin-1 GP01 and Jilin-1 GP02 satellites, launched on January 12, 2019, were equipped with identical sensor payloads, including a multispectral imager (PMS) and cameras sensitive to short-wave, medium-wave, and long-wave infrared spectra. Each satellite was furnished with a pair of multispectral imagers (PMS1 and PMS2) capable of acquiring remote sensing data across 20 spectral bands at spatial resolutions of 5 m, 10 m, and 20 m, respectively. The swath width for data capture was 60 km, with a lateral swing imaging capability of \(\pm 45^{\circ}\). The focus of this paper is on the data procured from the multispectral imager. Table 2 provides an overview of the pertinent technical specifications of the Jilin-1 GP01/02 PMS.  
Due to the use of MODIS BRDF parameters, this paper selected B3 (450-515 nm), B4 (525-600 nm), B5 (630-680 nm), and B6 (784.5-899.5 nm) with a 5 m resolution similar to MODIS satellite settings for the angle effect correction study. Figure 2 illustrates the comparative spectral response functions of the Jilin-1 GP01/02 PMS and MODIS. Given that the blue band is susceptible to contamination from residual atmospheric influences, it was excluded from the accuracy assessment in the monitoring of crop parameters [38].  
To ensure congruence between satellite observation data and corresponding field measurements, the Jilin-1 GP01/02 PMS data corresponding to 12 quasi-synchronized scenes were secured. Specifically, satellite and ground observations were synchronized on July 18, July 19, September 11, and September 17. For the remaining satellite data, the temporal discrepancy from the ground measurement acquisition was maintained within a 3-day window. The Jilin-1 GP01/02 PMS data underwent a series of processing steps, including radiometric calibration, atmospheric correction, and geometric correction. Additionally, the imaging geometry parameters for each pixel were calculated, encompassing the solar zenith angle (\(\theta_{s}\)), view zenith angle (\(\theta_{v}\)), and relative azimuth angle (\(\phi\)). A summary of the imaging times and geometrical information for these data is presented in Table 3.  
Figure 1: The distribution map of the study area and the field survey points.  
\begin{table}
\begin{tabular}{c c c} \hline
**Band** & **Spectral Range (nm)** & **Spatial Resolution (m)** \\ \hline B0 & 450–800 & 5 \\ B1 & 403–423 & 5 \\ B2 & 433–453 & 5 \\ B3 & 450–515 & 5 \\ B4 & 525–600 & 5 \\ B5 & 630–680 & 5 \\ B6 & 784.5–899.5 & 5 \\ B7 & 485–495 & 10 \\ B8 & 615–625 & 10 \\ B9 & 650–680 & 10 \\ B10 & 698.75–718.75 & 10 \\ B11 & 732.5–747.5 & 10 \\ B12 & 773–793 & 10 \\ B13 & 855–875 & 20 \\ B14 & 660–670 & 20 \\ B15 & 677.5–685 & 20 \\ B16 & 750–757.5 & 20 \\ B17 & 758.75–762.75 & 20 \\ B18 & 935–955 & 20 \\ B19 & 1000–1040 & 20 \\ \hline \end{tabular}
\end{table}
Table 2: Technical specifications of Jilin-1 GP01/02 PMS.  
Figure 2: Spectral response functions of Jilin-1 GP01/02 PMS and MODIS in visible to near-infrared bands.
# Designing a Reliable UAV Architecture Operating in a Real Environment
## 4 Collision Avoidance Algorithm  
### The General Model of Collision Avoidance  
To present the algorithms supporting the UAV reaction to the occurrence of a critical situation, an algorithm for avoiding the collision of two air platforms was selected. The basic version of the algorithm is described in the article [9]. It defines a sequence of  
Figure 5: The physical architecture of UAV with additional simulation units. Simulation packages replace FCC. The remaining UAV components remained unchanged.  
equations that allow the calculation of the minimum safe manoeuvres of two airplanes, which restrains the platforms from violating the protection zone.  
In brief, the article assumes that two UAVs fly with a constant speed and direction (see Figure 6). Since the velocity vectors, \(\vec{V_{A}}\) and \(\vec{V_{B}}\) are known, the nearest approach vector is defined as:  
\[\vec{\tau}_{m}=\acute{\epsilon}\times\left(\vec{\tau}\times\acute{\epsilon} \right), \tag{1}\]  
where: \(\acute{\epsilon}=\vec{V_{B}}-\vec{V_{A}}\), is defined in the Figure 6, \(\acute{\epsilon}\) is its normalized representation (\(\acute{\epsilon}=\frac{\vec{\tau}}{\|\acute{\epsilon}\|}\)). If \(\|\vec{\tau_{m}}\|<r_{safe}\), then separation distance will be violated. \(r_{safe}\) is the safe distance.  
Next, the time of closest approach \(\tau\) is calculated:  
\[\tau=-\frac{\vec{\tau}\cdot\vec{\epsilon}}{\acute{\epsilon}\cdot\acute{ \epsilon}}. \tag{2}\]  
This enables to calculation of the positions of the UAVs for the closest approach. For \(UAV_{A}\), these vectors can be calculated as presented in the Equation (3):  
\[\vec{\tau}_{VSA}=k\cdot\frac{r_{safe}-\vec{\tau}_{m}}{\vec{\tau}_{m}}(-\vec{ \tau}_{m})\,, \tag{3}\]  
where \(k=\frac{\vec{V_{B}}}{\|\vec{V_{A}}\|+\|\vec{V_{B}}\|}\) represents coefficient forcing a slower airplane to take a bigger turn, as higher speed reduces maneuverability. The resulting dodge vector \(\vec{U_{A}}\) is calculated as follows:  
\[\vec{U}_{A}=\vec{V_{A}}\cdot\tau+\vec{\tau}_{VSA}\,. \tag{4}\]  
[PERSON], et al. [9] propose to calculate \(\vec{U}_{A}\) as unit vectors to define the direction of requested movement. However, for some reason, it is better to use the non normalized \(\vec{U}_{A}\) on some systems to infer the collision avoidance waypoint position.
## 2 Literature Review
### Spatial Analysis Techniques  
Spatial analysis is a crucial technique for understanding and interpreting geographic data to identify patterns, relationships, and trends within a spatial context [6; 20]. It plays a vital role in various fields, including urban planning, environmental studies, and transportation management. The core methods of spatial analysis include spatial statistical analysis, spatial regression analysis, and cluster analysis. Spatial statistical analysis applies statistical techniques to analyze the spatial distribution and patterns of data [21]. This method is essential for identifying hotspots, trends, and anomalies within geographic data, providing insights that are not immediately apparent through simple visual inspection. Spatial regression analysis, on the other hand, examines the relationship between spatial and non-spatial variables [22]. This technique allows researchers to understand how geographic factors influence outcomes such as population density, pollution levels, and disease spread. By integrating geographic information into regression models, researchers can make more accurate predictions and understand underlying spatial dynamics [23]. Cluster analysis groups data points based on similarities or distances [24]. It identifies areas with similar characteristics, such as regions with high pollution levels, high population density, or specific socioeconomic conditions, and is particularly useful for detecting spatial patterns and making informed decisions on resource allocation and policy implementation.  
Isochrone analysis is a specialized type of spatial analysis used to represent areas that can be reached within a specific timeframe from a given location [25]. It considersfactors such as transportation networks, traffic conditions, and travel speeds to accurately calculate travel times. The principles of isochrone analysis involve creating lines, known as isochrones, on a map that connect points with equal travel times to a central location [26]. Isochrone analysis provides a detailed and realistic representation of accessibility within a given area by incorporating data on road networks, public transportation routes, and traffic patterns. It is widely used in transportation planning to assess the accessibility of public transportation stops and optimize routes and stops to ensure efficient and equitable service coverage [27]. It is also critical in emergency service planning, helping to determine the optimal locations for facilities such as fire stations and hospitals to minimize response times. Additionally, businesses use isochrone analysis for site selection to maximize accessibility to potential customers within specific travel times. By visualizing accessibility and travel times, isochrone analysis aids in making informed decisions regarding the placement and optimization of various services and facilities, enhancing urban planning and public service delivery, ultimately contributing to an improved quality of life for communities.
## 4 Proposed Experimental Results and Comparative Performance
### Performance of Single Models  
Before we evaluate the proposed multi-model ensemble framework, the parameters of the individual models, cluster linear regression (CLR), long-term multilayer perceptron (LMLP), Fourier series descriptor (FSD), and short-term multilayer perceptron (SMLP), are tuned in advance to serve as a baseline for comparison. As previously described in Section 3.3.1, the CLR has been well constructed by a decision tree partition. The main parameters of the MLP are the number of hidden layers and the number of neurons in each hidden layer. We first constructed various one-hidden-layer MLPs with the number of neurons equivalent to 8, 10, 12, denoted by MLP(8), MLP(10), and MLP(12), respectively. The 3-year (2017-2019) test set forecasting performance is listed in Table 3 where the best result is shown in boldface. It is seen that MLP(12) outperforms the other counterparts. To test if the MLP performance improves with the number of hidden layers, the three-hidden-layer MLP with 12 neurons in each hidden layer, denoted by MLP(12, 12, 12), is constructed for comparison. We observe that increasing the number of hidden layers in MLP does not show the performance improvement in forecasting. This observation is consistent with the results from the literature [36], which has shown that an MLP with only one hidden layer can be satisfactorily used in different fields of engineering. In this paper, both the long-term and short-term MLPs are implemented with one-hidden-layer with 12 neurons. For the FSD, we have to determine the best degree \(n\) of the Fourier polynomial. The 3-year test set forecasting performance of the FSD, with \(n\) equivalent to 30, 40, 50, 60, 70, 80, and 90, is shown in Table 4. We see that there is no clear trend about the optimal value of \(n\). Consider the tradeoff between the forecasting performance and computational efficiency, we chose \(n=60\) in our FSD setting. Finally, Table 5 tabulates the RMSE, MAE, and MAPE test performance obtained by each individual model for all days in the three-year test set. It is seen that the LMLP is the best among the four models for the three-year test set, CLR ranks at the second place, followed by FSD and SMLP.
# Unraveling Patient Safety Culture Trends in U.S. Hospital Settings: A Yearly Retrospective Analysis
## 3 Results
### Patient Safety Culture Overview
While variations in staff selection methods were less pronounced, surveys of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Category** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** & **D8** & **D9** & **D10** & **D11** & **D12** \\ \hline \multicolumn{10}{c}{Region} \\ \hline Northeast & 62\% & 64\% & 76\% & 68\% & 79\% & 62\% & 63\% & 44\% & 49\% & 68\% & 56\% & 42\% \\ \hline South & 67\% & 70\% & 81\% & 75\% & 83\% & 66\% & 72\% & 48\% & 54\% & 74\% & 63\% & 50\% \\ \hline Center & 68\% & 66\% & 80\% & 72\% & 82\% & 65\% & 69\% & 48\% & 56\% & 72\% & 62\% & 49\% \\ \hline West & 62\% & 66\% & 77\% & 70\% & 82\% & 63\% & 66\% & 43\% & 51\% & 69\% & 60\% & 45\% \\ \hline \multicolumn{10}{c}{\(p\)-value} \\ \hline \multicolumn{10}{c}{\(p\)-value} \\ \hline \multicolumn{10}{c}{\(6\)-\(49\) beds} \\ \hline \multicolumn{10}{c}{\(72\%\)} \\ \hline \multicolumn{10}{c}{\(6\)-\(49\) beds} \\ \hline \multicolumn{10}{c}{\(50\)-\(99\) beds} \\ \hline \multicolumn{10}{c}{\(68\%\)} \\ \hline \multicolumn{10}{c}{\(68\%\)} \\ \hline \multicolumn{10}{c}{\(79\%\)} \\ \hline \multicolumn{10}{c}{\(73\%\)} \\ \hline \multicolumn{10}{c}{\(82\%\)} \\ \hline \multicolumn{10}{c}{\(74\%\)} \\ \hline \multicolumn{10}{c}{\(70\%\)} \\ \hline \multicolumn{10}{c}{\(72\%\)} \\ \hline \multicolumn{10}{c}{\(75\%\)} \\ \hline \multicolumn{10}{c}{\(76\%\)} \\ \hline \multicolumn{10}{c}{\(77\%\)} \\ \hline \multicolumn{10}{c}{\(78\%\)} \\ \hline \mulselected departments or units showed marginally higher responses for some dimensions, although most comparisons in this category lacked statistical significance. Institutionally, non-teaching hospitals scored significantly higher across all dimensions compared to teaching hospitals (\(p<0.001\)). Smaller hospitals (6-49 beds) consistently reported higher positive responses than larger hospitals (400+ beds) (\(p<0.001\)).
## 2 Materials and Methods
### Ultrasound Signal Processing
#### 2.2.1 Dynamic Filtering Module Based on the Radix-4 CORDIC Algorithm  
This paper adopts a set of 33 rd-order dynamic finite impulse response (FIR) filters with a sampling frequency of 100 MHz, a probe with a bandwidth of 1 MHz, and an initial ultrasonic echo signal with a center frequency of 3.5 MHz. Assuming that the cut-off frequency is \(\omega_{c}\), the filter order is N, and the filter coefficients are designed with the Hamming window. According to [35; 36], the calculation formula of the filter coefficient can be determined as shown in Equation (1).  
\[\mathrm{h}(\mathrm{n})=\left\{\begin{array}{ll}\frac{\sin[\omega_{c}(n-\tau )]}{\pi(n-\tau)}\cdot\left(0.54-0.46\cdot\cos\left(\frac{2n\pi}{\mathrm{N}-1} \right)\right)\cdot\mathrm{R}_{\mathrm{N}}(\mathrm{n})&n\
eq\tau\\ \frac{\omega_{c}}{\pi}\cdot\left(0.54-0.46\cdot\cos\left(\frac{2n\pi}{\mathrm{ N}-1}\right)\right)\cdot\mathrm{R}_{\mathrm{N}}(\mathrm{n})&n=\tau\end{array}\right. \tag{1}\]  
where \(\tau=\frac{\mathrm{N}-1}{2}\). From Equation (1), the process involves a large number of sine and cosine functions and multiplication operations. The radix-4 CORDIC algorithm is used to compute high-precision sine and cosine functions. Compared with the traditional radix-2 CORDIC algorithm, the number of iterations is reduced by half, the computation delay is low [37; 38], and it is suitable for pipeline structure implementation, providing a new way to solve the filter coefficients dynamically and in real-time, thus saving ROM storage resources and improving the computation accuracy of the filter coefficients. This paper uses the idea of modularization to design dynamic FIR filters. The block diagram of the dynamic FIR filter design is shown in Figure 3. The phase-locked-loop (PLL) module is used to multiply the 50 MHz clock input to a sampling frequency of 100 MHz. The relevant parameters are input into the radix-4 CORDIC operating module. The pipeline structure is adopted to increase speed. The reading of the 17 filter coefficients is controlled by a FIFO controller and a multiplexer. The FIR filter module adopts a parallel distributed architecture. It can reduce the resource consumption of the lookup table by dividing the unified lookup table into smaller lookup tables [39]. For a 33 rd-order filter, four ROM tables are required, and the ROM addresses vary with the receiving depth, resulting in different filter coefficients.  
Figure 3: Block diagram of the dynamic finite impulse response (FIR) filter design.  
Figure 2: Flow diagram of radial artery ultrasonic signal processing.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 3 Experiments and Results
### Results and Analysis  
To evaluate the effectiveness of our proposed method, the simplified contours generated by the Douglas-Peucker (DP) algorithm, Wang-Muller (WM) algorithm, and CCSM were compared. First, a visual analysis was performed to examine and verify the ability of these methods in showing the continuous multi-scale details of simplified contours. Second, the shape similarity change trend of multi-scale simplified contours generated by these methods was evaluated. Third, the retention degree of the global structural features of simplified contours generated by these methods was compared. Fourth, the change degree in position was assessed.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 6 Discussion
### Limitations  
However, there are still several limitations for TrendFlow.  
**Constrained summarization of generated research trends**. The existing TrendFlow framework generates research trends for each paper within a single cluster, which may lead to an overwhelming number of trends if the cluster contains a large number of papers. This necessitates refined post-processing of the generated research trends. At present, TrendFlow utilizes a union-find algorithm to group similar research trends, which is a basic and straightforward approach. Nevertheless, exploring more advanced methods (e.g., neural-network-based techniques) is essential for handling potentially larger datasets.  
**Domain-specific adaptations**. In this study, the RTA datasets were annotated by sampling literature exclusively from machine learning and deep learning-related domains. Consequently, the generalizability and adaptability of the TrendFlow framework to diverse research domains remain largely unexplored. It is important to recognize that the framework's performance might vary across different domains. Future research should investigate the necessity of domain-specific adaptations or customizations to enhance its performance in various fields of study.  
**Scalability**. The TrendFlow framework's performance and scalability in handling largescale datasets have not been thoroughly evaluated. The increasing volume of research publications may pose challenges in terms of computational resources, processing time, and memory requirements. Future work should consider addressing these issues by optimizing the framework's performance and testing its scalability with more extensive datasets.  
In addition to the framework limitations, the human evaluation of TrendFlow's performance should be taken with caution due to the small dataset size and subjective nature of manual ratings. A more extensive evaluation with a larger dataset and a more diverse pool of domain experts would help obtain more reliable results.