esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
###### Abstract  
This novel research introduces a game-changing architecture design for Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) decoders in Fifth-Generation New-Radio (5G-NR) wireless communications, specifically designed to meet precise specifications and leveraging the layered Min-Sum (MS) algorithm. Our innovative approach presents a fully parallel architecture that is precisely engineered to cater to the demanding high-throughput requirements of enhanced Mobile Broadband (eMBB) applications. To ensure smooth computation in the MS algorithm, we use the Sub-Optimal Low-Latency (SOLL) technique to optimize the critical check node process. Thus, our design has the potential to greatly benefit certain Ultra-Reliable Low-Latency Communications (URLLC) scenarios. We conducted precise Bit Error Rate (BER) performance analysis on our LDPC decoder using a Hardware Description Language (HDL) Co-Simulation (MATLAB/Simulink/ModelSim) for two codeword rates (2/3 and 1/3), simulating the challenging Additive White Gaussian Noise (AWGN) channel environment.  
wireless communication; 5G-NR standards; LDPC codes; min-sum algorithm; HDL Verifier; co-design
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Application of Reinforcement Learning Algorithm in the Development of Mobile Robot
#### 4.1.1 Path Planning, Optimization, and Obstacle Avoidance with RL
While studies have tested the importance of the RL method in an outdoor environment, another recent study by [PERSON] et al. [38] considered a multimodal deep reinforcement learning technique in a complex indoor environment. Like other scholars, the study demonstrated DRL's significance in learning multiple control policies using sensors. However, their reliable and robust multimodal DRL model overcame the difficulties posed by the heterogeneity of sensor modalities and the complicated indoor environment. It was further argued that the mobile robot model based on DRL had an associated high-performance rate in terms of convergence speed, average accumulated rewards, and success rate.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 2 The Instantaneous (Shannon) Spectral Entropy
For this reason, entropy-based approaches are particularly well-suited for the operational modal analysis of civil structures and infrastructures since the ambient vibrations can be easily approximated to a pure white Gaussian noise [23; 24]. However, the concept is still suitable for deterministic driving forces. As long as the input remains constant (or at least similar), any relevant variation in the output entropy can be directly linked to a structural change in the target system. As it will be shown later for the experimental case study, this condition is satisfied for wind turbines operating at similar wind speeds. Furthermore, this input dependence can be easily bypassed by pairing ISE values with wind speed readings and considering only threshold trespassings at a constant wind speed.
## 2 Methods and Framework
### Application Framework Based on LLMs  
The Application Framework based on LLMs is structured into six layers, as illustrated in Figure 2: the hardware layer, data storage layer, heterogeneous resource scheduling service layer, model training and inference layer, model repository layer, and platform management layer.  
Figure 1: BERT network input and coding.  
Figure 2: Application framework based on LLMs.  
The specific content of the platform is as follows:  
1. The hardware layer integrates GPUs, TPUs, sensors, and smart meters for efficient computational power and data collection from the power system and customers. This layer additionally comprises a data collection interface and a data transmission module.
2. The data storage layer utilizes the NFS, HBase/Neo4j, MySQL, GBase, and Redis databases to stores massive multi-source heterogeneous data. NFS offers distributed file systems for large-scale data storage and retrieval. HBase and Neo4j store structured and unstructured data, respectively. MySQL and GBase manage structured business data, while Redis stores temporary and cache data.
3. The heterogeneous resource scheduling service layer manages resource allocation, task scheduling, troubleshooting, and performance monitoring and optimization. It oversees computing assets like CPUs, GPUs, and TPUs, arranges computational tasks, detects and resolves system issues, and optimizes system performance.
4. The model training and inference layer includes a training warehouse, an inference engine, and a workflow for managing training datasets, deploying trained models for real-time or batch inference, and defining and overseeing model training and inference progress with complex workflows.
5. The model warehouse stores pre-trained LLMs, such as BERT, along with fine-tuned models tailored to specific application scenarios.
6. The platform management layer includes model deployment and management, data management, and resource allocation.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 5 Future Work  
The performance will be further improved in the new \"_WeakHEO 2\"_ version of our receiver, where the available hardware resources will be increased, allowing the integration of the navigation module into the processor within the FPGA. This will allow the orbital filter to provide aiding to the acquisition and tracking modules at a high rate to improve performance as shown in [26]. The orbital filter of the \"_WeakHEO 2\"_ receiver will directly filter the raw pseudorange and pseudorange rate observations instead of the single-epoch least square solution, with an expected higher navigation accuracy also in case of less than four GPS observations available. In addition, the iteration rate of the navigation output will be increased from 0.1 Hz to 1 Hz and the number of tracking channel increased from 6 to at least 12 channels. Furthermore, in the future we will also consider processing other frequencies and signals of other GNSS constellations to further improve the overall performance.  
The work presented in this paper was carried out in collaboration between all authors. [PERSON] was leading the project and contributed together with [PERSON] to the system requirement and implementation definitions as well as to the overall design discussions. [PERSON] was concerned with the definition the MTO scenario and system requirements, as well as with the design and the implementation of the navigation module (adaptive orbital filter). He also contributed to carry out the simulation tests and hardware-in-the-loop tests of the navigation performance. [PERSON] contributed to the testing and design verification detailed in this paper and is now responsible for the maintenance and development of the _WeakHEO_ and _WeakHEO 2_ receivers. [PERSON] and [PERSON] contributed respectively to the design and the hardware implementation of the acquisition and of the tracking modules. [PERSON] contributed to the supervision of the overall FPGA design and implementation of the signal processing engine. [PERSON] contributed to the implementation of the orbital filter. All authors contributed to revisions of the manuscript.  
The authors declare no conflict of interest.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 5 Barcode Data Extraction
### Transform a Square Grid Format Image to an Edge Image  
Based on the two label matrices produced by the CNN models, a new square grid is constructed and transformed, which size is the same as the barcode size. The common gridlines of the two adjacent cells are transformed by using a sliding window algorithm again in the grid. A horizontal sliding window with the size of 1 x 2 cells is applied to scan the grid horizontally, as shown in Figure 8. We look up the label values in the horizontal pair label matrix for every step of the sliding window. If a label value is 0, the common edge of the two adjacent cells is removed. If a label value is 1, no additional operation is needed.  
Figure 7: Demonstration of the inputs and outputs of the CNN model for a 12 × 12 DM barcode. (**a**,**c**) The horizontal and vertical sliding window sampling in the grid format of the barcode image, respectively; (**b**,**d**) the label matrix for the horizontal and vertical pairs, respectively.  
After the horizontal scanning process, a vertical sliding window continues to work on the grid. As shown in Figure 9, a vertical sliding window with the size of \(2\times 1\) cells is used. The operation in the vertical scanning process is similar to the horizontal one. After implementing the scanning process in two directions, an edge image for the barcode is formed, as shown in Figure 10a. Then, the outermost borders of the grid in the edge image are removed or retained by using the information of the DM Timing Pattern, as shown in Figure 10b. In addition, Figure 10c shows the transformed edge image.  
Figure 8: Demonstration of the first step of transforming the grid: (**a**) the horizontal pair label matrix; (**b**) scan and transform the grid; (**c**) the result of transformation.  
Figure 10: An edge image generation process: (**a**) a barcode image; (**b**) the edge image with solid line border; (**c**) modify the outermost border of the grid; (**d**) the modified edge image.  
Figure 9: Demonstration of the second step of transforming the grid: (**a**) the vertical pair label matrix; (**b**) scan and transform the grid; (**c**) the result of transformation.  
### Transform a Square Grid Format Image to an Edge Image  
Adding or removing the edge between two adjacent vertices will lead to increasing or decreasing degrees of the vertices in the edge image. It means that the wrong edges in the edge image are related to the degrees of the vertices, and a correction algorithm is proposed. The degree of any vertex in the standard barcode edge image can be only 0, 2, or 4, as shown in Figure 11b. When the edge image is not standard, there are some odd-degree vertices, such as 1 or 3, as shown in Figure 11c. Therefore, the proposed correction algorithm can correct the edge image by eliminating these odd-degree vertices. The process of the proposed correction algorithm is divided into two steps: (1) pairing odd-degree vertices in the edge image; and (2) eliminating odd-degree vertices in the edge image.
###### Abstract  
Variations in methane-ammonia blends with hydrogen enrichment can modify premixed flame behavior and play a crucial role in achieving ultra-low carbon emissions and sustainable energy consumption. Current combustion units may co-fire ammonia/methane/hydrogen, necessitating further investigation into flame characteristics to understand the behavior of multi-component fuels. This research aims to explore the potential of replacing natural gas with ammonia while making only minor adjustments to equipment and processes. The laminar burning velocity (LBV) of binary blends, such as ammonia-methane, ammonia-hydrogen, and hydrogen-methane-air mixtures, was investigated at an equivalence ratio of 0.8-1.2, within a constant volume combustion chamber at a pressure of 0.1 MPa and temperature of 298 K. Additionally, tertiary fuels were examined with varying hydrogen blending ratios ranging from 0% to 40%. The results show that the laminar burning velocity (LBV) increases as the hydrogen fraction increases for all mixtures, while methane increases the LBV during blending with ammonia. Hydrogen-ammonia blends are the most effective mixture for increasing LBV non-linearly. Enhancement parameters demonstrate the effect of ternary fuel, which behaves similarly to equivalent methane in terms of adiabatic flame temperature and LBV achieved at 40% hydrogen. Experimental data for neat and binary mixtures were validated by different kinetics models, which also showed good consistency. The ternary fuel mixtures were also validated with these models. The Li model may qualitatively predict well for ammonia-dominated fuel. The Shrestha model may overestimate results on the rich side due to the incomplete N\({}_{2}\)H\({}_{3}\)sub-mechanism, while lean and stoichiometric conditions have better predictions. The Okafor model is always overestimated.  
laminar burning velocity; ternary fuel blending; ammonia; methane; hydrogen 2023
## 2 Materials and Methods
### Py-GC-MS Conditions and Analyses  
The goodness-of-fit and feasibility of the digestion protocol were confirmed through qualitative chemical analysis using Py-GC-MS. To evaluate whether the selected digestion approach (KOH 5 M at 50 \({}^{\circ}\)C for 48 h) enabled the identification of the chemical composition of microplastics, 0.1 \(\upmu\)g g\({}^{-1}\) of PET and PS standards were added to 1.0 g of cheese. In brief, polymer standard solutions were prepared by dissolving PET particles in 1,1,1,3,3,3-hexafluoro-2-propanol. Specifically, for the PET stock solution (1000 \(\upmu\)g g\({}^{-1}\)), 5.0 mg of PET particles were weighed and mixed with 5 mL of 1,1,1,3,3,3-hexafluoro-2-propanol. PET standard solutions at concentrations of 10 and 1.0 \(\upmu\)g g\({}^{-1}\) were prepared by diluting the stock solution. Then, 50 \(\upmu\)L of the 1000 \(\upmu\)g g\({}^{-1}\) solution were spiked into the sample. A stock solution of PS (1000 \(\upmu\)g g\({}^{-1}\)) was prepared by dissolving 5.0 mg of PS particles in 5 mL of a mixture of dichloromethane and toluene (1:1 \(v/v\)). PS standard solutions at concentrations of 10 and 1.0 \(\upmu\)g g\({}^{-1}\) were prepared by diluting the stock solution. Subsequently, 50 \(\upmu\)L of the 1000 \(\upmu\)g g\({}^{-1}\) solution were spiked into the sample. The sample was digested using KOH 5 M at 50 \({}^{\circ}\)C for 48 h, following the previously described method. The experiments were conducted in triplicate. Subsequently, the solutions were filtered using glass fiber filters (GF/F, Whatman, pore size 1.6 \(\upmu\)m, diameter 47 mm) with a vacuum system, concentrating the filtrate only in the central part (thickness 1 mm, length 3 mm) of the GF/F filters. The filters were then analyzed using Py-GC-MS.  
For the qualitative analysis of spiked PET, a pyrolyzer (CIS 6 Gerstel) was utilized. The temperature program for the CIS pyrolyzer was as follows: it was held at 70 \({}^{\circ}\)C for 0.1 min, then increased by 12 \({}^{\circ}\)C/s until reaching 600 \({}^{\circ}\)C, and the temperature was held for 4 min. The CIS pyrolyzer also allowed on-column injection. GC separation was performed using a Rxi-17 Sil capillary column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m, Restek, Bellefonte, PA, USA). The GC temperature program was as follows: it was held at 90 \({}^{\circ}\)C for 1 min, then increased by 30 \({}^{\circ}\)C min\({}^{-1}\) up to 250 \({}^{\circ}\)C, followed by an increase of a 4 \({}^{\circ}\)C min\({}^{-1}\) up to 330 \({}^{\circ}\)C, and was then held for 0 and 5 min, respectively. Helium was used as the gas carrier at a flow rate of 1.2 mL min\({}^{-1}\). The liner was replaced at the end of each chromatographic analysis. The mass spectrometer was operated in EI-positive mode with an energy of 70 eV, scanning the \(m/z\) range from 29 to 400 at a rate of 7 scans s\({}^{-1}\). The GC/MS transfer line, ion source, and quadrupole mass analyzer temperatures were set at 300 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 3 Underwater SLAM: Background and Overview  
Underwater SLAM is a critical technology for enabling AUVs to navigate and map complex, GNSS-denied environments. With applications ranging from marine exploration and environmental monitoring to infrastructure inspection and resource mapping, underwater SLAM plays a pivotal role in advancing underwater robotics. However, the aquatic environment presents unique challenges, including limited visibility, dynamic conditions, sensor noise, and communication constraints, which complicate the deployment of traditional SLAM methods. Over the years, researchers have developed specialized techniques to address these challenges, evolving from early adaptations of terrestrial SLAM to advanced, multi-sensor fusion and deep learning-based approaches. This section provides an overview of the key challenges, the evolution of underwater SLAM, and the growing importance of UWSNs in advancing the field.
## 3 Robot Calibration Principle
### Comparison of Calibration Methods for Dual-Robot  
Combining the existing dual-robot calibration methods: handshake method [30; 31] and coordinate measuring machine method [27], the three calibration methods are compared in terms of accuracy, cost, human factors, and robot workspace limitation. The comparison results are shown in Table 1. The meanings of \"-\", \"o\" and \"+\" in Table 1 are poor, medium, and good.  
The handshake method is limited by the working space of the robots, which requires that the working spaces of the two robots overlap. Since the operator needs to manually control the connection of the two robot end adapters, the results of this calibration method will be affected by human factors and produce errors.  
The coordinate measuring machine method has good calibration accuracy, but the equipment used for calibration is expensive, and there are difficulties in practical application.  
The accuracy of the three-point measurement calibration method meets the calibration requirements. The coordinate data of the robot calibration point comes from the robot data and the wire sensor measurement and is not affected by the human factors of the operator. This method does not have the limitation of the working space of the robot and the cost is very low, so it has good practicability when used in the actual factory.
## 2 Materials and Methods
### Electrophoretic Mobility Shift Analysis (EMSA)  
For analysis of KpSSB binding, a biotinylated deoxythymidine (dT) homopolymer dT30 was employed as a standard assay. The ssDNA dT30 was biotinylated at its 5\({}^{\prime}\) terminal. Incubation of this labeled ssDNA (30 fmol/uL) was with purified KpSSB at various concentrations (0, 20, 39, 78, 156, 312, 625, 1250, 2500, and 5000 nM). The electrophoretic mobility shift analysis (EMSA) was conducted using the LightShift Chemilluminescent EMSA Kit. Briefly, KpSSB and the DNA substrate were incubated together for 60 min at 37 \({}^{\circ}\)C in 40 mM Tris-HCl (pH 7.5) and 50 mM NaCl. After adding a dye mixture, an 8% native polyacrylamide gel was used. The reaction samples were analyzed at 100 V for 1 h in TBE running buffer. The protein-DNA complexes were electroblotted onto a positively charged nylon membrane (GE, Boston, MA, USA). A UV-light instrument equipped with 312 nm bulbs was used for a cross-linking reaction. After a 10-min exposure, the transferred DNA was then cross-linked to the nylon membrane. The streptavidin-horseradish peroxidase conjugate and chemilluminescent substrate (Pierce Biotechnology, Waltham, MA, USA) were used for detecting the DNA. The [Protein]\({}_{50}\) was estimated based on the concentration of the protein that bound 50% of the input DNA.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 3 Method  
### Gaussian Dynamic Convolution  
The Gaussian dynamic convolution was first proposed to efficiently deal with the segmentation problem for the single image [15]. This work will introduce the Gaussian dynamic convolution to construct convolutional networks to handle the problem of large scales of objects in high-resolution remote sensing images. Here we first give an example to illustrate the principle of GDConv implementation.  
For an ordinary \(3\times 3\) convolutional kernel, a feature map is obtained by sliding on the input layer. Assuming that the coordinates of the center of the convolutional kernel are \(d=(x,y)\), the coordinates of the positions in the remaining eight directions can be represented by d plus the set of orientations s and the offset \(\Delta\):  
\[\begin{split}&\tilde{d}_{i}=d+\Delta_{i}\odot s_{i}\\ & s_{i}\in\{\langle-1,-1\rangle,<-1,0>,\cdots,<1,1>\},\Delta=<1,1 >\end{split} \tag{1}\]  
For example, the feature vector in the lower left corner can be expressed as:  
\[\langle x,y\rangle+\langle-1,-1\rangle\odot\langle 1,1\rangle=\langle x-1,y-1\rangle. \tag{2}\]  
We illustrate a toy example of GDConv in Figure 2. We first fix the center weights and then give a random offset \(\Delta\) to each direction, the offset is obtained from a two-dimensional Gaussian distribution with standard deviation \(\sum\). The Gaussian distribution equation is:  
\[\text{Gaussian}(0,\Sigma)=\frac{\sqrt{2}}{\Sigma\sqrt{\pi}}\exp\biggl{(}- \frac{x^{2}}{2\Sigma^{2}}\biggr{)}. \tag{3}\]  
We can see that the dynamic receptive field breaks the shape limitation. Different from the deformable convolution of which the offset needs to be learned from the previous feature map by an additional convolution layer, the GDConv is able to change the offset dynamically.
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 6 Some Important Applications of Hydrogels in Transdermal Drug Delivery Systems
### Cancer Therapy  
Traditional cancer treatments, especially chemotherapy, carry the risk of systemic side effects such as reduced bone marrow function, liver or kidney failure, and neurological issues. Hydrogels present significant potential for research in this area. To tackle these challenges, it is essential to control drug release, targeting the medication directly to the tumor site, particularly when integrated with tissue engineering techniques. Hydrogel patches equipped with microneedles have been shown to be particularly effective for drug delivery in cancer treatment. A specially designed transdermal patch exhibited a significant release of two milligrams of anastrozole targeted for breast cancer treatment but caused harm to the adjacent healthy tissues. In a separate investigation, another study detailed the transdermal delivery of isoliquiriigenin through a hydrogel formulated with hyaluronic acid and hydroxyethyl cellulose. This drug is recognized for its ability to impede pancreatic cancer progression [84].
## 2 Materials and Methods
### Bayesian Network Model  
To comprehend the relationships between the baseline data and our outcome, we developed a Bayesian network model. A Bayesian Network is a directed acyclic graph comprising nodes and edges, utilised to construct an approximation of the joint probability distribution over all variables and the outcome of interest. This joint probability distribution provides all necessary information for making probabilistic inferences on one variable given knowledge of the other variables in the distribution. Nodes represent variables, while directed edges, depicted by arrows, elucidate relationships among these variables [28].
## 2 Material and Methods
### SVAT Model and SMAI
#### 2.1.1 The Soil Moisture Availability (SMA) Concept  
For assessing land surface moisture state, the soil moisture availability (SMA) concept is adopted to serve as an information source for 'warnings' of environmental constraints. Based on the 'SVAT_bg' model outputs of SM, a quantitative index soil moisture availability index, SMAI has been developed and operationally calculated at a site scale for the region of each NIMH synoptic station twice per day (0600 UTC and 1800 UTC) for 4 soil layers: 0-5 cm; 0-20 cm; 0-50 cm; 0-100 cm. SMAI is applied as a diagnostic tool in the case of soil overmoistening conditions and flood risk [42], and also works in the case of dry environmental conditions, accounting for drought severity [36; 45].