esa-sceva/satcom-chunk-collection huggingface dataset:
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.3 Comparative Experiments  
To further validate the effectiveness of our approach, we specifically compare our methods with several advanced methods for attribute. The test results are as seen in Table 9.  
CRF [23]: The CRF model was proposed by [PERSON]. It incorporates the word, POS, n-gram words, suffix and prefix, and location features.  
LSTM-CRF [16]: In article, the LSTM-CRF model is like that proposed by [PERSON] et al. [16].  
Char-LSTM-CRF [18]: The character embedding of the words is given to a bidirectional LSTM. Finally, it concatenates outputs to an embedding from a lookup table to obtain a representation for this word.  
LSTM-CNNs-CRF [24]: The CNN training obtains the character embedding, then connects the character embedding and the word embedding, and inputs it into the LSTM. Finally, it inputs the vector output from the LSTM into the CRF to jointly decode to obtain the optimal sequence label.  
As can be seen from Table 9, our method is significantly superior to the basic methods of CRF and LSTM-CRF. Compared to the Char-LSTM-CRF and LSTM-CNNs-CRF models, we consider syntactic features, which works well. In addition, we can also see that combining LSTM-CNNs-CRF with integrity algorithm can achieve good results. This is because the integrity algorithm can analyze semantics from a language perspective and improve the integrity of element extraction. This again illustrates the validity and migration of the integrity algorithm.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 2 Fundamentals of Compressed Sensing
### Nonlinear Recovery Algorithm
#### 2.3.2 L1-Norm Minimization Recovery  
An alternative is to use \(\ell_{1}\) norm and defined the \(\ell_{1}\) norm of a vector \(\lVert\mathbf{x}\rVert_{1}\) as \(\lVert\mathbf{x}\rVert_{1}=\sum_{n=1}^{N}\Bigl{\lvert}x(n)\Bigr{\rvert}\). Then the optimization problem solved can be rewritten as  
\[\begin{split}\hat{\mathbf{x}}&=\operatorname*{argmin}_{ \mathbf{x}}\lVert\mathbf{x}\rVert_{1}\\ \text{s.t.}&\mathbf{y}=\mathbf{\Phi}\mathbf{x}=\mathbf{\Phi}\mathbf{ \Psi}\mathbf{\alpha}\end{split} \tag{5}\]  
Due to the convexity of \(\ell_{1}\) the norm, Equation (5) can be regarded as the convex relaxation version of Equation (4), which can be solved via linear programming, with the computational complexity polynomial over the length of the signal.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 3 Results and Discussion
### Visual Assessment  
In this experiment, samples were periodically picked out from the device for visual assessment; detailed observations are given in Table 2. With the increase of graphene content, the color of the specimens turns black gradually. It can be noticed that before 20 F-T actions, all specimens with no change in surface. With the increase of freeze-thaw actions, the surface of concrete samples gradually changes. After 30 F-T cycles, tiny cracks and some porous were observed on the surface of control samples, and it was found that the edges of the control specimens began to loosen. However, no obvious change was observed on other GC samples. After 150 F-T cycles, control samples displayed cracking, no damage was observed on GC0.05, GC0.1, and GC0.2 samples, while some porous was found on GC0.02, GC0.3, and GC0.4 samples. After 200 F-T cycles, control samples showed severe cracking and sponge-like surfaces, and GC0.4 became more porous, showing small visible cracking;  
Figure 4: SEM images of the specimens after 200 freeze-thaw tests.  
Figure 5: The slump of concrete.  
however, GC0.05, GC0.1, and GC0.2 still have no obvious damage. From the surface appearance of the sample noticed in the experiment, we can obtain the damage progression. During repeated F-T tests, the water penetrates into the pores of concrete, and when the temperature is lower than a certain value, water will freeze, resulting in expansion stress, resulting in micro-cracks and macro-cracks; therefore, we could observe damages including pores, spalling, and cracks. The addition of GNP causes lower porosity [12] and forms a compact microstructure, as a result of the specimens with GNP might have less water absorption than control specimens, and showed less surface damage compared with the ordinary concrete samples through all the F-T actions.
# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
The idea behind using object detection is based on two observations. The first is that the fovea is anatomically close to the center of the retina. Thus, it can be a good reference point to center the photomontage. However, because it has fairly vague features, in the form of a slightly darker spot on the retina, it is not easy to use it as a reference in image alignment. Our second observation is about the optic disc, in that it has characteristic  
Figure 1: Overview of the proposed framework for constructing retinal fundus photomontages. Through deep learning based object detection, we are able to apply prior knowledge of the fovea and optic disc to determine the optimal order in which to integrate the images into the montage. Deep learning is also leveraged to reduce errors in registration. appearances. This makes feature point matching easier around the optic disc, and it can be exploited as a reference for more reliable image registration. Based on these observations, we categorize the images based on the existence of the fovea and the optic disc. Namely, (i) images containing both, (ii) images containing only the optic disc, (iii) images containing only the fovea, and (iv) images containing neither. Each category of images is integrated into the montage in this order, based on the positions of the fovea and/or optic disc within the image. For images with neither the fovea nor the optic disc, we rely on keypoint matching to determine the order. When constructing the montage, we not only apply an accurate registration method, but also include a criterion for rejecting possible mismatched images. If the rigid transform and non-rigid displacement vectors computed during registration turn out to be excessively large distortions, we consider that the registration process might have failed and exclude that image from the montage. Combining these components, our framework can be repeatedly applied to all the image frames to generate the final montage. By applying the detected positions of the fovea and optic disc, we effectively incorporate prior knowledge in the process, thereby resulting in a more robust montage construction with reduced failures. In addition, we can also improve the efficiency by avoiding the need to compare many image pairs in order to determine the best image to integrate next. We also improve the robustness of the image integration by maximizing the accuracy of image registration. We provide experimental evaluation on 62 retinal image sets to demonstrate the robustness, efficiency, and accuracy of the proposed framework.
## 2 Materials and Methods
### Model Performance Assessment  
The coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), and index of agreement (\(IA\)) were used to assess the performance of the different LUE models. The model performance is optimum for an \(RMSE\) close to 0 and for an \(R^{2}\) and \(IA\) close to 1.  
\[R^{2}=\left[\frac{\sum_{i=1}^{n}(o_{i}-\overline{o})(p_{i}-\overline{p})}{ \sqrt{\sum_{i=1}^{n}(o_{i}-\overline{o})^{2}}\sqrt{\sum_{i=1}^{n}(p_{i}- \overline{p})^{2}}}\right]^{2} \tag{13}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(o_{i}-p_{i})^{2}} \tag{14}\]  
\[IA=1-\frac{\sum_{i=1}^{n}(P_{i}-O_{i})^{2}}{\sum_{i=1}^{n}\bigl{(}\bigl{|} \bigl{(}P_{i}-\overline{O}\bigr{)}\bigr{|}+\bigl{|}(O_{i}-\overline{O})\bigr{|} \bigr{)}^{2}} \tag{15}\]  
where \(p_{i}\) and \(o_{i}\) represent a pair of estimated and measured values, and \(\overline{p}\) and \(\overline{o}\)  denote the mean estimated and measured values, respectively.  
Figure 3: The relationship between the fraction of incident photosynthetically active radiation absorbed by vegetation canopies (FPAR) and the leaf area index (LAI) (**a**) and the normalized difference vegetation index (NDVI) (**b**). The red line is the fitting curve.  
Figure 2: Photosynthesis light-response curve of cork oak plantation on sunny (**a**) and cloudy (**b**) days. The blue line is the slope curve of the utilization of the light energy. The red line is the fitting curve.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 2 Design Principles and Requirements for Upcoming MM Techniques  
Upcoming wireless networks will assist in accommodating various tenants (groups of users having common access and privileges) as well as industry verticals on the same network structure [9]. These revolutions, a few of which are being discussed by researchers [6], exemplify a model move from the existing network architecture design. As a result, MM mechanisms will need to be redesigned or re-assessed. We initially present the functional requirements of MM mechanisms for upcoming wireless networks in Table 1, formulated on the basis of features we derived from various network scenarios (current and forthcoming). From Table 1, we can perceive that the MM solutions for 5G and upcoming networks will have to adjust and change in order to cope with future wireless networks competently. Further, MM solutions will need to be reshaped, so that they may offer adaptability, dependability, extensibility, and reduced power consumption to guarantee unified mobility and required QoS. In one respect, from these requirements, specific criteria will affect the development and design of future MM solutions. Therefore, in the subsequent text, we provide an understanding of these various design benchmarks and their influence on 5G and upcoming MM mechanisms. We conclude from Table 1 that using only one MM solution will be inefficient to tackle all prevailing and future circumstances. Hence, these solutions face the challenges of attaining careful IP packet dispatching, RAT and BS selection, session control, and path optimization. Further, some other applications such as simulated reality, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), improved reality, etc., will require high bandwidth, dependability, and minimum delay requirements [22]. These requirements will also become a challenge for MM mechanisms. Additionally, RAN technologies are
## 2 Significance of Data Monetization Architecture  
In line with the above concerns of standards and previous data monetization solutions, we analyze here the necessity for building a new architecture, especially in the mobility context, as follows:  
* Vehicle OEMs are hit by the wave of Industry 4.0 with the advent of Digital Twin, where all the configuration and data of a vehicle are stored in the cloud. In close accordance is the launch of a Software-Defined Vehicle (SDV) technology. [15] allowing to \"centralize data with other components of ADAS, bodywork, chassis, and telematics in a Physical computer unit\". Also, all the customization and upgrades are made over the FOTA (Firmware Over The Air) technology, which is simplified by at most two High-Performance Computers (HPC) for SDV in a vehicle. All these Vehicle-to-Cloud communications, as well as the data that are shared, create the necessity of moving towards a decentralized data monetization where each participant in the ecosystem of OEM like Renault, Component Manufacturers like Qualcomm, Cloud Service providers like Google and the vehicle owner can each own a piece of the pie (service built on top of data and rewards) offered by the data, ensuring mutual benefit.
* Data which the vehicle owner engender have to create a virtuous cycle where each piece of data, starting from the sensor data, can be used by the equipment OEMs like Bosch, Continental. for improvisation, Vehicle OEM can enable the provisioning of data and a vehicle owner can obtain service benefits and up-gradation from the equipment OEM. All these actors create a virtuous cycle of fidelity and continuous improvement complemented by data monetization.
* We design our architecture from a consortium point of view with benefits shared with each actor and guarding data privacy, provenance, certification, accountability and validity with the closed set of participants, which is needed for an automotive ecosystem.
## 2 Materials and Methods
### Mean-Field Model  
The TVB-AdEx model is based on a mean-field method capturing the second-order firing rate of an excitatory and inhibitory population of adaptive exponential integrate-and-fire neurons [25] using a Master Equation formalism [19; 26], as well as the first order of the adaptive current of the excitatory population [20]. It results in a system of differential equations describing the time evolution of the mean firing rate (\(\
u\)) of the two populations\((\mu=e,i)\), the variance (\(c_{\lambda,\eta}\), where \(\lambda=\eta\)) and covariance (\(c_{\lambda,\eta}\), where \(\lambda\
eq\eta\)) of excitatory and inhibitory firing rates (\(\lambda=e,i\) and \(\eta=e,i\)), and the mean adaptive current (\(W_{\mu}\)) for the excitatory population.  
\[T\frac{d\
u_{\mu}}{dt} = (F_{\mu}-\
u_{\mu})+\frac{1}{2}\epsilon_{\lambda\eta}\frac{\partial ^{2}F_{\mu}}{\partial\
u_{\lambda}\partial\
u_{\eta}} \tag{1}\] \[T\frac{d\epsilon_{\lambda\eta}}{dt} = \delta_{\lambda\eta}\frac{F_{\lambda}(1/T-F_{\eta})}{N_{\lambda}} +(F_{\lambda}-\
u_{\lambda})(F_{\eta}-\
u_{\eta})\] (2) \[+\frac{\partial F_{\lambda}}{\partial\
u_{\mu}}c_{\eta\mu}+\frac {\partial F_{\eta}}{\partial\
u_{\mu}}c_{\lambda\mu}-2c_{\lambda\eta}\] \[\frac{\partial W}{\partial t} = -W/\tau_{w}+b_{e}\
u_{e}+a_{e}(\mu_{V}(v_{e},\
u_{i},W)-E_{L,e}) \tag{3}\]  
where \(T\) is the time constant of the firing rate equations and covariance equations and \(F_{\mu}\) is the transfer function. \(\tau_{w}\), \(b_{e}\), and \(a_{e}\) are, respectively, the time constant, the spike-frequency, and subthreshold of the adaptation. \(E_{L}\) is the leak reversal potential and \(\mu_{V}\) is an estimation of the average voltage of the excitatory population.  
A stochastic equation, denoted as Equation (4), is employed to represent an Ornstein-Uhlenbeck (OU) process, capturing the random fluctuations of the mean firing rate, commonly known as the noise. This process is derived by introducing a mean-reverting term to standard Brownian motion, a stochastic process similar to the random movement of particles in a fluid or gas. This mean-reverting term is considered to be a stabilizing component to the motion, resulting in a tendency to return to a central value. The computation of the derivative of this stochastic equation is stored in GPU memory to be available for the next time-step.  
\[\tau_{OU}\frac{d\mathcal{O}U_{t}}{dt}=(\mu-OU_{t})+\sigma\,dW_{t} \tag{4}\]  
where \(\mu\) (=0.0) is the mean of the noise, \(\sigma\) (=1.0) is the variance of the noise, and \(dW_{t}\) is a Wiener process (Brownian motion). \(\tau_{OU}\) represents the time constant of the noise. The \(dW_{t}\) is represented by a Gaussian noise implemented with the CUDA Random Number Generation (CURAND) library, making use of a XORWOW generator, a variation on the XOR shifting generators. The next number in the sequence is generated by repeatedly taking the exclusive OR of a number with a bit-shifted version of itself [27].
## 4 Experimental Results and Discussion  
In this section, we present a series of experiments designed to test the performance of our proposed data imputation approach. The goal is to assess its effectiveness in handling missing data across various contexts, including different missing rates, data sizes, feature sizes, levels of information, and correlations between features. All experiments utilize tabular data.  
In the first experimental scenario, we employed synthetic datasets to identify the most effective optimization algorithm among the PSO, GWO, SCA, and WOA. Synthetic datasets are advantageous because they allow us to explore scenarios that may be challenging to replicate with real data, and they are also easier to visualize. In the second scenario, we tested our proposed imputation approach using approximately 10 real datasets. Here, we compared the performance of the selected optimization algorithm against other state-of-the-art data imputation algorithms to identify the most effective imputation method. Finally, in the third experiment, we applied several tabular datasets extracted from a video-surveillance system. We compared our proposed imputation method with various state-of-the-art algorithms to evaluate its performance.  
In any of our experimental scenarios:  
* All original datasets initially had no missing values. Incomplete datasets were generated using the MCAR (Missing Completely At Random) mechanism, with varying rates of missing values. The number of missing values followed a Poisson distribution, where the mean was the missing rate multiplied by the feature size for each data point in the dataset.
* To evaluate the performance of all imputation methods, including our proposed approach, we tested different missing rates: 5%, 15%, and 25%.
* \(n\): is the number of instances or observations in the dataset.
- \(\mathbf{x}_{i}\): is the original value for the \(i\)th observation.
- \(\hat{\mathbf{x}}_{i}\): is the imputed value for the \(i\)th observation. To further compare the performance of all algorithms, we computed the average ranks, which determine the rank (_rnk_) of the proposed imputation method relative to all other methods.
* Each experiment was repeated 10 times to mitigate the effects of randomness in some imputation methods.
## 3 Results  
The final report on each daily working activity included three different time counts in seconds: the total recording time (TRT), the face detection time (FDT) and the screen fixation time (SFT).  
The TRT coincided with the shift duration, which varied among the workers. The FDT cumulatively measured the time in which a worker's face was in front of the screen (at the working station), even if they were not directly using the computer (e.g., working on paper sheets or making phone calls). The SFT counted the cumulative time of screen fixation by the worker, giving us a real indication of active eye load. The main descriptive results of such variables are reported in Table 1.  
We observed wide differences in the different time parameters among the workers, possibly owing to different work tasks. The workers with lower time values confirmed that, owing to unscheduled firm necessities, the activity was mostly performed in a setting that was different from the usual setting.  
The data showed intrinsic consistency, as the FDT was lower than the TRT (slightly less than half, on average), and, in turn, the SFT was lower than the FDT. The recordings included lunch breaks as well as any other breaks throughout the day.  
The workers agreed that the prototype did not interfere with normal daily activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Min.** & **Mean \(\pm\) SD** & **Median (IQR)** & **Max.** \\ \hline TRT, s & 16,445 & 23,434 \(\pm\) 3659 & 22,577 (21,530–25,572) & 31,443 \\ \hline FDT, s & 1073 & 11,030 \(\pm\) 4616 & 11,859 (8202–13,736) & 18,954 \\ \hline SFT, s & 836 & 6619 \(\pm\) 4637 & 5714 (2763–10,904) & 18,811 \\ \hline FDT (\% of TRT) & 3 & 48 \(\pm\) 20 & 47 (38–61) & 87 \\ \hline SFT (\% of FDT) & 3 & 29 \(\pm\) 20 & 25 (12–45) & 81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the different times (in seconds) monitored by the system (IQR: interquartile range; Min.: minimum value of the distribution; Max.: maximum value of the distribution).  
Figures 4 and 5 show two different user-VDT interaction patterns, as elaborated by the system. The spikes (red and yellow) are the single events recorded: fewer spikes indicate continuous activity, and more spikes represent fragmented activity (many more stops and starts). The events were recorded as \"on\" or \"off\"; thus, the diagram presenting a continuous line means that the activity continued without interruptions (values on the y-axis: 2: eye fixation; 4: face detection) or the user took a break (values on the y-axis: 1: eye fixation; 3: face detection).  
Figure 4: Events recorded by the software of a VDT user during his work shift. As we can see, there was a break from activity at the PC of nearly 3 h.  
Figure 5: Another example of a VDT use pattern. We can observe the difference between single events recorded for facial detection and fixation.
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 2 Related Work
It is worth noting that most existing clustering methods often require some form of supervision or expert knowledge, such as specifying the number of clusters or setting similarity thresholds and algorithm-specific hyperparameters. The research gap in the provided passage is that most previously investigated clustering methods for data mining relied on manual parameter selection to identify clusters from raw data. However, these methods often faced challenges in high-dimensional datasets and provided relatively lower performance. This suggests a need for a clustering method that can overcome these limitations and offer improved performance in high-dimensional settings. To address this research gap, this study extends the work on developing the novel clustering method, called DPPA, developed by [37]. This method is non-parametric and can calculate 1-NN and Max-NN by analyzing the positions of data in the dataset, eliminating the need for initial manual parameter assignments. The proposed method is evaluated using well-known publicly available datasets and offers the advantage of being less time-consuming by reducing the dependence on artificial parameter selection. Furthermore, the performance of the proposed method is compared with four popular clustering algorithms (DBSCAN, K-means, affinity propagation, and Mean Shift) to assess its effectiveness. In summary, the research gap pertains to the limitations of existing clustering methods in high-dimensional datasets, while the proposed solution is the development of the DPPA algorithm that addresses these limitations and provides improved performance.