esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 3 Proposed Scheme
### Data Extraction And Recovery Procedure  
In this subsection, we describe the procedure for extracting the hidden information and recovering the original cover images from the two stego images \(\Delta\mathbf{I}_{1}\) and \(\Delta\mathbf{I}_{2}\) generated during the embedding process (Section 3.3). Based on the virtual pixel construction method described in Section 3.1 and the syndrome computation process defined in Section 3.3, the extraction and recovery are performed sequentially for each pixel pair in the stego images.  
For data extraction, the virtual pixel \(y\) is reconstructed at each pixel position and the syndrome \(S\) is recalculated using the previously defined parity-check matrix \(H\). Each extracted syndrome directly corresponds to a group of three secret bits, which are sequentially collected to form the hidden message vector \(m\).  
Following extraction, the recovery of the original cover images proceeds by leveraging the property that all virtual pixels belonged to the zero-syndrome coset prior to embedding. In this way, the codeword associated with the zero syndrome that minimizes the discrepancy from the current stego pixel is identified for each virtual pixel. The optimal codeword is determined by minimizing the Euclidean error between the extracted bits and the candidate codewords, following the error metric introduced in Section 3.1.  
After the nearest codeword is found, the cover pixel values are reconstructed accordingly, restoring the two original cover images \(\mathbf{I}_{1}\) and \(\mathbf{I}_{2}\).  
The overall extraction and recovery process is summarized in Algorithm 3.
# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
###### Abstract  
Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.  
Keywords: retinal blood vessel image; computer-aided diagnosis; U-shaped neural network; residual learning; semantic gap +
Footnote †: journal: Applied Sciences
## Instrumentation  
### Acoustic Microtransmitters  
Each microtransmitter, surgically implanted in study fish, transmits a unique binary code encoded using binary phase shift keying (BPSK) as a 416.7-kHz carrier waveform, \(\pm\)0.5% (2.08 kHz). The source level of the current microtransmitter design is larger than +153 dB re 1 \(\upmu\)Pa _@_ 1 m. It has a maximum weight of 0.430 g in air. Its maximum dimensions are 6.00 mm wide, 4.10 mm thick, 12.00 mm long, and 0.300 mL in volume (Figure 2).  
Figure 2: JSATS acoustic microtransmitter (Advanced Telemetry Systems, Inc., Isanti, MN, USA).  
The code structure of the microtransmitter includes a total of 31 bits, with 7 synchronization bits (Barker code 1110010 in bits or 72 in hexadecimal format), 16 ID bits (4 hexadecimal digits), and 8 cyclic redundancy check (CRC) bits (2 hexadecimal digits), resulting in the 65,536 possible unique identification codes. BPSK was chosen for encoding because of its excellent bit error rate performance in noisy environments. A bit change from 0 to 1 or 1 to 0 results in a phase change of 180 degrees in the carrier (no phase change occurs if the next bit is the same). Unlike an electrical signal, phase change requires several cycles when the piezoelectric transducer changes its phase of oscillation and a vibrating mass cannot instantly modify the phase of vibration. This is apparent in the waveform as a slight lengthening of the time between message bits as the new phase is achieved. The active element may also shorten (speed up) the time between bits as it changes phase, but this is much less common. During the phase change, the magnitude of oscillation decreases. The physical requirements for change in phase are evident in microtransmitter waveforms (Figure 3). Each transmitted bit requires 10 carrier cycles. The microtransmitters emit their unique 31-bit codes at a programmed interval, typically every 3, 5, or 10 s, depending on the study design, for 310 cycles at a carrier period of 2.4 us, resulting in a signal length of 744 us and an acoustic length of approximately 1.1 m at 20.
## 4 Related Work
### User Applications  
The Appdaptivity architecture has been designed to be abstracted from user applications, in such a way that any application can be integrated with App adaptivity using its communication means and understanding its data formats. Hence, in addition to user applications created from scratch such as smartphone, web or desktop, App adaptivity can also be integrated into other platforms or applications through its API. As discussed, behaviours are defined in the PoCo and user applications display the information and data received to end users in the way that they have been configured. Taking into account the system goals, user applications should provide the mechanisms to access and interact with the underlying physical devices without the efforts of programming and configuring. This goal has been the key philosophy in the design of user applications, therefore configuration and behaviours are defined in the PoCo, whereas user applications are only responsible for displaying the behaviours defined, discovering RDs when the system requires it and establishing the users' locations, which do not require any configuration. Consider, for example, a smartphone application launched as a user application in Appdaptivity, the only configuration required is installing the application if the application logic has previously been defined. Suppose a smart building, the administrator could create the application logic associated with the building infrastructure. Then, workers only have to install the application to start using the smart building functionalities.  
Once the locations or the building flow have been established and started respectively, user applications start receiving the behaviours defined in the PoCo configuration, their corresponding data and the changes during the execution of the application. Figure 7 shows screenshots of a user application with an established location. Concretely, from top to bottom, Figure 7 displays an average temperature chart, a humidity average real-time value, and a group of light actuators for the interaction with. Figure 7 displays a pop-up window to manage a group of light actuators. The left button of the pop-up will turn on all lights belonging to the selected group once it has been pressed, whereas the center one will turn off all lights. User applications always contain up-to-date behaviours based on the state of the CoAP networks, and so in the case of a new or shutdown CoAP server deployment, user applications will be updated by the PoCo.  
Figure 6: PoCo configuration with building and location flows, access control, and different behaviours.  
The RD discovering can also be done by user applications, especially in cloud deployments where the PoCo cannot discover RDs by itself. It is important then that the discovering service takes into account the network changes, as for example, the connection to another Wi-Fi network, since RDs could be deployed in such networks. Lastly, although optionally, user applications can interact directly with the resources received with a CoAP client, otherwise they can use the request service available in the PoCo.
## 7 Overview of Deep Learning in 5G Security
### Relevant Deep Learning Techniques for 5G Security  
As the demands of 5G security continue to grow, researchers have explored a variety of deep learning techniques to create robust and efficient security solutions. Among these, CNNs, RNNs, and Deep Belief Networks (DBNs) have emerged as particularly effective methods for enhancing 5G security [140].  
CNNs are a type of deep learning model that are particularly well-suited for processing grid-like data, such as images and time-series data. In the context of 5G security, CNNs can be used to analyze the network traffic, identifying complex patterns and anomalies that may be indicative of security threats [141]. For instance, a CNN could analyze the traffic flow between different network nodes, identifying suspicious or irregular activities that might signal a potential cyberattack. Moreover, the convolutional layers in CNNs are efficient in detecting local patterns within the data, which can be instrumental in identifying localized attacks in the network [142].  
RNNs are another deep learning model that are uniquely equipped to handle sequential data by retaining information from previous inputs in their hidden layers. This makes them particularly relevant for 5G security, as they can be used to analyze sequential or time-series data such as network logs or packet flows, identifying patterns and anomalies over time that may suggest an ongoing or imminent security breach [143]. Furthermore, the recurrent nature of RNNs allows them to remember past events, which is essential for detecting slow, progressive attacks that unfold over a period of time.  
Table 14 provides a detailed overview of the role of deep learning techniques in the realm of 5G security, offering insights into the applications, strengths, and weaknesses of various approaches. From convolutional networks adept at image analysis to recurrent networks designed for sequence-based tasks, each method comes with its own set of advantages and limitations. While some excel in handling specific types of data or offer  
advantages in scalability, they may require significant computational resources or face challenges such as overfitting and model complexity.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### Caltech Pedestrian Dataset
#### 4.2.1 Training Configuration on Caltech Dataset  
We used a multi-scale training strategy to detect different scales of pedestrians. During training, the images were resized, on the short side, into 11 scales (608, 640, 672, 704, 736, 768, 800, 864, 896, 928, and 960). In the region proposal network, the anchors were generated with multiple scales (16, 32, 64, 128, and 256), and the stride was set to 8. Because the bounding box aspect ratio distribution was 0.41 on average, we set the anchor scale as (0.41, 0.5, and 0.7). For the Caltech pedestrian dataset, the momentum was set to 0.9. The initial learning rate of 0.001 was used to optimize the model with a total of 70,000 iterations. After 55,000 iterations, the learning rate was reduced by a factor of 10, and after 62,000 iterations reduced again. The experiments were performed with a batch size of 2, both in training and evaluation. It took about 12 h to train the model.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 1 Instruction
[16] demonstrated that the results from pattern recognition (error rates ranging from 8% to 19%) when the training and testing data are from the same level of force, were much better than the results (error rates ranging from 32% to 44%) when they were from different levels of force. [PERSON] et al. [17] studied the impact of different loads on the prediction accuracy of a joint angle based on sEMG, and they remarkably improved the estimation performance through multi-sensor fusion. Despite the brilliant results, this does not mean that the same conclusion certainly exists for gait recognition based on sEMG when the loads vary. In addition, few researchers have studied the influence of load variation on the accuracy of gait recognition from sEMG, so this area is still not well understood. To study this issue in-depth, we performed our experiment under four different loads at three speeds, and we employed back propagation neural networks (BPNNs) to obtain accurate gait recognition for every trial, and then we used the statistics to analyze the fluctuations in the accuracy of gait recognition with the load change at each speed. SVM and k-NN were used to compare the results, to clarify whether the result was caused by the algorithm or the load variation. Based on the experiment, we studied the impact of intra-loads and inter-loads on gait recognition accuracy, and we simulated the potential load state that is faced by people in real-time environments by combining the data from different loads to demonstrate (1) whether the load variation has a significant influence on gait recognition; and (2) whether an exoskeleton (or prosthesis) control system trained by sEMG signals from single or parts of loads still works well under multi-load conditions. The remainder of the paper is organized as follows. Section 2 describes in detail the processes and methods of the experiment. Section 3 presents the results of the experiment and analyzes the data, including the effect of load variation on the accuracy of gait recognition, the comparison results for the three algorithms, an analysis of differences between intra-loads and inter-loads in gait recognition accuracy, and an analysis of the influence of load variation on gait recognition in a simulated real-time environment. Section 4 discusses the reasons for our results, and Section 5 presents our conclusions.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 2 Backgrounds  
### Laser Atmospheric Transmission Theory  
When the laser propagates in the atmosphere, part of the optical radiation energy is absorbed and converted into other forms of energy (such as thermal energy, etc.); the total effect of absorption and scattering weakens the intensity of transmitted light radiation. The change in light intensity is the following:  
\[\frac{dI}{I_{0}}=\frac{I-I_{0}}{I_{0}}=-\beta dl \tag{1}\]  
\(I\) is the light intensity after the laser atmosphere transmission, \(I_{0}\) is the light intensity emitted by the laser, \(l\) is the transmission distance, \(\beta\) is the atmospheric extinction coefficient; the atmospheric transmissivity (\(\%\)) is \(\tau\) at the transmission distance \(L\):  
\[\tau=\frac{I}{I_{0}}=e^{-\int_{0}^{L}\beta dl} \tag{2}\]  
If \(\beta\) is constant at the transmission distance \(L\):  
\[\tau=\frac{I}{I_{0}}=e^{-\beta L} \tag{3}\]  
Equation (3) is [PERSON]'s law which is used to describe the atmospheric attenuation.
# ML-Based Materials Evaluation in 3D Printing
## 4 Discussion
### Directions for Further Studies  
Further research on machine learning-based materials evaluation in 3D printing aims to address existing challenges and explore new opportunities to improve the technology. Developing large, high-quality, and diverse datasets is a key direction to enable more accurate and generalizable machine learning models for materials evaluation. Incorporating multimodal data such as mechanical, thermal, and chemical properties along with printing conditions can improve model predictions and insights [81; 82]. Integrating physics-based ML models to combine domain knowledge with data-driven methods is promising for increased model robustness and interpretability. Advanced generative models, such as diffusion models, can be explored to design new materials tailored to specific 3D printing applications [83]. Extending the application of transfer learning to adapt models trained on one type of material or process to others can reduce the reliance on expensive experimental data. An improved real-time machine learning framework for in situ material monitoring during the printing process can enable dynamic adjustments to prevent defects and optimize results [84]. Expanding the use of reinforcement learning can help autonomously discover optimal printing parameters and material formulations [85]. Research into explainable AI (XAI) methods will help improve the interpretability of ML predictions, making them more accessible to materials scientists and engineers [86]. Developing ML algorithms for multi-material systems can address the complexities of material interactions in composite and hybrid 3D printing. Exploring the use of ML in sustainability, such as in predicting the recyclability and life cycle performance of 3D-printed materials, will support environmentally friendly manufacturing practices [87]. Research into integrating ML with advanced imaging and nondestructive assessment techniques can improve the detection of microscopic defects and improve material consistency. ML-based optimization of bio-based and biodegradable materials for 3D printing offers opportunities for developing sustainable technologies [88; 89; 90]. Expanding the use of digital twins in combination with ML to simulate the performance and degradation of materials over time can increase predictive capabilities [91]. Addressing issues related to model scalability and computational efficiency will be critical to implementing ML in industrial 3D printing environments. Collaborative research between academia, industry, and government agencies could lead to the creation of standard benchmarks and protocols for evaluating ML-based materials in 3D printing [92; 93]. These research directions aim to push the boundaries of what is possible in ML-based materials science, contributing to smarter, more efficient, and innovative 3D printing technologies [94; 95].
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 6 Conclusions  
A novel approach for improving the likelihood probability in MIMO systems that use one-bit ADCs was presented herein. The proposed method leverages detected data symbols to obtain a data-based likelihood probability, which is then combined with the pilot-based likelihood probability using a weighted approach. The optimization problem for the weighted combination was formulated by minimizing the expected MSE, and then a computationally efficient solution to the problem was derived. Simulation results demonstrated that the proposed method achieved an approximate SNR gain of 0.3 dB compared to conventional method by enhancing the accuracy of the likelihood probability.  
An interesting direction for future research is to extend to the approach to MIMO systems using low-resolution ADCs, specifically with 2-3 bits of resolution. To achieve this extension, the pilot-based likelihood probability can be obtained using the results from [11]. However, the data-based likelihood probability for the extended system is currently unknown, necessitating further investigation. Another area for future research is to obtain the weights in the optimization problem through an online manner. The proposed  
Figure 8: MSE for the proposed method based on different detection methods at SNR = 0 dB.  
weight are currently obtained using off-line manner, which makes it challenging to capture the variations in the channel. To address this limitation, investigating weight update based on the current channel conditions is necessary.  
The work of [PERSON] was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korea Government (MIST) (No. 2021R1F1A1063273).  
Not applicable.  
The author declares that there is no conflict of interest.
## 2 Various PPF Strategies
### PPF of Carboxy Monoliths  
A new precursor monolithic capillary column was prepared by the in situ co-polymerization of 2-carboxyethyl acrylate (CEA) and EDMA in a 300 \(\upmu\)m i.d. fused silica capillary yielding a poly(CEA-_co_-EDMA) monolithic column [39]. This carboxy monolith precursor with its surface carboxyl groups was subsequently subjected to PPF with octadecylamine (ODA) via a carbodiimide conjugation reaction in the presence of an organic soluble carbodiimide, \(N\),\(N^{\prime}\)-dicyclohexylcarbodiimide (DCC). see Scheme 28. The carbodiimide with its zero-length carboxyl-to-amine coupling ability works by activating the surface carboxyl groups of the monolith for direct reaction with the primary amines of ODA via amide bond formation. This reaction, which is applied for the first time in monolithic column fabrication, yielded the octadecyl-poly(CEA-_co_-EDMA) monolithic capillary column for use in reversed phase capillary LC. This octadecyl column was evaluated for its RPC retention property with alkylbenzenes (ABs) and alkyl phenyl ketones (APKs). The methylene group selectivity (\(\alpha_{\text{CH2}}\)) in the particular homologs was about the same for both ABs and APKs (\(\alpha_{\text{CH2}}=1.6\)) using the same mobile phase composition. This may indicate that the retention mechanism is the same for both homologous series. The retention behaviors of polyaromatic hydrocarbons and chlorophenol compounds were also examined on the octadecyl capillary column. The study showed good retention and selectivity of octadecyl-poly(CEA-_co_-EDMA) monolithic capillary column toward the various solutes under investigation. In addition, the column showed good permeability and reproducibility.  
The same precursor monolithic capillary column (carboxy monolith), namely poly(CEA-_co_-EDMA) was subjected also to PPF with Tris and D-Glucamine in the presence of _N_-(3-dimethylamineopropyl)-_N_\({}^{\prime}\)-ethylcarbodiimide hydrochloride (EDAC), a water soluble carbodiimide for the use in HILIC (see Scheme 29) [40]. Similarly, to DDC, the EDAC with its zero-length carboxyl-to-amine coupling ability works by activating the surface carboxyl groups of the monolith for direct reaction with the primary amines of Tris and D-Glucamine via amide bond formation. The Tris-amide (TA)- and D-Glucamide-poly(CEA-_co_-EDMA) monolithic capillary columns were evaluated using neutral and charged polar analytes, such as phenolic acids, nucleic acid bases and nucleosides. The difference in polarity between these monolithic capillary columns is noticeable in terms of the numbers of hydroxyl group per ligands, and therefore, the D-Glucamide-poly(CEA-_co_-EDMA) monolith bearing 5 hydroxyl groups per ligand could separate more polar species compared to the TA-poly(CEA-_co_-EDMA) monolith which has 3 hydroxyl groups per ligand. Solute retention on both types of HILIC columns were reproducible from run-to-run, day-to-day and column-to-column.  
**Scheme 29.** Schematic diagram showing the preparation of poly(CEA-\(\alpha\)-EDMA) monolith and its PPF with (I) Tris and (II) \({}_{\rm D}\)-Glucamine. Taken with permission from [40].