esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool
## 3 Methods
### Improved YOLOv5 Algorithm  
The YOLOv5, a one-stage object detection algorithm, was developed by the Ultralytics LLC team. Compared to other networks of similar size, YOLO demonstrates superior performance [26]. Moreover, the YOLOv5s model boasts a compact memory footprint [26]. This paper adopts the YOLOv5s architecture to facilitate future applications on embedded devices. As shown in Figure 2, the structure of the improved YOLOv5 algorithm is divided into four parts: Input, Backbone, Neck, and Output [26; 27]. The Input encompasses Mosaic data augmentation, adaptive anchor frame calculation, and adaptive image scaling. The Backbone network comprises Conv units, C3 units, and a spatial pyramid pooling-fast  
Figure 1: The structure of ICA and BiFPN. (**a**) ICA, the parts highlighted in yellow denote improvements; (**b**) BiFPN.  
(SPPF) unit [27]. The Conv unit sequentially performs convolution calculations, batch normalization, and the SiLU activation function. The C3 module equally divides the input tensor into two branches and conducts convolution operations. One branch passes through a Conv unit and subsequently through multiple Resunit [33], which comprise a residual structure (Figure 2). The other branch performs direct convolutions and then concatenates the outputs of the two branches to operate a Conv unit. The Neck primarily integrates the ICA module, BiFPN, Conv units, and C3 units. The BiFPN comprises BIF-Add2, BIF-Add3, and other units, with \"BN\" representing batch normalization. In contrast, the Neck of the original YOLOv5 algorithm leverages the pyramid attention (PAN) network [34], a path aggregation network. In the Output, three detection layers are dedicated to identifying large, medium, and small targets.  
However, when applying YOLOv5s to detect drowning targets on the surface of swimming pools, certain limitations become apparent, such as its restricted capability to address target deformation and posture changes. These factors impede detection efficacy and heighten the difficulty of tasks. Overcoming these issues while preserving the algorithm's efficiency is a significant challenge in drowning detection. Hence, this study introduces an improved YOLOv5 algorithm specifically designed for water surface target detection, as illustrated in Figure 2. Key innovations in this algorithm include (1) Inserting the ICA module enhances the model's capability in feature extraction and facilitating learning regarding deformed targets and posture changes and (2) replacing the PAN with the bi-directional feature pyramid network (BiFPN). The integration of BiFPN into the YOLOv5 model facilitates more robust feature fusion in the neck, enhances connections between features across different layers, and notably improves the model's recognition accuracy [27; 35].
# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
## 4 Discussion
### BM and the Trade-Off Waterfall  
Figure 7 is a Trade-off Waterfall that illustrates the trade-off between the _benefit of intervention timeliness_ and the _cost of intervention success_. Intervention success is determined by the model's accuracy. A red bar represents a decrease in accuracy from one week to the next, and a blue bar indicates an increase. The length of a bar indicates the magnitude of change in accuracy, \(\kappa(t)\).  
The trade-off between the _benefit of intervention timeliness_ and the _cost of intervention success_ could help identify whether there were patterns over each year that could inform users about the _optimal_ week (\(t^{*}\)) to intervene. If the current year was 2019, then \(t^{*}\) for 2019 (\(t^{*}_{2019}\)) could be determined by either one or a combination of the following factors:  
1. \(t^{*}_{2019}\) was chosen to be the \(t\) in 2018 that yielded the maximum value of \(\kappa(t)\) in 2018.
2. \(t^{*}_{2019}\) was based on _exogenous_ considerations determined by the institution's stakeholders. Examples of exogenous considerations were the urgency required for intervention and resources required to make interventions.  
For instance, the 2018 Trade-off Waterfall was not available to this study. Therefore, the \(t^{*}_{2019}=17\) used in this cohort's B-PM and BM was based only on the exogenous consideration that interventions should be conducted by week 17.  
Practical Benefits and Limitations of the Trade-Off Waterfall  
The Trade-off Waterfall was computed after the Outcome of the students was made available. It did not show the week that produced the highest accuracy in real-time, and, in some weeks, the trade-off between accuracy and timeliness was not positive. For example, observe that \(\kappa(15)<\kappa(14)\). In exchange for a delayed intervention, BM produced a worse \(\kappa\) score from \(\{L(s)_{t}\}\), which would not have made the delay worthwhile. A similar observation was determined for the delay between weeks 18 and 19. Therefore, there was no way to infer the optimal week to make predictions and interventions in real-time. Instead, the Trade-off Waterfall indicated that:  
1. The general trade-off was that \(\kappa(t)\) increased as \(t\) increased;
2. The trade-off peaked at some point, and, in this case, three weeks before the examination period at \(t=18\). Therefore, it may not be worth waiting for the start of an examination period (such as \(t=21\)) before conducting interventions (given the login data of this cohort, different cohorts and different datasets from those presented in this report may produce different peak periods). For example, the Trade-off Waterfall showed that, after \(t=18\), there was no benefit of waiting for an extra one, two or even three weeks to intervene because \(\kappa(19)\), \(\kappa(20)\), \(\kappa(21)<\kappa(18)\).
# Research on the Influence of Rectifying Orifice Plate on the Airflow Uniformity of Exhaust Hood
## 4 Experiment and Discussion
The surface of the exhaust hood was divided evenly into 9 areas, and the measuring points 1 to 5 of the wind velocity was located at the center of each area, as shown in Figure 1c. Each measuring point was tested continuously for 20 s, and the measured velocity and average velocity were recorded and calculated, as shown in Figure 5.  
The experiment demonstrates that the wind velocity fluctuated around the average wind velocity by about 0.1 m/s. Moreover, the average wind velocity at each measuring point exceeded 0.5 m/s. This result is consistent with our simulation results. Using the measured velocity obtained from the nine measuring points, the average wind velocity and the wind velocity non-uniformity of the exhaust hood were calculated according to Equation (2). The results indicated that the average wind velocity was 0.5917 m/s,  
Figure 4: (**a**) Wind velocity isopleth maps; (**b**) flow field visualization experimental device; (**c**) measuring the wind velocity with a high-precision wind velocity probe. more than 0.5 m/s [22], with a wind velocity non-uniformity of 9.84%, less than 25%. These experimental results are in good alignment with the simulation data. Our study demonstrates that rectifying orifice plates significantly affects the airflow uniformity of exhaust hoods. Specifically, the double-layer rectifying orifice plate configuration enables the airflow within the exhaust hood to be distributed in parallel, effectively enhancing the capture efficiency of dust and toxic pollutants.
# Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection
## 2 Related Works  
### Three-Dimensional Pulmonary Nodule Detection  
The early detection of pulmonary nodules is the best way to prevent and treat cancer. With the gradual increase in lung cancer patients in recent years, there has been much interest in using computer-aided diagnosis (CAD) systems to improve radiologists' efficiency. Most CAD systems have two stages: 1. candidate pulmonary nodule detection and 2. false positive pulmonary nodule filtering. Most previous work has used 2D anchor-point detection networks [3]. However, 2D or multi-view 2D models can lose important information from the original image and fail to identify pulmonary nodules accurately. Therefore, the use of 2D anchor-point detection networks is often combined with 3D networks to screen for false positive nodules and achieve more accurate detection. In recent years, many scholars have constructed 3D candidate nodule detection networks that do not need to be coupled with a false positive nodule filtering network to achieve higher accuracy. For example, [PERSON] et al. [19] extended the DenseNet model to a pulmonary nodule detection task. They achieved a competitive performance metric (CPM) of 0.897 on the LUNA16 dataset by combining five 3D dense blocks and the maximum pooling layer. [PERSON] et al. [20] incorporated a one-shot aggregation block, the receptive field block, and a feature fusion scheme into the YOLOV3 model. They eliminated duplicate detection by a non-polar depressed value algorithm, achieving a CPM of 0.905 on the LUNA16 dataset. [PERSON] et al. [9] proposed a centroid matching model (SCPM-Net) based on a 3D sphere representation. SCPM-Net, compared to traditional pulmonary nodule detection models, does not require the manual setting of anchor parameters to achieve good outcomes.
## 3 Results  
### Spatial Pattern  
Using the 1 km resolution 8-day composite MODIS and BESS GPP data from 2001 to 2015, this study calculated the spatial distribution of multiyear mean GPP across China. Figure 2 showed that both GPP products exhibited substantial spatial heterogeneity with a decreasing trend from the southeast coast to the northwest inland area. This can be attributed to the dry climate with sparse vegetation cover in the northwest region, while the climate of the eastern area is relatively humid with good thermal conditions appropriate for the growth of vegetation all year round. In addition, the GPP of Southern China was apparently larger than that of Northern China because of differences in vegetation phenology. Nevertheless, the study implied that both MODIS and BESS GPP products had a great number of missing values in the arid and semiarid Northwest China including the midtemperate semiarid region (III), the warm temperate arid region (IV), and the plateau zone (VI), which meant that these approaches reduced the ability to estimate GPP in these areas.  
In view of the eight climate zones across China (Figure 3), terrestrial GPP of both MODIS and BESS models exhibited the largest value in the tropical humid region (VIII) with approximately 5.28 g C m\({}^{-2}\) d\({}^{-1}\), followed by the subtropical humid region (VII: approximately 3.55 g C m\({}^{-2}\) d\({}^{-1}\)). Then, the GPP values over the warm temperate subhumid region (V), cold temperate humid region (I), and midtemperate humid region (II) were quite close. The GPP of the midtemperate semiarid region (III) was about 0.91 g C m\({}^{-2}\) d\({}^{-1}\), and the lowest GPP values were found in the warm temperate  
Figure 2: Spatial distribution of multiyear mean GPP across China from 2001 to 2015. (**a**) and (**b**) represent MODIS and breathing Earth system simulator (BESS) GPP, respectively. The black line delineates the boundary of eight climate zones. The insert shows the full southern extent of China’s islands.  
arid region (IV) and plateau zone (VI) below 0.7 g C m\({}^{-2}\) d\({}^{-1}\). However, obvious differences existed between the MODIS and BESS GPP values over these climate zones with the biases ranging from 0.028 g C m\({}^{-2}\) d\({}^{-1}\) to 0.185 g C m\({}^{-2}\) d\({}^{-1}\). Generally, BESS GPP is relatively higher than MODIS GPP due to a separate treatment towards C3/C4 photosynthesis pathways, except for the tropical humid region (VIII). Most areas of cropland in the North China Plain and Northeast China planted the C4 maize, and 60% of C4 species were grasses, distributed in the grassland ecosystems of Northwest China and the Tibet Plateau.
## 4 Simulation and Analysis of Results
### Analysis of Diversity  
Under the data of ulysses16, as shown in Figure 12, SMA only gets path 1. However, the optimization process of TPSMA obtains the path 1 and path 2, which is the same as the length result of SMA. At the same time, in Figure 12, TPSMA gets two groups of paths 5 to 6 and paths 7 to 9. The path lengths of each group are the same but the paths have different orders of points. Similarly, TPSMA will produce a variety of cases due to the addition of random factors. Therefore, the diversity and comprehensiveness of the optimization results are increased.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Missions
#### 7.1.3 Multi-Footprint Observation LiDAR and Imager (MOLI)  
JAXA's Multi-Footprint Observation LiDAR and Imager (MOLI) aims to map the forest canopy and evaluate biomass. MOLI will employ a two-beam LiDAR system that creates parallel paths separated by 50 m, allowing detailed canopy surface measurements. Operating at a wavelength of 1064 nm with a pulse repetition frequency of 150 Hz, MOLI is designed to provide high-resolution data with a 25-meter resolution. This instrument, which will be deployed on the International Space Station (ISS) from 2024 to 2028, aims to improve our understanding of forest structure and contribute to global environmental monitoring efforts [171; 172; 173].
# Investigation of THz Absorptive Signatures in Opioids
###### Abstract  
We investigate the possibility of sensing opioid drugs, such as fentanyl, by their THz electromagnetic signatures. The methods include both computer modeling and experiments. Molecular dynamics simulations predict that fentanyl should display THz resonances, with several of them occurring below 1.0 THz; the lowest one is at around 0.337 THz (337 GHz). Spectroscopy measurements were conducted on oxycodone, which was used as a surrogate for fentanyl. They display vibrational absorption resonances between \(\sim\)1.4 and 1.6 THz.  
sensing; opioids; fentanyl; oxycodone; molecular dynamics modeling; spectroscopy; THz +
Footnote †: journal: _Applied Sciences_
# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 3 Methodology
### Feature Selection  
Feature selection enhances the accuracy and prediction capability of ML algorithms by identifying the most important variables and eliminating unnecessary ones. This technique is crucial because it helps simplify the model, reduce overfitting, and make it easier to interpret and present the results [47].  
With a dataset of 24 features, the decision-making process becomes more complex. For streamlining this process, we incorporated feature selection by utilizing the SelectKBest method and a mutual information score. Using a filter-based approach, SelectKBest assesses the significance of each feature, regardless of the ML model employed [48]. Statistical techniques are utilized to assess and prioritize the characteristics based on their correlation with the output variable. In this situation, the mutual information score plays a crucial role as it quantifies the level of dependence between characteristics and the response variable.  
The mutual information between feature \(X\) and response variable \(Y\) is defined as follows:  
\[\text{Mutual Information}=\sum_{x\in\mathcal{X},y\in Y}p(x,y)\log\biggl{(}\frac{p( x,y)}{p(x)p(y)}\biggr{)} \tag{2}\]  
where \(p(x,y)\) denotes the joint probability distribution function of \(X\) and \(Y\), whereas \(p(x)\) and \(p(y)\) represent the marginal probability distribution functions of \(X\) and \(Y\), respectively.  
Using SelectKBest with mutual information score, we identified the top 12 features, which are detailed in Table 1. These selected features, significant both statistically and clinically for CKD, are used for further analysis and model development. Adding them to the model makes it better at identifying and predicting CKD, which shows how useful it is for finding the disease early and treating it well.  
A correlation heatmap (Figure 4) is used to evaluate the linear relationships between different features of CKD. You can assess the strength and direction of these associations using the correlation coefficient, which ranges from \(-1\) to \(+1\). When both variables are increasing, we say that there is a positive correlation, and when both are decreasing, we say that there is a negative correlation. There is no linear relationship when the value is 0. Dark blue denotes high negative correlations in the heatmap, while dark red suggests strong positive correlations. The heatmap reveals that hemoglobin, packed cell volume, and specific gravity are strongly correlated with CKD. For example, specific gravity has a strong positive correlation with CKD (0.71), making it a significant predictor, while albumin shows a strong negative correlation (\(-0.61\)). This visual representation helps identify key clinical and laboratory features that play an important role in CKD detection and progression, highlighting variables that may warrant further investigation in clinical practice or ML models.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 3 Theoretical Basis
### Mean-Shift Algorithm  
The mean-shift algorithm, a density-based clustering technique proposed relatively early, has progressively gained popularity due to continuous refinement by researchers. It finds extensive applications in the realm of computer vision, including video tracking, trajectory tracking, image segmentation, and various other domains.  
* For n sample points in \(R_{d}\) within a \(d\)-dimensional space, represented as \(i=1\),..., \(n\), and any point x within this space, the mean-shift vector of that point is defined as follows: \[M_{h}=\frac{1}{K}\sum_{x_{i}  
algorithm converges to the densest point, corresponding to the highest density point. This point of convergence is termed the local density extremum. Each iteration is recorded, along with the sample points involved in the calculation. After an iteration, an unreferenced sample point is randomly selected as the starting point \(x_{i}\), and the aforementioned iterative process is repeated until all points have been accounted for, as illustrated in Figure 2.  
The local density extremum points derived from each iteration process serve as pseudo-cluster centers. Points documented throughout this procedure are allocated to these pseudo-cluster centers, with the possibility of multiple iterations yielding the same pseudo-cluster center. Consequently, all points recorded during the process are designated as pseudo-cluster centers. In instances where the distance between two pseudo-cluster centers falls below a specific threshold, they are merged into a single cluster. The dense pseudo-cluster centers thus become the true cluster centers, resulting in the final clustering outcome.  
Presently, the enhanced mean-shift algorithm has integrated kernel functions to improve the precision of sample density estimation. The Gaussian kernel function is the most commonly used one. It operates by applying a kernel function at each sample point in the dataset, followed by the summation of all kernel functions to derive the kernel density estimation of the dataset. The mean-shift vector representation that includes a kernel function is as follows:  
Figure 2: DPC-MS algorithm iteration flowchart.  
\[M_{h}(x)=\frac{\sum_{i=1}^{n}G(\frac{x_{i}-x}{h_{i}})(x_{i}-x)}{\sum_{i=1}^{n}G( \frac{x_{i}-x}{h_{i}})} \tag{6}\]  
Among them, \(G(\frac{x_{i}-x}{h_{i}})\) is the kernel function. When considering the influence of distance, it is possible to incorporate a weight function for all data points to reflect their varying levels of significance. The weight function adopted in this article is the weighted Euclidean distance, and the formula is as follows:  
\[d(x_{i},y_{i})=\sqrt{\sum_{i=1}^{n}(\frac{x_{i}-y_{i}}{s_{i}})^{2}} \tag{7}\]  
\(s_{i}\) is the standard deviation of the component, and the weighted Euclidean distance is taken into Equation (6) to obtain the following formula.  
\[M_{h}(x)=\frac{\sum_{i=1}^{n}G(\frac{d(x_{i},x)}{h_{i}})d(x_{i},x)}{\sum_{i=1} ^{n}G(\frac{d(x_{i},x)}{h_{i}})} \tag{8}\]
## 4 Performance Analysis
### Simulation Results
#### 4.3.5 Energy Efficiency  
Figure 6 indicates the energy efficiency of routing protocols. The results indicated that the energy efficiency of FSACO was 5.04-times better than EEABR, 1.7-times better than IACO and 1.8-times  
Figure 4: Comparison of energy consumption of FSACO, EEABR, IACO and SensorAnt under a static network scenario with 100 sensor nodes.  
Figure 5: Comparison of the energy standard deviation of FSACO, EEABR, IACO and SensorAnt under a static network scenario with 100 sensor nodes.  
better than SensorAnt. This means FSACO could deliver more sensing data to the sink node, while requiring equivalent energy consumption. The reason is that FSACO could find the initial effective route (i.e., feasible route) to the sink node quickly and the convergence time was less than that of other routing algorithms. This means FSACO could establish a stable optimal route quicker than EEABR, IACO and SensorAnt; hence, FSACO could deliver more sensing data to the sink node during in the same sampling time.
## 3 Results and Discussion
### Validation of the RelA Activity Assays  
Several methods to study RelA enzymatic activity in vitro and in vivo have been published [25; 26; 28; 48], and our methods were adapted from these sources. We performed two kinds of RelA activity tests: a ppGpp-dependent fluorescent reporter in vivo assay and direct (p)ppGpp detection assays in vivo and in vitro. The first method was based on the ability of ppGpp to affect expression of different genes [54]. One of these genes, _rpsJ_, encodes the 30S ribosomal protein S10 [55; 56]. Its promoter, _PrpsJ_, belongs to the r-protein family of promoters, which is strongly inhibited by ppGpp and the DksA transcriptional factors [57; 58]. Recently, a plasmid construct carrying a _wfp_ (yellow fluorescent protein) gene driven by the _PrpsJ_ was published [46]. This reporter plasmid contains the broad host range RK2 minimal replicon and is compatible with many other plasmid vectors. Comparison of the yellow fluorescent protein (YFP) activity between wild-type (WT) _E. coli_ K12 and its _relA\({}^{-}\)_ mutant confirmed the effect of ppGpp production on _PrpsJ_ activity and served as a validation of this method.  
The direct (p)ppGpp detection in vivo and in vitro assays relied on different \({}^{32}\)P radioactive nucleotides (y-\({}^{32}\)P-ATP, \(\alpha\)-\({}^{32}\)P-GTP) for use as substrates, and thin-layer chromatography (TLC) to  
Figure 1: Schematic of pipeline for determination of effective RelA inhibitors.  
separate the reaction products. Several methods were tested and optimized to give the best results for assessing the production of (p)ppGpp. It was found that the in vitro buffer system did not need to be phosphate free, as previously indicated [59]. It was also found that the concentration of magnesium needed to be above 5 mM for optimal synthesis of (p)ppGpp. Previous work has indicated that the 70S ribosome is needed for ReIA to produce (p)ppGpp in vitro [60]; however, we found this not to be the case. There was no difference observed with 5 mM MgCl\({}_{2}\) with and without 70S (Figure 2); therefore, it was not used in the in vitro reactions.  
In the case of the in vitro assay, it was found that using \(\gamma\)-\({}^{32}\)P-ATP with 5 mM Mg\({}^{2+}\) and no 70S ribosome was optimal to study the production of both ppGpp and ppGpp. In the case of the in vivo studies, [\({}^{32}\)P]-orthophosphate was used as the radiation source, and the cells then incorporated the \({}^{32}\)P into (p)ppGpp. Both methods required TLC with a stationary phase of a polyethyleneimine (PEI)-cellulose plate and a mobile phase of 1 M potassium phosphate monobasic.