esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### List Decoding Algorithm for Polar Codes from the Perspective of Information Geometry  
In this subsection, leveraging the theory of information geometry and utilizing the characteristics of the soft outputs from the BP (Belief Propagation) decoding of polar codes, a geometric framework is constructed for the BPL decoding algorithm of polar codes. Firstly, the statistical manifold where the soft information resides is analyzed, with each soft information output from a BP decoder regarded as a point on this statistical manifold. Then, the metric matrix of this statistical manifold is calculated.
## 3 Results
### Kinetic Data Analysis  
A two-way ANOVA of kinetic data identified significant interaction (speed*shoe) for peak vertical impact (F\({}_{(2,22)}\) = 11.44, \(p\) = 0.005, Figure 3A), with a main effect for speed (F\({}_{(2,22)}\) = 22.93, \(p\) < 0.0001) but not shoes (F\({}_{(1,11)}\) = 0.916, \(p\) = 0.361). Key post hoc multiple comparison between shoes indicated decreased impact differences for cushioned shoes versus minimalistic control at both 10 km\(\cdot\)h\({}^{-1}\) (1.42 vs. 1.32 N\(\cdot\)BW\({}^{-1}\), \(p\) = 0.009) and 14 km\(\cdot\)h\({}^{-1}\) (1.72 vs. 1.51 N\(\cdot\)BW\({}^{-1}\), \(p\) = 0.005). There was also a significant interaction (speed*shoes) for average loading rate (F\({}_{(2,22)}\) = 5.456, \(p\) = 0.013, Figure 3C), with main effects for both speed (F\({}_{(2,22)}\) = 71.05, \(p\) < 0.0001) and shoes (F\({}_{(1,11)}\) = 6.596, \(p\) = 0.028). Key post hoc multiple comparisons between shoes indicated decreased average loading rate differences for cushioned shoes versus minimalistic control at both 10 km\(\cdot\)h\({}^{-1}\) (44.9 vs. 39.7 N\(\cdot\)BW\({}^{-1}\)\(\cdot\)s\({}^{-1}\), \(p\) = 0.008) and 14 km\(\cdot\)h\({}^{-1}\) (61.4 vs. 54.7 N\(\cdot\)BW\({}^{-1}\)\(\cdot\)s\({}^{-1}\), \(p\) = 0.004), but not 6 km\(\cdot\)h\({}^{-1}\) (14.9 vs. 14.6 N\(\cdot\)BW\({}^{-1}\)\(\cdot\)s\({}^{-1}\), \(p\) > 0.999).  
Figure 2: Force deformation curves and corresponding values of energy absorption, energy return, heat dissipation, and elastic return (%) for the minimalist-control shoe (\(-\)) and the cushioned shoe (\(-\)).  
There was no speed*shoe interaction (F\({}_{(2,22)}\) = 0.427, \(p\) = 0.659, Figure 3B) or main effect of shoe (F\({}_{(1,11)}\) = 3.695, \(p\) = 0.084) for active peak force. However, there was an increased main effect for speed (F\({}_{(2,22)}\) = 458.3, \(p\) < 0.0001). Time to active peak force presented no speed*shoe interaction (F\({}_{(2,22)}\) = 3.444, \(p\) = 0.052), but there were decreased main effects for speed (F\({}_{(2,22)}\) = 4647, \(p\) < 0.0001) and increased main effects for shoe (F\({}_{(1,11)}\) = 6.596, \(p\) = 0.028). However, shoes only presented post hoc differences at 6 km\(\cdot\)h\({}^{-1}\) (0.449 vs. 0.462 s\({}^{-1}\), \(p\) = 0.0004).  
There was no speed*shoe interaction for step impulse (F\({}_{(2,22)}\) = 0.365, \(p\) = 0.204) or main effect of shoe. However, there was a decreased main effect of speed (F\({}_{(2,22)}\) = 1023, \(p\) < 0.0001, Figure 3D).
## 2 Materials and Methods
### Methodology
#### 2.2.3 Experimental Procedure  
To enhance readers' understanding, a visual explanation in Figure 4 of the combined PCA and TOPSIS methodologies is provided. This approach leverages PCA's capability to reduce dimensionality and extract key information, along with TOPSIS's strength in evaluating and ranking alternatives based on multiple criteria. Together, these methodologies offer a robust tool for decision making in port terminal management and ranking many container terminals according to several criteria. Technical (computational) procedures, like MATLAB and Excel, were used for the calculations in the research and outcomes (results) were obtained.  
The combined PCA and TOPSIS methodology involves several key steps. Initially, relevant data about selected ports are collected to create a comprehensive database. In the PCA application phase, the data are normalized to standardize the units of measurement, and Principal Component Analysis (PCA) is applied to reduce data dimensionality, thereby determining the weights of the criteria based on the principal components. Following this, the TOPSIS application phase begins with the construction of a weighted decision matrix by multiplying the normalized values by the determined weights. The best and worst possible values for each criterion, termed ideal and anti-ideal solutions, are identified. Subsequently, the distances of each port to these ideal and anti-ideal solutions are calculated, and the relative proximity coefficient of each port to the ideal solution is determined. Finally, the ports are ranked based on their proximity coefficients, with the highest coefficient indicating the most preferred port.  
In summary, TOPSIS is distinguished by its simplicity, ease of use, intuitiveness, and effective handling of multiple criteria. It provides a clear and rational approach to ranking alternatives, combining straightforwardness with analytical rigor.
# DM-SLAM: Monocular SLAM in Dynamic Environments
## 2 Materials and Methods.
### DLRSAC for Reconstructing Static Map
We use the 8-point method when solving the fundamental matrix, so the number of molecular variables is 8. The smaller the molecule, the closer the 8 feature points used in the solution model, and the higher the confidence that they also belong to our motion model. The denominator is the distribution of the interior points belonging to the model. The larger the distribution, the more feasible the model should be adopted as the reference system. In other words, the probability that we think it is camera motion model increases with the distribution. Before we describe the algorithm flow, some definitions need to be clarified in advance. _Grid:_ We will divide the image into \(l*m\) grids of the same size. _Grid model:_ Fundamental matrix represents the current grid solved by RANSAC [8] with the matched feature in current grid. _Couple:_ Refers to the test of how many inliers in tested grid satisfy the _Grid model_. _Couple Degree:_ The result of Couple divided by the total number of inliers in the tested grid. _Coiling Grid:_ Testing grid or testing grid model in the process of Coupling. _Coupled Grid:_ Tested grid or tested grid model in the process of Coupling. _Couple Matrix:_ Consists of Couple Degrees. A row of elements in the _Coupling Matrix_ share a common _Coupling Grid_, and the _Coupled Grid_ of same column of elements is the same. _Coupled Grid set:_ Consists of the grids whose _Couple Degree_ with _Coupling Grid_ is greater than a certain threshold and _Coupling Grid_ itself. _Grid model distribution:_ Refers to the geometric distribution of Coupled Grid set of one Grid model. Algorithm flow is described as below:  
1. Extract features in the frames used for initialization and match them. 2. Divide the image into l \(\times\)m grids, assign features into grids according to whether features belong. For each _Grid_, if the number of features doesn't satisfy the solution condition, it is discarded. Then, solve the centroid with all the feature points within these grids which pass above check. 3. For each grid, normalize all the features, then use RANSAC to solve the _Grid model_ with the normalized features. 4. Constructing _Couple Matrix_ by calculating _Coupled Degree_ of each grid with other grids. 5. Get _Coupled Grid set_ of each _Grid model_ by setting certain threshold, calculate distribution of the set using the centroids of the grids in the set. 6. Select the _Grid model_ with the largest _Grid model_ distribution as the camera pose, the features in grids contained in the _Coupled Grid set_ of selected grid are considered to be candidate static feature points. 7. Triangulate static feature points in step 6, initialize map with successful triangulated features. 8. Use bundle adjustment to adjust 3D map points and camera pose.
## 3 Results and Discussion
### Multivariate Statistical Analysis

Table 15: Concentrations (\(\mu\)g/L) of sulfur containing compounds found in Malvazija istarska white wines produced by fermentation with _Saccharomyces cerevisiae_ (SCE) and _Saccharomyces paradoxus_ (SPA) yeasts in two harvest years determined by untargeted two-dimensional gas chromatography with time-of-flight mass spectrometry (GC \(\times\) GC/TOF-MS). which were consistent across two harvest years, multivariate statistical analyses were conducted using HCA and PLS-DA. To reduce the impact of variations due to harvest year, the data were normalized within each year. Two separate reduced datasets were analysed. The first dataset included variables, such as basic physico-chemical parameters (Table 1), PR proteins (Table 2), and grape-derived phenolic compounds (Table 3), which showed statistically significant differences based on one-way ANOVA of the normalized data. The second dataset included 40 volatile compounds (Tables 4-17) selected for their strong statistical difference (the highest _F_-ratio values) determined by one-way ANOVA applied on the normalized data. Additional volatile compounds frequently cited as key wine odorants were also included in the second dataset. Using the first dataset, clear clustering of wines according to yeast species was achieved, as illustrated in the heatmap in Figure 1. The control SCE wines from both harvest years showed higher levels of organic acids, including malic acid, as well as several phenols, particularly _p_-coumaric acid and _trans_-resveratrol. These wines also had elevated levels of specific PR proteins, notably a group of chitinases. SPA wines were consistent in having higher levels of pH, glycerol, tartaric acid, volatile acidity, and particular thaumatin-like PR proteins, compared to control SCE wines. PLS-DA analysis of the first dataset successfully differentiated wines from both harvest years based on yeast species (Figure 2a). The top fifteen volatile compounds with the highest VIP scores are presented in Figure 2b. Compounds with the highest VIP scores (>1) included _trans_-resveratrol, malic acid, citric acid, and _p_-coumaric acid, which were more characteristic of SCE wines. In contrast, SPA wines were distinguished by elevated levels of glycerol, pH, and volatile acidity.
## 3 Specimen and Experimental Setup
### Experimental Setup  
A schematic diagram of the air-coupled ultrasonic testing experimental setup is presented in Figure 6. The Signal generator Tektronix AFG3102 (Tektronix, USA) is used to produce an exciting signal with a specific coded sequence and bandwidth (33-bits P3 polyphase coded sequence with 3-cycles per-pulse and -6 dB bandwidth is 138 kHz). Then, the signal is amplified by Power Amplifier (AR-75A250A, AR, USA) to excite Transmitter (YiChao, focused, the resonance frequency is 400 kHz and the diameter is 30 mm, Yichao, Suzhou, China). The excited ultrasonic signal passes through honeycomb sandwich composite sample and is received by Receiver ([PERSON], focused). Then, the weak detected signal passes to Gain Amplifier and Bandpass Filter device (Panametrics 5800, Olympus, Japan) for gain compensation and filtering, acquired by an 8 bits, 250 Msps ADC (NI PCI-5114), which is embedded in PC. Then, the acquired data is transmitted to PC for processing operation. In conjunction with mechanical scanning module, including mechanical two-dimensional scanning frame (QMI LS-06), motion controller (QMI MC-ST-02), servo motor, etc., the air-coupled ultrasonic testing system can perform C-scan of honeycomb sandwich composite sample.
# Analysis of AMI Communication Methods in Various Field Environments
## 7 Conclusions  
In order to support future smart grid-related power services, introducing various AMI communication methods, such as wired and wireless approaches, are required. For such AMI communication approaches, generally required technical specifications, such as the frequency bandwidth and band, modulation methods, transmitter powers, and receiver sensitivities, are well-known. However, experimental analyses of actual AMI communication environments are insufficient under such specifications. In this paper, we considered the HS PLC, Wi-SUN, and ZigBee modems to quantitatively analyze the communication performances for AMI through both experimental testbeds and practical AMI sites. For practical AMI sites, we selected 18 sites with 48 MPs and classified the sites into five areas: Overhead Residential Area, Overhead Commercial Area, Overhead Rural Area, Overhead Factory Area, and Underground Area. We conducted measurements of signal and noise power spectra on 48 MPs, and derived linear regression models for received powers according to the areas. In general, the wireless communication method can provide longer communication distances for the overhead power line case. However, the performance of the wireless communication method severely deteriorates when a LOS path is not guaranteed. Thus, at the areas where a lot of obstacles between the transmitter and receiver antenna are present at high-density consumer areas such as underground power line case, PLC methods can provide better performances than wireless communication. Through the constructed models, we can efficiently choose an appropriate communication method and plan a methodology for building an AMI network. Finally, in order to visualize the communication performance of constructing AMI networks, we provided graphical simulation tools for both PLC and wireless communication methods for AMI based on NDIS. Electricity providers can utilize the results of this research to select an appropriate communication method according to the types of customers and to maintain an appropriate quality of services, such as meter reading success rate, tariff download, and power remote control.  
[PERSON] conducted the experiments and regression analyses in the wireless communications, and organized and refined the manuscript. [PERSON] derived the issues of evaluating the communication methods for AMI environments and managed the measurement experiments for practical AMI sites. [PERSON] conducted the experiments and regression analyses in the power line communication. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grant No. C11J180034 from the Korea Electric Power Corporation (KEPCO) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A6A1A03032119). This research for [PERSON] was financially supported by Hansung University.  
The experiments of this research was supported by [PERSON], KEPCO, Republic of Korea.  
The authors declare no conflict of interest.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 3 Methodology
### Method Overview  
The main goal of our model is to generate an image \(y_{c}\) that maintains the same content as the input content image glyph \(x_{c}\), but with the font style based on a set of reference/style images \(y=\{y_{1},y_{2},\ldots,y_{k}\}\), where \(k\) denotes the number of sample images. This can be expressed as:  
\[y_{c}=G(x_{c},y). \tag{1}\]  
Figure 1 shows the architecture of the model. Our model consists of a typical generator and a discriminator. The generator comprises three parts that work jointly to generate font images, as shown in Figure 1. The first part is a content encoder, which conditions the model to generate images with specific content. The second part is an attention based multitask style encoder used to disentangle style from style images at both global and local style levels. Third, we have a multi-layer Adaptive Instance Normalization-based decoder that takes character content code and style code. The decoder's input style code is the concatenated representation of global style embedding and patch-based attention applied to patches of style images. Importantly, we used Adaptive Instance Normalization (AdaIN) [24; 25] in the decoder to effectively inject the style code for generating font images. Lastly, a multitask discriminator not only predicts between real and fake images but also classifies the content and style of generated images based on target images.  
Figure 1: Overall Architecture. The style encoder extracts global style (\(s_{g}\)) and local patch embeddings (\(s_{i}\)) from reference glyphs. A multi-head patch attention module weights each patch’s local style relevance. The decoder fuses these style embeddings with the content code using AdaIN at multiple layers.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
###### Abstract  
Multispectral detection leverages visible and infrared imaging to improve detection performance in complex environments. However, conventional convolution-based fusion methods predominantly rely on local feature interactions, limiting their capacity to fully exploit cross-modal information and making them more susceptible to interference from complex backgrounds. To overcome these challenges, the YOLO-MEDet multispectral target detection model is proposed. Firstly, the YOLOv5 architecture is redesigned into a two-stream backbone network, incorporating a midway fusion strategy to integrate multimodal features from the C3 to C5 layers, thereby enhancing detection accuracy and robustness. Secondly, the Attention-Enhanced Feature Fusion Framework (AEFF) is introduced to optimize both cross-modal and intra-modal feature representations by employing an attention mechanism, effectively boosting model performance. Finally, the C3-PSA (C3 Pyramid Compressed Attention) module is integrated to reinforce multiscale spatial feature extraction and refine feature representation, ultimately improving detection accuracy while reducing false alarms and missed detections in complex scenarios. Extensive experiments on the FLIR, KAIST, and M3 FD datasets, along with additional validation using SimuNPS simulations, confirm the superiority of YOLO-MEDet. The results indicate that the proposed model outperforms existing approaches across multiple evaluation metrics, providing an innovative solution for multispectral target detection.  
m +
Footnote †: journal: applied sciences  
ultispectral pedestrian detection; cross-modal feature fusion; attention mechanisms; multiscale feature extraction; SimuNPS
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Results
#### 5.2.4 Energy Consumption Efficiency
Moreover, the popular applications in the WBANs are of the normal traffic type, which can easily satisfy the QoS through the WiFi network with low energy transmission in comparison to UMTS/LTE. The default connection to the WiFi, MADM with CCN approach helps to maintain the current WiFi connection and minimize the number of handovers by integrating CCN and a combination of different QoS and user preferences. These are the reasons that lead to the energy consumption efficiency of our proposed approach (which reaches 56.09%) in comparison with MADM without CCN (28.33%) and RSS-VHD (11.68%), as shown in Figure 6b.
## 3 System Architecture and Test Environment  
The proposed traffic-management system aims to analyze the traffic at an intersection located between Bouskoura and Casablanca. For this purpose, we installed an IP camera connected to an Nvidia Nano Jetson card, which serves as our processing unit. Figure 1 shows some of the images captured during the installation. Our system functions as a smart camera capable of detecting and tracking vehicles in real time. One of the most important features of our camera is that it follows the concept of edge computing, which means that the analysis is performed directly on the device, and only the counting results are transmitted. This has two main advantages: first, we do not need to transmit the images, which reduces bandwidth requirements; second, we respect the privacy of citizens, which is a crucial consideration for any application deployed in a public place.  
The system architecture of the proposed traffic management system is composed of several components, including data acquisition, object detection and classification, object tracking and identification, counting, and data transmission. The data-acquisition component captures video footage of the traffic at each intersection using a smart camera and statistics are calculated and transmitted via the MQTT protocol. The object detection and classification component uses the YOLOv7-tiny algorithm for vehicle identification and classification. The object tracking and identification component uses the Deep SORT algorithm to track and identify vehicles. The counting component combines image processing techniques and deep-learning algorithms to count the number of vehicles passing through  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Model** & **Cost** \\ \hline IP camera & \multicolumn{1}{c}{Hikvision Varifocal Motorized Bullet Camera} & USD 270 \\ \hline AI Processing Unit & \multicolumn{1}{c}{Nvidia Jetson Nano 4 GB Development Kit} & USD 214.63 \\ \hline Accessories (cabling, protection, etc.) & \multicolumn{1}{c}{Approximately USD 200} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost breakdown of equipment used in the solution.  
Figure 1: Installation images of our smart vehicle counter system.  
each lane. Finally, the data-transmission component is responsible for transmitting the processed traffic data to a centralized server for further analysis and decision-making via the MQTT protocol.  
Figure 2 shows the architectural overview of the proposed system, including its components.
# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 2 Related Works and Methods
### Methods  
#### 2.2.1 HS-YOLO Algorithm  
The current general detection algorithms do not perform well in detecting small objects when directly applied to power operation scenes. This requires improving the object detection algorithms according to the specific situation in the special electric power operation scenario. The YOLO [27] series of object detection algorithms have been widely applied due to their efficiency, high accuracy, strong scalability, and interpretability. YOLOv5 enhances the detection capabilities of small objects through the adoption of an anchor-free detection framework and the incorporation of operations such as the SPP module. Therefore, to address the challenge of achieving accurate detection in intricate power operation scenarios, we propose the HS-YOLO algorithm based on the YOLOv5 network. The HS-YOLO algorithm is illustrated in Figure 1.  
The HS-YOLO algorithm introduces HRNet [28] and sub-pixel convolution [29] to target small objects in power operation scenes. Figure 1 illustrates the network architecture partitioned into three parts: Backbone, Neck, and Head, which are responsible for feature extraction, fusion, and final detection, respectively. The overall flow of the HS-YOLOalgorithm is as follows: First, the algorithm utilizes HRNet to extract object features from the surveillance images. Distinct branches handle feature maps of diverse scales and subsequently amalgamate the features from those scales, yielding feature maps denoted as C1, C2, and C3, which preserve a wealth of feature information. Then, these three features are input into the Neck part, where subpixel convolution is introduced, and feature maps of different scales, N1, N2, and N3, are obtained. Finally, in the detection head, the model will perform final processing to obtain the coordinates, width, height, and category of the object, thereby achieving the objective of object detection.