esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 3 Method
### Run-Time Dynamic Programming for Arbitrary-Sized Image  
With the specific instruction set, the D-SWIM buffer can be rapidly re-programmed for processing arbitrary sized images. Figure 8 demonstrates the system workflow on both the FPGA and the server. The server obtains the images from users and prepares the D-SWIM instruction list for the image size. Due to the low complexity of the instruction generator in Algorithm 1, the server generates the instruction list online and then writes it to the Instruction Memory of D-SWIM at the FPGA side. Besides the instruction, the server also sends the value of image height (\(N_{height}\)) to the control register in D-SWIM that determines the life-cycle of the instruction list. Subsequently, the server sends the corresponding image to the FPGA and obtains the computational results in a continuous data stream. Note that the communication latency is hidden in the fully pipelined workflow. Thus, the image computation logic on FPGA only stalls for a brief period of time during instruction loading.
## 2 Materials and Methods
### Analysis and Utilization of Data  
#### 2.4.1 Preprocessing of Spectral Data  
To effectively utilize the spectrum information for modeling and remove the impact of noise and irrelevant data, it is essential to preprocess the acquired hyperspectral data. This study extensively utilizes the findings of earlier scholars [21; 22] to determine the most effective preprocessing technique for sequentially handling the spectrum data. The chosen method involves applying Savitzky-Golay (SG) smoothing followed by the first-order derivative. The SG smoothing is a type of filter that uses the polynomial convolution method. It works by fitting the weighting coefficients of high-order polynomials using the least-squares method within a moving window. This allows for smoothing filtering, which enhances the signal-to-noise ratio and removes noise. The method of smoothing filtering [23], enhances the signal-to-noise ratio, eliminates noisy data, and preserves spectrally valid information, resulting in an overall improvement in the spectrum's signal-to-noise ratio. By utilizing the first-order derivative spectra, it is possible to effectively eliminate the inaccuracy caused by spectral line drift and reduce the impact of the soil background. This, in turn, improves the correlation between the spectral variables and the target variables. The preprocessing procedure was executed using The Unscrambler X 10.4 program.  
The SG smoothing filter is defined as  
\[Y_{j}^{*}=\frac{\sum_{i=-m}^{i=m}C_{i}Y_{j+1}}{N} \tag{3}\]  
where \(Y_{j}^{*}\) is the smoothed hyperspectral data, \(Y_{j+1}\) is the pre-smoothed hyperspectral data, \(C_{i}\) is the filter coefficient, and \(N\) is the number of sliding window data of \(2m+1\).  
The first-order derivative of the spectral reflectance is  
\[R^{\prime}=\frac{R_{i+1}-R_{i-1}}{\lambda_{i+1}-\lambda_{i-1}} =\frac{R_{i+1}-R_{i-1}}{2\Delta\lambda} \tag{4}\]  
where \(R^{\prime}\) is the first-order derivative spectrum, \(R_{i}\) is the corresponding spectral reflectance at band \(i\), \(\lambda_{i}\) is the wavelength of the \(i\)th band, and \(\Delta\lambda\) is the interval between neighboring wavelengths.
###### Abstract
Furthermore, the wavelength is set to \(\lambda\) = 300 \(\upmu\)m in order to reduce the sampling requirements to 256 \(\times\) 512 pixels when using spherical wave spectrum method [18]. Compared with the sampling points of more than \(4\times 10^{10}\) for conventional SCGH in visible range, that of the proposed generation method is reduced to an acceptable level of \(10^{6}\). Moreover, the spatial light modulator (SLM) with curved surface is difficult to realize so far, and reconstructing a spherical hologram by laser after printing it on a flexible film is still a challenging task today [18]. Given the staggering sampling points for the visible range of wavelengths required in the concentric-sphere model and the constraints of the optical materials and the production technology currently, it is difficult for practical application due to the long time consumption and unavailable holographic reconstruction equipment. In this paper, a fast diffraction calculation method between plane and sphere for SCGH based on phase compensation (PC) is proposed. Our PC method can be summarized in two steps: First, a wavefront recording plane (WRP) is used to record the optical field propagated from the object plane. As the conventional plane-to-plane diffraction calculation method is adopted in this step, the number of sampling points of WRP is coincident with that of the adopted plane diffraction model. In the second step, the phase difference introduced by the optical path difference between WRP and SCGH is approximately compensated point-to-point in order to generate the SCGH, which means the number of sampling points is constant with that of WRP. Compared with the original point source (PS) method, few sampling points, good scalability, and significant acceleration performance are the absolute advantages of our PC method. Therefore, the problem of time-consuming calculation of the propagation model between plane and sphere is solved firstly to our best knowledge, which is different from accelerating at the expense of limiting the wavelength in the invisible micron band in the two concentric spherical propagation model. The feasibility of our method is verified by numerical simulations and optical experiments, and the error is analyzed by theory and simulations in this paper. The reconstruction quality is good regardless of the increase in the curvature of SCGH or the resolution of the object plane, even though it is an approximate method. Moreover, in this paper, an alternative approach using PC method to implement optical experiments of SCGH is provided by compensating the phase difference between SCGH and planar SLM before loading the hologram on the SLM for reconstruction, which makes it even closer to realize the practical applications of spherical holography. This paper consists of four sections. In the Section 2, we introduce the PC method and analyze its error. The simulations and experiments are described in Section 3. Finally, we draw conclusions and discuss prospects in Section 4.
## 3 Results
### Interpretation of Statistical Tests  
To precisely assess the level of agreement or discrepancy among the data sets obtained from simulations for the best effective combinations of drugs, we utilized Statistical Package for the Social Sciences Release 13.0 (SPSS Release 13.0) Package for a detailed analysis. Initially, we conducted normality tests across all data sets, which revealed that none of the data sets followed a normal distribution. Consequently, we applied nonparametric statistical tests to make pairwise comparisons between the data sets derived from the different models.  
First, we conducted the Friedman test. The probability of obtaining the observed results by chance referred to as \(p\)-value, less than 0.05, is typically considered statistically significant, indicating that the hypothesis that there are no differences between the groups is rejected. For the case study of SMN synthesis, this value was \(p<0.0001\). The direct implication of this observation is that for the seven best effective combinations of drugs and their combinations shown in Figure 11 there is a significant difference between the medians of the simulation results from three paradigms.  
Then, we performed the Dunn-Bonferroni test to compare a pair of related data sets on a single sample to assess whether the data sets have the same distribution. The pairwise comparison of data sets yielded \(p<0.001\). Hence, we concluded that there is a substantial  
Figure 11: Test statistics collected from Friedman and Dun–Bonferroni tests for the best effective combinations of drugs.  
difference between the distribution of related values in deterministic, stochastic, and fuzzy stochastic simulations. Moreover, statistical analysis reveals that values in the stochastic cases are significantly higher than corresponding values in the deterministic cases and that values in the fuzzy stochastic models are substantially higher compared to related values in the stochastic models. The results of the comparison of the simulation results in line with three paradigms are summarized in Figure 11.
## 3 SPSA Learning-Based Three-Dimensional Fuzzy Modeling
### SPSA Learning for Resulting Components of Three-Dimensional Fuzzy Rule  
#### 3.3.1 Fourier Space Base Function  
The space base function is crucial to the performance of the modeling [7]. In three-dimensional fuzzy modeling, the KL decomposition technique and SVR are used to estimate space base functions. Traditional DPS modeling approaches allow for the selection of fixed form functions, including spline functions, Legendre polynomials, and Jacobi polynomials [7], as space base functions, in addition to KL decomposition estimation. In this study, Fourier space base functions are taken as the resulting components of three-dimensional fuzzy rules. The spatial dynamics of a DPS can be represented by multiple sine waves and cosine waves with different amplitude and frequency. A Fourier space base function is expressed as in (9):  
\[\begin{split}\varphi_{l}(\bar{F})=a_{i}\sin(b_{l}\bar{F})+c_{l} \cos(b_{l}\bar{F})+d_{l},\\ (l=1,2,\ldots,Z^{\prime}).\end{split} \tag{9}\]  
Therefore, the variable \(w(\bar{F},k)\) can be expressed as a linear sum of Fourier space base functions, with each space base function multiplied by its corresponding time coefficient, which is shown as given in (10):  
\[w(\bar{F},k)=\sum_{l=1}^{Z^{\prime}}\psi^{l}(k)\varphi_{l}(\bar{F}). \tag{10}\]\[\psi^{l}(k)=\sum_{h=1}^{P}a_{h}^{l}\prod_{s=1}^{I}E_{A}\times\prod_{i=1}^{L}\prod_{ s=1}^{K}E_{B}, \tag{11}\] \[E_{A}=\exp\left(-\left(\left(w(f_{h},k-s)-c_{sh}^{l}\right)/\sigma _{sh}^{l}\right)^{2}\right),\] \[E_{B}=\exp\left(-\left(\left(q_{i}(k-g)-d_{i}^{gl}\right)/\delta _{i}^{gl}\right)^{2}\right).\]  
where \(\psi^{l}(k)\) is called a fuzzy basis function [22] in the context of the three-dimensional fuzzy system, also called the time coefficient in a three-dimensional fuzzy model; \(c_{sh}^{l}\) and \(\sigma_{sh}^{l}\) are denoted as the central point and breadth of the Gaussian-type three-dimensional fuzzy set \(\mathcal{O}_{sl}\) at the \(h\)th sensor position; \(d_{i}^{gl}\) and \(\delta_{i}^{gl}\) represent the central point and breadth of the conventional Gaussian fuzzy set \(Q_{i}^{gl}\); and \(a_{h}^{l}\) represents the weight assigned to the \(h\)th sensor location.  
The SPSA method will be used to learn the coefficients of the Fourier space base function.
## 8 A Unified Framework for Cognitive Radio and Cognitive Radar
### Bayesian Network  
Bayesian Network (BN) is a graphic model which explicitly uncovers the probabilistic structure of dependency in a set of random variables. It uses a direct acyclic graph (DAG) to represent the dependency structure, in which each node denotes a random variable and each edge denotes the relation of dependency. The key difference between BN and HMM is that the former represents the hidden states using a set of random variables instead of a single random variable [137, 138, 139]. BN is a static model. While Dynamic Bayesian Network (DBN) is a powerful tool to model the sequential data or the dynamic system. DBN can be employed in the context of cognitive radio or cognitive radar to model the spectrum. For modeling the spectrum, the main task is learning, which means statistic information is extracted from the measured training data and DBN is built. There are two stages of learning. One is structure learning, i.e., topology selection. The other is parameter learning, i.e., parameter estimation. In the stage of structure learning, we need to determine the topology of DBN, i.e., the structure of dependency. In the stage of parameter learning, the conditional probability distribution (CPD) of each node should be estimated. After DBN is built, we can use it to do filtering, prediction, classification and so on, all of which can be called inference. In our work, DBN is exploited to predict the state of the specific spectrum. The results of prediction will be conveyed to decision maker and decision maker will make the control decision for the behavior of cognitive radio or cognitive radar.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 5 Simulations and Results
### Result  
#### 5.2.1 Environment 1  
Comparing different values of the threshold parameter \"\(z\)\" to obtain the optimal value. In this simulation, the network is composed of nodes with numbers 25, 75,..., 275, and the nodes' movement speeds vary randomly between 0 and 5 m/s at different time intervals. The threshold values for \"\(z\)\" are set to 1.282, 1.440, 1.645, 1.960, and 2.576, where higher values indicate fewer nodes with significant deviations. The performance of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Platform & NS3.35 \\ Simulation Time & 110 s \\ Simulation Protocol & AODV, ND-AODV, and CND-AODV \\ Number of Nodes & 25, 75, 125, 175, 200, 225, 275 \\ Active Nodes & 10 \\ MAC Protocol & IEEE 802.11p \\ Simulation Area & \(1000\times 1000\) m\({}^{2}\) \\ Propagation Loss Model & TwoRayGroundPropagationLossModel \\ Speed of nodes & 3, 5, 25, and 50 m/s \\ Packet Size & 512b \\ Mobility Model & RandomWaypointMobilityModel \\ Transport Layer Protocol & UDP \\ Transmission Model & CBR (constant bit rate) \\ Transmission Rate & 2 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters of NS3.  
CND-AODV protocol in the mobile ad hoc network is evaluated for different \"z\" values, as shown in Figures 13-15. From Figures 13-15, it can be observed that regardless of the \"z\" value, both the throughput and packet delivery ratio exhibit a decreasing trend, and the delay demonstrates an increasing trend. However, when the threshold value \"z\" is set to 1.645, corresponding to the 90% confidence interval, there is a slight improvement in both throughput and packet delivery ratio compared to other values. Additionally, the increase in delay is reduced, resulting in a minor improvement as well.
# Efficient Online Log Parsing with Log Punctuations Signature
## 4 Evaluation
### LogHub Dataset and Accuracy Metrics  
Our benchmark datasets come from the LogHub data repository [28]. LogHub maintains a collection of logs from 16 different systems spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and stand-alone software. Many prior log parsing research [15; 16; 17; 18; 26] evaluated their approaches on these logs.  
As illustrated in Table 1, LogHub contains 440 million log messages which amount to 77 GB. We can see that there are great differences in the number of templates in different datasets. The Android dataset has a maximum of 76,923 templates, while the Proxifier has a minimum of 9 templates. The average length of log messages is basically in the tens or twenties, but the maximum log message length of some datasets can reach hundreds. In terms of the log content, there is little difference among datasets, which is mostly readable free text.  
Benefiting from the large size and diversity of LogHub datasets, we can measure the accuracy of log parsers and test their robustness and efficiency. LogHub picked up a subset of 2000 log messages from each dataset and manually labeled the event templates as the ground truth to ensure a consistent benchmark environment. In Table 1, \"#Templates(2k)\" indicates the number of event templates in the 2000 log subsets. Such manually labeled data are used to evaluate the accuracy and robustness of our log parser.  
Same as the prior work by [PERSON] et al. [15], we adopt the _parsing accuracy_ (PA) metric to measure the effectiveness of our log parser. PA is defined as the ratio of correctly parsed log messages over the total number of log messages. After parsing, each log message will be assigned with an event id suggesting which event type it belongs to. Regarding an event type, we consider it as correct if and only if all its log messages in the ground truth are parsed with the same event id. PA is stricter than the standard evaluation metrics, such as precision, recall, and F1-measure.
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 1 Introduction
In this way, the patient can observe on the screen a virtual skeleton on their body, where the areas to be treated are highlighted. Additionally, virtual objects are superimposed on the video (objects to hit, decoration, instructions, user interface, etc. ), which allows a gamified scene to be created. ExerCam does not need any special controller or sensor for its operation, as it works with a simple, webcam-type RGB camera, which makes the application totally accessible and low cost. Therefore, the aportation of our work is the presentation of ExerCam as a low-cost system, including the use of 2D vision, which could be an alternative tool to be taken into account in telerehabilitation therapies. A study of healthy subjects has been conducted in order to assess the technical viability and usability of the developed application. The paper is organized as follows: Section 2 explains the ExerCam application developed in depth. In Section 3, the results obtained are presented and discussed, highlighting the validity of the use of ExerCam as a tool for measuring the range of motion (ROM), necessary to be able to perform an evaluation of the evolution of the patients. Finally, Section 4 presents our conclusions.
## 5 Discussion and Conclusions  
### Discussion  
In this study, we introduce an innovative system for automatic detection of puncture timing based on clinical injection training. This system exploits image processing and YOLOv7 techniques to precisely detect the puncture time of trainers throughout the procedure. The proposed approach was assessed on a dataset comprising 109 experimenters, and it exhibited an average detection error of merely 0.330 s. It is worth noting that this value is considerably lower than the average total operation time of 11.00 s for each participant.  
The system employed image processing techniques, such as region of interest (ROI) extraction and image rotation, to facilitate the extraction of pertinent information from frames. Furthermore, the YOLOv7 algorithm was employed to detect the injection needle, and the appropriate threshold for puncture time was determined through post-processing and experiments to achieve the final automatic detection of the puncture timing.  
Our results illustrate the effectiveness and robustness of the proposed system, even in challenging scenarios where the operator is left-handed or holds the syringe incorrectly. These findings imply that the system is potentially applicable in a clinical environment, where it can facilitate the provision of objective and precise feedback during medical training for novices, given the current social limitations imposed by COVID-19.  
However, our study is not without limitations that could be addressed in future research. One limitation resides in the YOLOv7 model's incapacity to discern the state of the punctured vessel. Nevertheless, we hold the belief that ongoing research endeavors and advancements in computer vision technology possess the capacity to redress this limitation. This technological breakthrough could potentially be achieved by formulating more sophisticated algorithms or integrating supplementary modalities, such as ultrasound guidance.  
Another aspect worthy of consideration is the exploration of additional variables, such as patient pain perception, which would yield significant value in future clinical trials. Not only would this yield a more comprehensive assessment to further comprehend patients' sensations and responses throughout the injection process, but it would also contribute to the enhancement of comfort and patient satisfaction during the injection procedure. Furthermore, it would offer guidance for refining injection techniques and training, thereby augmenting its practical applicability in real-world scenarios.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 2 Related Work
### Comparison with Related Work  
The purpose of this paper is not to construct clusters but to make cache management more efficient by using the clusters. Each CH manages content in its cluster unitarily, which solves the problem of cache duplication that was a challenge in previous works [3; 7]. In this way, vehicles can cache more content in a cluster, which enables CHs to reply to diverse requests locally instead of further fetching them from other vehicles or content providers.
## 5 Experiment
### Results  
#### Overall Evaluation  
The experimental results of applying the DRKG model across four benchmark datasets demonstrate its substantial performance advantages over existing methods. As shown in Table 5, the DRKG achieves perfect scores of 100% on both the hit@1 and F1 metrics for the MetaQA dataset, while achieving 96. 6% on hit@1 and 89. 3% on the PathQuestion dataset. Since each question in PathQuestion has only one standard answer, the hit@1 and F1 scores of the evaluation results of this dataset are the same. For more complex scenarios, scores of 88.16% on hit@1 and 84.05 F1 on WebQSP are obtained, along with 66.99% on hit@1 and 58.04 F1 on CWQ. This performance disparity primarily stems from the characteristics of the dataset: MetaQA and PathQuestion benefit from the comprehensive coverage of the knowledge graph, enabling the DRKG to effectively comprehend the logic of the questionsand precisely retrieve the answers through the knowledge graph interfaces. Performance degradation on WebQSP and CWQ correlates with increasing linguistic complexity and knowledge graph incompleteness, and although the DRKG maintains reasoning capabilities, missing nodes or relations in these datasets frequently disrupt reasoning chains.