esa-sceva/satcom-chunk-collection huggingface dataset:
# Video over DSL with LDGM Codes for Interactive Applications
## 3 Methodology
### Extending LDGM for \"Bursty\" Channels
The heuristic behind that choice is that the range of columns starting from \(h\) already has the most resilience against packet-error bursts. After each adjustment to \(H\), \(GRE(H)\) in equation (10) is re-calculated until \(GRE(H)\) ceases to increase, at which point the algorithm halts. Other heuristic stopping criteria could be applied, as the behavior of \(GRE(H)\) is not established, though see Section 4.3.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 3 Methodology
### Scopus Analysis  
During the data collection process, we recognized that the existing literature encompassed a wide array of input modalities beyond simple RGB, such as RGB-D, 3D models, LiDAR, depth maps, and other sensor data. Although our original goal centered on RGB models, the wealth of information encountered led us to broaden our investigation to include these additional input types. This strategic expansion allows our review to present a more holistic overview of the field, ensuring that readers have access to insightsthat cover a broad spectrum of approaches, thus stepping slightly beyond the original RGB-focused scope.  
To collect relevant literature, we performed comprehensive keyword-based searches in the Scopus database. We began with the 25 RGB models identified in the baseline survey and then searched using their model names in the title, abstract, and keywords of articles. This search yielded 155 articles. The keywords used were: the name of each 25 RGB model, \"Pose Estimation\", \"6D pose\" and \"object\". After examining the content, 124 articles appeared closely related to 6D pose estimation in computer vision contexts. Further scrutiny led to the removal of studies that did not introduce or evaluate a 6D pose estimation model, and researches published more that once ultimately leaving a corpus of 84 articles--models, Figure 1.  
To facilitate a meaningful analysis, we established a structured set of questions for each model:  
1. Model name and publication year.
2. Input type(s) required by the model.
3. Single-image or multi-image input capability.
4. Real-time applicability, including reported speed (in FPS) and the GPU hardware needed.
5. Datasets used for model evaluation.
6. Metrics employed to measure model performance, focusing on Average Distance of Model Points (ADD), Average Distance of Model Points-Symmetric Object (ADD-S), Average Distance of Model Points for Symmetric and Non-Symmetric Objects (ADD(-S)), average recall (AR), Area Under Curve for (ADD AUC ADD), and Area Under Curve for ADD(-S) (AUC ADD(-S)).
7. Performance values reported for the above metrics.
8. Specific items or objects on which the model was tested.
9. Strengths and limitations of the model.  
Figure 1: Research Workflow. Base article [3].  
10. The ability of the model to handle symmetrical objects, multiple objects, multiple identical objects, transparent objects, objects of different shapes, and occlusions.  
We systematically recorded the available information for each model, creating a standardized template that allows for clear, comparable summaries of model capabilities. The final aim was to distill our findings into a set of comprehensive tables, thereby offering a high-level reference that researchers and developers can consult when selecting 6D pose estimation models that best align with their particular requirements--ranging from computational constraints and input modality to robustness against environmental challenges and object characteristics.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 4 Case Analysis
### Case Verification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{\begin{tabular}{c} **Actual Value** \\ **(10\({}^{4}\) m\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Predicted** \\ **Value (10\({}^{4}\) m\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Solar** \\ **Radiation** \\ **(J/cm\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Difference** \\ **Value (10\({}^{4}\) m\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Relative** \\ **Error (\%)** \\ \end{tabular} } \\ \hline
5 & First year-11-21 & 310 & 300 & 11.9 & 1664 & 10 & 0.03 \\
6 & First year-11-22 & 245 & 235 & 3.8 & 869 & 10 & 0.04 \\
7 & First year-11-23 & 425 & 437 & \(-\)0.6 & 308 & 12 & \(-\)0.03 \\
8 & First year-11-24 & 443 & 440 & 1 & 309 & 3 & 0.01 \\
9 & First year-11-25 & 374 & 379 & 5 & 581 & \(-\)5 & \(-\)0.01 \\
58 & Second year-02-24 & 212 & 220 & 7.2 & 664 & \(-\)8 & \(-\)0.04 \\
59 & Second year -02-27 & 65 & 71 & 10.8 & 1356 & \(-\)6 & \(-\)0.09 \\
60 & Second year -02-28 & 64 & 69 & 11.1 & 1414 & \(-\)5 & \(-\)0.08 \\
61 & Second year -03-01 & 74 & 78 & 12.2 & 1672 & \(-\)4 & \(-\)0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error table of partial predicted and actual values.
# Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters
## 1 Introduction
Various digital image processing techniques are also used for enhancing and detecting line-like structures in 2D images [12, 13, 14]. Most of these methods are based on fixed scale techniques. These techniques are not suitable for MUAP enhancement and detection in sEMG images due to the non-uniform width of the MUAP propagation pattern. In a recent study [4], the authors have analyzed several line-enhancing filters for enhancing and detecting the MUAP propagation pattern in sEMG spatio-temporal images. Some authors have used the Gabor filter [15,16], Hermite Shape filter [17], and steerable filters [18,19] to enhance the sEMG images. From the results of these studies, it is evident that the Hermite shape filter is the best candidate to represent and enhance the MUAP propagation pattern. However, it was found that the Hermite shape filter and other filters analyzed in Reference [4] are unable to retain the polarities of the EMG signals. In some recent studies [20, 21, 22], multi-scale methods based on Hessian are proposed for enhancing line-like structure in digital images. However, none of these methods are utilized for the MUAP enhancement in the EMG images. In Reference [5], the authors have proposed that sEMG signals can be enhanced using the Hessian-based filter. In this preliminary work, only the bright portion of the MUAP is enhanced. Moreover, the analytical justification of the Hessian filter and its performance analysis on synthetic, simulated, and experimental sEMG was not studied. This proposed study is an extension of the work in Reference [5]. It presents a similar framework based on the Hessian matrix that enhances a Gaussian line-like structure (MUAP propagation structure) in sEMG spatio-temporal images with analytical justification. A novel approach is presented based on the multi-scale Hessian matrix for detecting and enhancing the MUAP propagation structure in the spatio-temporal sEMG images. The proposed work is an extension of the initial work done by the authors in Reference [20] and then by the authors of References [22,23] for enhancement of linear and tubular structure in 2D and three-dimensional (3D) images. The same approach is also successfully used in Reference [21] to enhance and segment vessels in retinal digital images. The proposed method is compared with different linear structure enhancement methods using synthetic, simulated, and experimental sEMG signals. The proposed method has an average accuracy of 98% in enhancing and detecting the MUAP propagation region in sEMG spatio-temporal images. This manuscript is organized into 4 sections. Section 2 describes the analytical justification of using the Hessian matrix for MUAP enhancement. It provides a detailed mathematical background for the design of the proposed multi-scale filter. Section 3 analyzes the results of the proposed filter for the enhancement of MUAP in the synthetic, simulated, and experimental signals in detail. Section 4 provides the conclusion of the proposed work.
## 4 Experimental Results
### Comparision with Other Techniques  
This section compares the performance of the proposed method with the models known from the literature. Most of the previous models were evaluated on Mini-MIAS and DDSM datasets, whereas two CAD models have been evaluated on INbreast and BCDR datasets. The average performance values obtained for the literature and proposed models are summarized in Tables 8-10. The proposed model yields better results for performance evaluation in terms of accuracy, sensitivity, specificity, and F-measure when compared to traditional FD and LBP features. Table 8 presents a comparative study between our proposed M-FD + GA + ANN and several studies in the literature for the Mini-MIAS dataset. Our proposed model yields a 100% performance result in terms of all used matrices in the context of classification using 316 images out of 322 from the Mini-MIAS database. The previous study [47] obtained 100% sensitivity, whereas the work of [57] used hybrid LB-GLCM+LPQ texture features, and fewer images were used for the evaluation in both works. Moreover, we also found that the study of [57] gives 100% accuracy where this work used LWT+PCA (32 features), and 119 mammogram images from Mini-MIAS were evaluated. However, our proposed model exceeds all presented results in Table 8.  
Table 9 presents a comparative study between our proposed model and previous works for the DDSM dataset. We discovered that the proposed model yields better results compared to the traditional FD and LBP features. The previous study [46] obtained 100% sensitivity, and the previous study [58] gives 100% accuracy in the Mini-MIAS dataset. Table 9 depicts that our proposed model yields better sensitivity and accuracy compared to previous studies [57; 58] in the DDSM dataset. In the same trend, the previous study [63] gives 100% sensitivity and our proposed model gives 99.79% in the DDSM dataset due to using a small set of mammogram images. DDSM holds a high number of images, while the previous study of [63] used only 250 images in the evaluation. Figure 11: ROC for the training, validation, and testing of the proposed model using new M-FD features.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.2 Multiple Receiver  
Multiple Receiver, the so called forwarder set, is the most important issue in OR because the number of receivers directly corresponds to the number of senders in the next hop transmission of OR, which determines the degree of data redundancy. Several papers have been trying to optimally determine a forwarder set [11; 12; 13; 14; 15; 16; 17]. We can do research to make use of ADC in determining the forwarder set, because setting the nodes to different wakeup times may result in a different receiver set, or forwarder set. Investigating the relation between controlling the wakeup times of ADC and finding the optimal forwarder set is going to be one of the interesting research topics related to OR.
## 1 Introduction
This study developed a unilateral KAEGO for hemiplegic patient gait training and proposes a simple gait control strategy that employs an IMU sensor and knee angle sensor to detect gait phases and recognize the patient's gait intentions, eliminating the need for GRF sensors. Additionally, we applied a simple torque generation method based on knee angular velocity to assist the knee joint movements [13]. This paper aims to evaluate the effectiveness of this approach by assessing the accuracy of gait recognition and comfortable gait assistance through two types of walk tests. The first experiment involved a hemiplegic patient and a healthy individual, each performing 10 m walk tests [29], during which data were collected to analyze the accuracy of gait recognition and how quickly the gait intentions were identified. In the second experiment, we hypothesized that if the developed KAEGO provided appropriate assistive torque, it would lead to a decrease in metabolic cost during walking, despite the potential discomfort from the weight (3.4 kg) increase and wearability of the device. To test this hypothesis, we adopted a method of measuring the metabolic cost of transport (COT) during a six-minute walk test [24; 30; 31]. The 10 m walking test (WT) results showed that the proposed control strategy accurately recognized the patient's gait intentions before the swing phase transition and generated customized assistance torque accordingly. In the 6 min WTs, it was observed that even though gait speed and travel distance increased, the metabolic cost (VO\({}_{2}\), heart rate) remained similar or decreased. This result is consistent with the user survey result indicating that the participant felt more comfort and stability when walking with the developed KAEGO compared to walking without it.
# Performance Evaluation of a Novel Propulsion System for the Spherical Underwater Robot (SURIII)
###### Abstract  
This paper considers a novel propulsion system for the third-generation Spherical Underwater Robot (SURIII), the improved propulsion system is designed and analyzed to verify its increased stability compared to the second-generation Spherical Underwater Robot (SURII). With the new propulsion system, the robot is not only symmetric on the \(X\) axis but also on the \(Y\) axis, which increases the flexibility of its movement. The new arrangement also reduces the space constraints of servomotors and vectored water-jet thrusters. This paper also aims to the hydrodynamic characteristic of the whole robot. According to the different situations of the surge and heave motion, two kinds of methods are used to calculate the drag coefficient for the SURIII. For surge motion, the drag coefficient can be determined by the Reynolds number. For heave motion, considering about the influences of edges and gaps of the SURIII, the drag coefficient needs to be calculated by the dynamic equation. In addition, the Computational Fluid Dynamics (CFD) simulation is carried out to estimate some parameters which cannot be measured. The pressure contours, velocity vectors and velocity streamlines for different motions are extracted from the post-processor in the CFD simulation. The drag coefficients of surge and heave motion are both calculated by the simulation results and compared with the chosen one by Reynolds number. Finally, an experiment is also conducted for measure the propulsive force of the multi-vectored water-jet thrusters by using a 6-DoF load cell. The experimental results demonstrate the propulsive force is better than a previous version. Thus, the propulsive performance is better than before.  
spherical underwater robot; hydrodynamic analysis; Computational Fluid Dynamics simulation; propulsion system; vectored water-jet thrusters +
Footnote †: journal: Applied Mechanics and Applications
###### Abstract  
This work is a comprehensive experimental and theoretical study aimed at understanding the photothermal and molecular shape-change contributions to the photomechanical effect of polymers doped with azo dyes. Our prototypical system is the azobenzene dye Disperse Red 1 (DR1) doped into poly (methyl methacrylate) (PMMA) polymer formed into optical fibers. We start by determining the thermo-mechanical properties of the materials with a temperature-dependent stress measurement. The material parameters, so determined, are used in a photothermal heating model--with no adjustable parameters--to predict its contribution. The photothermal heating model predicts the observations, ruling out mechanisms originating in light-induced shape changes of the dopant molecules. The photomechanical tensor response along the two principle axes in the uniaxial approximation is measured and compared with another independent theory of photothermal heating and angular hole burning/reorientation. Again, the results are consistent only with a purely thermal response, showing that effects due to light-induced shape changes of the azo dyes are negligible. The measurements are repeated as a function of polymer chain length and the photomechanical efficiencies determined. We find the results to be mostly chain-length independent.  
photomechanics; dye-doped polymers; polymer fibers; photo-isomerization; photothermal heating; molecular reorientation; angular hole burning; azobenzene; stress; strain +
Footnote †: journal:  
_Article_  
Photothermal and Reorientational Contributions to the Photomechanical Response of DR1 Azo Dye-Doped PMMA Fibers
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 1 Introduction
However, the use of maximum propagation delay, regardless of the internodal distances, causes some degree of inefficiency in terms of resource utilization. A similar method was also used in the dynamic NAV (DYNAV) determination protocol [24]. Unlike UW-CSMA/CA, DYNAV uses the maximum propagation delay only once for the NAV associated with the request-to-send (RTS) packet, which is the first packet in the handshaking process. For the remaining handshaking, an estimated value of the propagation delay is used to determine the NAV to avoid any unnecessarily long NAVs. However, since DYNAV considers only the propagation delay between a source and a destination, and not between a source and the neighbors or between a destination and the neighbors, all neighbors have the same NAV, regardless of their distances from the source or destination. This means that a neighbor who is far from the source or destination would wake up much later than a closer neighbor, as the distant neighbor overhears the control packets and sets the NAV at a delayed time. Accordingly, the likelihood of a distant neighbor occupying the channel is very small which causes severe unfairness problems in the network. In this paper, we propose a scheme to determine the NAV by taking into account the propagation delay of the underwater acoustic channel. However, unlike prior works, we take into account all possible propagation delays--propagation delay between a source and a destination; propagation delay between a source and the neighbors; and propagation delay between a destination and the neighbors. Such approach effectively determines an optimized NAV and eliminates any unnecessarily long silent periods. The NAV is constantly updated every time a new estimate of the propagation delay is obtained through the transmission time, which is time-stamped on to the overhearing handshaking packets. We show, through numerical experiments, that setting appropriate NAV or silent periods results in better throughput and fairness performance. The rest of this paper is organized as follows. Section 2 discusses the problem statement. In Section 3, the proposed scheme is presented. The simulation results and discussions of the proposed scheme are presented in Section 4. Finally, in Section 5, we give the conclusions.
## 1 Introduction: Name Disambiguation and Collocation
### Algorithmic vs. manual name disambiguation and collocation
#### 1.7.1 Manual name disambiguation and collocation  
[PERSON] and [PERSON] argue against manual name disambiguation. They conclude, \"Nevertheless, manual disambiguation is a surprisingly hard and uncertain process, even on a small scale, and is entirely infeasible for common names.\" [5]. Indeed the vast size of the internet makes manual name disambiguation and collocation there virtually impossible. For example, there may be two documents with the same author listed, but there is not enough information to determine with any certainty that the two articles were written by the same person or by two persons who happen to share the same name.  
On the other hand, humans can solve some problems and make judgments better than computers can. For example, when one is unsure whether two occurrences of a name represent the same person or two different people, a quick web search, including perhaps an examination of photographs, can often resolve the question. Humans are better than computers at grouping and analyzing variant forms of a single name. The emergence of manually-created, shared name authority files (such as the Virtual International Authority File, to be described below) shows that manual name disambiguation is necessary and sustainable. If algorithmic name disambiguation and collocation were successful, there would be no need for manual approaches to solving these problems. Initiatives that involve manual approaches, such as the application of unique identifiers and shared databases of name metadata, are beginning to proliferate, an indication of the need for and value of manual name disambiguation.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 3 Multi-Variable State Estimate Technique (MSET)
A nonlinear operator \(\otimes\) is introduced, and the Equation (9) could be written as,  
\[W=\left(D^{T}\otimes D\right)^{-1}\cdot\left(D^{T}\otimes X_{obs}\right) \tag{10}\]  
Concise nonlinear operator that calculates the Euclidean distance arithmetic is chosen in our paper, that is,  
\[\otimes\left(X,Y\right)=\sqrt{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}} \tag{11}\]  
The operator we employed is easy to understand physically, and the similarity of two vectors is represented by spatial distance. If the observation vector is similar with the vectors in memory matrix \(D\), the corresponding spatial distances would be small, and while there is a distinguishable different between observation vector and memory matrix, the distance would become large. Finally, the estimation vector could be written as,  
\[X_{est}=D\cdot\left(D^{T}\otimes D\right)^{-1}\cdot\left(D^{T}\otimes X_{obs}\right) \tag{12}\]  
The memory matrix \(D\) established a normal nonlinear normal state envelope space, while the input observation vector \(X_{obs}\) is located in the normal space and similar with a vector in \(D\), the corresponding vector \(X_{est}\) obtained by Equation (12) is rather accurate. However, if an incipient failure occurs, the characteristic of \(X_{obs}\) would change, and the observation vector \(X_{obs}\) would exceed the boundary of envelope space, as a result, \(X_{obs}\) could not be represented by using the vectors included in memory matrix \(D\), and the current observation vector \(X_{obs}\) and the estimate vector \(X_{est}\) would be very different. In conclusion, the incipient failure could be detected by analyzing similarity between \(X_{obs}\) and \(X_{est}\).