esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection Techniques  
DUBStepR (determining the underlying basis using stepwise regression) is a feature selection algorithm that is used to identify important genes for a given phenotype [19]. It uses a novel measure called the density index (DI) to identify gene-gene correlations and to evaluate the inhomogeneity in feature space. It leverages these correlations to perform stepwise regression analysis to select the most informative genes. The algorithm is designed to work with high-dimensional datasets and can be applied to a wide range of biological and medical applications. This tool can be useful in identifying key genes and pathways associated with a specific phenotype or disease and can aid the development of new diagnostic or therapeutic strategies. One limitation of the DUBStepR algorithm is that it is based on gene-gene correlations, which may not always accurately reflect the underlying biology of a phenotype or disease. Additionally, the algorithm is based on stepwise regression, which can be sensitive to the choice of initial genes and may not always converge to the optimal solution.  
Random forest is a popular machine learning algorithm that is commonly used for classification and regression tasks. One of the key strengths of random forest is its ability to provide insight into the relative importance of each feature (or variable) in the dataset. Variable importance, as calculated by random forest, can be used as a feature selection method. The idea is to rank the features based on their importance and select the top-ranking features for further analysis or use in a predictive model. This method has several advantages over traditional feature selection methods since random forest can handle non-linear relationships and interactions between features, and it does not require the assumption of independence between features. However, there are also some limitations to using random forest for feature selection. One main limitation is that the feature importance scores can be affected by the correlation between features. It should be used in combination with other methods and domain knowledge to come to a more informed decision.  
Furthermore, we applied the statistical strategy using the BPSC package [20]. BPSC (Beta-Poisson model for single-cell RNA-seq data analyses) is an R tool for the analysis of single-cell RNA-seq data. It uses a statistical model called the Beta-Poisson model to analyze the expression of genes in individual cells. The Beta-Poisson model is a flexible model that can handle over-dispersed count data, which are a common characteristic of single-cell RNA-seq data. One of the major advantages of BPSC is that it is able to handle high-dimensional single-cell data and it can be used to identify biologically meaningful gene expression patterns. However, as with any computational method, it also has some limitations. The performance of BPSC depends on the quality of the input data and the specific characteristics of the sample.
## 2 Materials and Methods
### Laboratory Data Analysis
#### 2.3.5 Ps Estimation Method 4: Nonlinear Neural Network Model  
Recently, a method was developed to combine the vocal function measures in a non-linear neural network model in an effort to increase the accuracy of Ps estimation [46]. The neural network consisted of two fully connected hidden layers with four neurons in each layer. The input to the network included all the measures listed in Table 2, except for RMS, CPP, HRF, and NAQ. Moreover, the model included as input the acoustic SPL extracted from the microphone signal since the microphone signal is available in the laboratory setting. The number of layers and neurons was chosen according to the best results reported for laboratory test data, which are the same conditions that were analyzed in this study. The output of the network has four neurons that yield estimates of Ps, vocal fold collision pressure, and muscle activation levels of the thyroidenoid and cricothyroid muscles. In contrast with the multiple regression model (Ps Estimation Method 3), the neural network model was pre-trained using simulated vowel signals, with radiated acoustic pressure (15 cm from the lips) ranging from 60 to 100 dB SPL, that were synthesized using a voice-production model consisting of a triangular body-cover model of the vocal folds and planar sound-wave propagation [46; 97]. The multidimensional space of the model-control parameters was sampled to synthesize 13,000 vowel segments that represented a range of typical (non-disordered) phonatory configurations. The network architecture was selected to maximize the model's predictive performance against experimental recordings of intra-ral pressure in 79 vocally typical female participants uttering consecutive /pae/ syllable strings at comfortable, loud, and soft levels, and was adjusted for the SPL conditions in this study (15 cm versus 10 cm distance microphone distance).
# Acoustic Shooting and Bounce Ray Method for Calculating Echoes of Complex Underwater Targets
## 5 Acoustic Shooting and Bounce Ray Method
### Intersection Test between Acoustic Beam Pipeline and Plane Triangular Plane Element  
After tracing the acoustic beam pipeline to the interior of the target subspace, it needs to perform the intersection test with the target surface element. This process can be regarded as a confirmation of the relationship between the intersection point and the target surface element position [16].  
As shown in Figure 4, the ray \(P=P_{0}+tU\) originating from the tracking point \(O^{\prime}\) of the space acoustic beam pipeline has an initial position vector of \(P_{0}\) and a direction vector of U. After traveling a distance of \(t_{c}\) along the direction of \(U\), \(O^{\prime}\) intersects with the triangular plane element \(S_{0}\) on the target surface with point \(V_{c}\). Given the coordinates of the three vertices \(V_{0}\), \(V_{1}\) and \(V_{2}\) of the triangular plane element, confirm whether \(V_{c}\) is in the \(S_{0}\) plane, and it can actually be transformed into the problem of judging the position the relationship between \(V_{c}\) and the three vertices and triangle sides, namely for point \(V_{2}\), determine whether \(V_{c}\) and \(V_{2}\) are on the same side of edge \(V_{0}\)\(V_{1}\). Similarly, determine the positions of points \(V_{0}\), \(V_{1}\) and \(V_{2}\). If they all meet the same side relationship, then \(V_{c}\) is in the \(S_{0}\) plane. Similarly, determine the positions of points \(V_{0}\), \(V_{1}\) and \(V_{2}\). If they all meet the same side relationship, then \(V_{c}\) is in the \(S_{0}\) plane.  
Using the vector operation property, the above judgment is expressed as:  
\[\left\{\begin{array}{ll}if&\prod\limits_{i=0}^{k-1}\left\{(\overrightarrow {V_{c}V_{i}}\times\overrightarrow{V_{i}V_{i+1}})\cdot(\overrightarrow{V_{i+ 2}V_{i}}\times\overrightarrow{V_{i}V_{i+1}})\right\}>0&V_{c}\in S_{0}\\ &\mbox{\emph{else }}V_{c}\
otin S_{0}\end{array}\right.\begin{array}{ll}n=n \mbox{ \emph{mod }}k\\ \end{array}\right. \tag{21}\]  
In the formula, \(k=3\); \(n=i\), \(i+1\), \(i+2\) in all the lower indices.  
Figure 3: Object subspace partition schematic.
## 5 Experiments and Results
### Quantitative Results
After manual localization of LAA, they employed the FCNs and fine-tuned them to segment each 2D LAA image slice. Further, they used a dense 3D CRF to refine the segmentations of all slices. [PERSON] et al. [45] proposed a method for DA and AA segmentation low-dose chest CT without contrast enhancement. They used a dilated convolutional neural network (CNN) that classifies voxels in axial, coronal, and sagittal image slices. The probabilities of the three planes were averaged per class and voxels were subsequently assigned to the class with the highest class probability to obtain final segmentation. Furthermore, [PERSON] et al. [46] proposed a probabilistic deep voxelwise dilated residual network named Bayesian VoxDRN that can predict voxelwise class labels with a measure of model uncertainty. By utilizing the dropout process, the model is able to learn weight distributions with a higher degree of data explanation. This considerably reduces the likelihood of overfitting. Nevertheless, it is important to highlight that compared methods use different datasets, often not publicly available for the research community. Datasets differ in the number of patients, 2D images per patient, and image modality. Moreover, most of the previous studies report the results of the commonly researched cardiovascular structures such as whole heart, left atrium, left ventricle, right atrium, and right ventricle (RV), while cardiac structures investigated in this work are significantly less represented in previous research (due to unavailability of publicly available datasets). This makes it hard to provide representative and quality comparisons with our work.
## 2 Battery Model Settings
### The Batteries
#### 2.1.4 Sodium Ion Battery (NIB), Na-Ion  
Many attempts still need to be made to make this technology competitive with the others in both the field of anodic materials as well as in new electrolytes. However, it is interesting to compare the environmental impact of this type of new accumulator to the existing technologies.  
In practice, researchers are starting to focus on this battery type because it can overcome the issue related to lithium resource scarcity, which is a relevant problem considering the expected and very fast growth of the demand in the use of lithium in low emission batteries as well as in hybrid and electric vehicles. Furthermore, lithium reserves are mainly located in remote or politically unstable areas. Therefore, geopolitical aspects can cause an increase of the future cost, extraction times, and the necessity of using lower quality resources.  
The distinctive trait of the NIB is the specific energy density per cell; it is up to 136 Wh kg\({}^{-1}\), thus comparable or even slightly higher than that of the existing LFP cells.  
Estimation of the achievable service life of NIBs is scarce due to the low technical maturity of the technology. The technical sheets for commercial use report at least 300 charge-discharge cycles with an initial conservation capacity of 80%, while 2000 cycles are considered feasible by some research studies [27; 28].
## 3 Results and Discussion
### Soil Analysis
The soil contained relatively high levels of Fe oxides (35.08 g kg\({}^{-1}\)), while Al and Mn oxides were less abundant (5.83 g kg\({}^{-1}\) and 0.15 g kg\({}^{-1}\), respectively). The high contents of Fe oxides suggest their significant role in heavy metal retention, particularly through chemisorption mechanisms [43]. The pseudototcal metal analysis showed that Cd was below the detection limit (<0.01 mg kg\({}^{-1}\)), confirming the absence of Cd in the soil. This ensures that Cd detected in the soil originates from the experimental treatments. In addition to Cd, pseudototal concentrations of other metals were analyzed to provide a detailed characterization of the agricultural soil (Table 2). These metals can have potential competitive interactions with HANPs during the immobilization of Cd. In particular, Cu and Zn may have a strong affinity for adsorption onto mineral surfaces, including H [45]. High concentrations of these metals may compete with Cd for sorption sites on the HANPs, potentially reducing their effectiveness in immobilizing Cd. In this manner, Cr and Ni, although less abundant in agricultural soils, also show potential mobility under acidic soil conditions and their ability to form stable complexes with soil organic matter and mineral oxides, which may influence the dynamics of metal immobilization [46]. In any case, only the Cu levels exceeded the reference values for non-industrial soils in Galicia [34]. This elevated Cu concentration may be attributed to agricultural practices, such as the use of Cu-based fungicides, which are common in the region. Other metals, such as Zn, Cr, and Ni, were within typical background levels for soils in similar environments. Thus, all these characteristics make the soil suitable for studying the effects of HANPs on Cd immobilization, providing a controlled environment to evaluate the interactions between Cd, treatment with HANPs, soil components, and quinoa.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 3 Network and System Model
### System & Channel Model  
Since the vehicle is assumed to be moving, it will encounter both small- and large-scale fading and therefore will have different channel model compared to a cellular user which is static or moving very slowly. The SINR of the two types of users, is given by Equations (1) and (2):  
\[\gamma_{c}=\frac{P_{c\kappa}\,h_{c,B}}{\sigma^{2}+P_{\
u\kappa}\,h_{\
u\kappa}} \tag{1}\]  
\[\gamma_{v}=\frac{P_{\
u\kappa}\,h_{\
u}}{\sigma^{2}+P_{c\kappa}\,h_{c\
u}} \tag{2}\]  
The channel power gains are denoted by \(h\) in Equations (1) and (2). The subscript '\(x\)' denotes the frequency resource block (RB) shared among users. We assume that the RB of V2P user can be shared by a CUE, but resource blocks cannot be shared between VUEs. The gain \(h_{c,B}\) is the desired channel gain from a CUE user to the base station, \(h_{\
u,c}\) is the interference gain from the D2D pair to the CUE user, \(h_{\
u}\) is the desired gain between the D2D based V2P pair, and \(h_{c,\
u}\) is interference gain from CUE to the D2D pair. The channel gain, in turn is dependent on pathloss, shadowing, distance, and small-scale fading component; given by Equation (3):  
\[h^{a,b}=PL\times\ Y\ \times\ell^{-\gamma}\ \times g^{a,b} \tag{3}\]  
where \(PL\) is the path loss, \(Y\) is the log-normal shadowing component, \(\ell\) is the distance between transmitter and receiver, and \(\gamma\) is the decay exponent. The path loss models for V2P and CUE are given in Table 1.  
Figure 1: A Road-Side Network Scenario.
## 2 Materials and Methods
### Improvement of Acquisition Rate
#### Communication protocol optimization

Table 4: Comparison of data transmission methods. The data communication between FPGA and USB chips is achieved by reading and writing FIFO data from the four ports (EP2, EP4, EP6, EP8) of CY7C68013A. The FPGA is the main device, and the port of CY7C68013A is the slave device. The slave FIFO can operate in synchronous or asynchronous mode. Due to the internal clock of the chip being 48 MHz and the clock of the EIT system being 50 MHz, the slave FIFO is set to operate in asynchronous mode. The firmware program of CY7C68013A was completed in the Keil uVision development environment. Through the firmware program, configure the dual function IO of CY7C68013A to Slave FIFO mode, pin FLAG to indicate the empty flag of EP2 port, FLAG to indicate the full flag of EP2 port, and FLAG to indicate the full flag of EP6 port. Additionally, ports 2 and 4 are configured as Bulk outputs, while ports 6 and 8 are configured as Bulk inputs. Burn the generated iic file into the chip's EEPROM to ensure that the power on the program is not lost. The reading and writing timing diagram of the chip is shown in Figure 8. The read-and-write control of the chip is implemented by two state machines, and its program is written in Verilog. The state transition diagram of the reading and writing process is shown in Figure 9.  
Figure 8: Reading and writing the timing diagram of the chip. (**a**) Read; (**b**) Write. Figure 7: Internal structure diagram of CY7C68013A.
## 5 Experiment and Results
### Evaluation of Level-2
#### 5.2.1 Accuracy of Tweet Classification  
We implemented the proposed algorithm using the Python language and evaluated it on datasets collected from Twitter. Six datasets built using TF-IDF as a criterion for their importance were used to experiment with 12 queries (12 stories). Based on the number of important words belonging to the vehicular traffic in each tweet, TP, TN, FN, and FP were calculated. Finally, accuracy, precision, recall, and F-scores were calculated. The results shown in Figure 8 indicate that an average accuracy of nearly 97% was achieved by the proposed algorithm. This high accuracy is the result of a simple matching process with words belonging to the vehicular traffic.
## 4 Discussion
### EMNEP Model and Its Simulation  
The hourly NEPs were calculated by GPP-Re using the 3-factor and 2-factor EMGPP and EMRe. It is clear that the NEP performance in the hourly, monthly, and annual NEP simulations were gradually improved when more processes in respiration were considered (e.g., the bias and RMSE values were decreased, Section 3.3). The purpose of developing four types of Re empirical models (Section 3.2) was for the better simulation of NEP, one of the most important factors in carbon exchange, and the best NEP estimations, were found by using Re (LDDR) and the corresponding GPP models (Section 3.3). Finally, the NEP empirical model captured most of the hourly, monthly, and annual NEP variational patterns (within 2 \(\sigma_{\rm obs}\)). The CO\({}_{2}\) flux measurements in 2016 (July, August, and October) had some problems and caused many unusual drops in the monthly sums that exceeded 2 \(\sigma_{\rm obs}\); therefore, the simulated NEPs can be used as a reliable reference for these problematical periods in the CO\({}_{2}\) flux measurements. Generally, the monthly sums of NEP exhibited overestimates in the summer and underestimates in the winter between 2013 and 2016, and the annual sums of NEP were in 1 \(\sigma_{\rm obs}\) for 2013 and 2014 and 2 \(\sigma_{\rm obs}\) for 2015.  
Under S/Q \(<\)0.5 conditions (low GLP loads, low cloud amounts, and aerosol concentrations), the constant in the Re (LR) method was close to 0, indicating that the PAR attenuated by atmospheric substances plays absolute dominant roles in respiration, while the scattered PAR does not. Under S/Q \(\geq\) 0.5 conditions (high GLP loads, cloud amounts, and aerosol concentrations), both the attenuated and scattered PAR by GLPs contribute respiration, or both the direct and diffuse PAR contribute the respiration and the diffuse PAR contributes much more than that at S/Q <0.5. A similar result is also reported [82; 83; 84].  
The respiration is small compared to NEE and GPP, and very difficult to be simulated accurately. The improved performance in the NEP simulations (hourly to annual sums) from the Re (LR) to Re (LDDR) method revealed that some factors (dark respiration and photorespiration, etc.) should be taken into consideration in the respiration and NEP processes. Based on these Re calculation results, the different NEP estimates can be used as references to evaluate NEP uncertainties to some extent.
## 5 Discussion
### Limitations of Pupilometry in Exploring Face Processing  
While our study demonstrates that pupillometry can reveal differences in face processing among NHPs, certain limitations must be acknowledged. Compared to commonly used brain research techniques, such as MRI, multi-channel electrophysiological recordings, and EEG, pupillometry offers several advantages, including shorter preparation times, no need for consumable materials, absence of required anesthesia, and non-invasive measurements.  
Therefore, it posits that pupillometry is a valuable and suitable tool for studying NHPs as experimental models.  
However, pupillometry does present some challenges. Firstly, it typically requires a longer latency to reveal differences in face processing. EEG and MEG studies have shown that the N170 and M170 components reflect differences in face processing in humans around 150-200 ms post-stimulus [60; 61; 62]. In NHPs, similar components appear approximately 112 ms after face presentation [63], while pupillometry may need 200-1000 ms to reveal processing differences. Secondly, controlling low-level properties of visual input, such as luminance, spatial frequency, and color, is essential in pupillometry studies, as these factors can significantly impact pupil size [64]. However, controlling these properties can affect subjective experiences with the images [65; 66], which may also influence pupil size. Thirdly, the neural mechanisms modulating pupil size changes are not fully elucidated; thus, pupillometry serves as an indirect method to assess brain activity [11]. It is essential to consider these limitations prior to implementing pupillometry in a particular study. Integrating artificial intelligence with pupillometry may further enhance its application in future cognitive research and the diagnosis of disorders [67].
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 1 Introduction
### Datasets  
Various datasets containing crowd videos and images are publicly available and are being used to validate the experimental results. Some of the publicly available datasets along with its description are shown in Table 1.  
UCSD dataset was the first dataset used for people counting [32]. The data has obtained through a camera installed on a pedestrian pathway. The dataset includes 2000 frames 238 \(\times\) 158 of video sequences, along with ground truth annotation of each pedestrian in every fifth frame having 49,885 pedestrian in total. The Mall dataset has been collected by means of surveillance cameras installed in a shopping small [33]. It has a total of 2000 frames with size of 320 \(\times\) 240. UCF\({}_{-}\)CC\({}_{-}\)50 dataset [2] is a challenging dataset comprising of a wide variety of densities and various scenes. This dataset has been obtained from different places like concerts, political protests, stadiums and marathons.  
The entire numbers of annotated images are 50 containing 1279 individuals per image. This dataset has a varying resolution and the individuals differ from 94 to 4543 representing a large variation in the image. The drawback of this type of dataset is that, there is only limited number of images available for training and evaluation. WorldExpo'10 dataset introduced in [34] has been used for cross scene crowd counting. The dataset comprises of 3980 frames of size 576 \(\times\) 720 with 199,923 labeled pedestrians. The maximum crowd count through this dataset is limited to 220 and is insufficient for evaluating extremely dense crowds counting. The Shanghai Tech dataset [35] has been introduced for large scale crowd counting containing of 1198 images with 330,165 annotated heads. In terms of annotated heads, this dataset is one of the largest. The dataset contains two types, namely Part A, Part B. Part A is made of 482 images taken from the internet randomly. Whereas Part B comprises of 716 images collected from the metropolitan street in shanghai. The most recent dataset is UCF-QNRF [11] having 1535 images. Within this dataset, the number of people fluctuates from 49 to 12,865 making a massive variation in density. Furthermore, it has a huge image resolution ranging from 400 \(\times\) 300 to 9000 \(\times\) 6000 and consists of crowd videos with varying densities and perspective scales. CUHK dataset has been collected from diverse locations namely, street, shopping malls, airports and parks. The dataset comprises of 474 videos clips from 215 scenes [36].  
This review paper is mainly focusing on the crowd monitoring (crowd counting, crowd localization and behavior detection). The rest of the paper is organized as follows. Section 2 shows search methodology and Taxonomy Level. In Section 3, crowd monitoring approaches taken from the previous literature are summarized in tabular form. Section 4 shows crowd related research approaches. Section 5 shows the convolutional neural network and deep CNN frameworks. Section 6 represents the discussion and Section 7 elaborates conclusions.