esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parameter Effects on VFA-SF and VFA-SF-OPT
### Effect of Node Velocity
Therefore, 0.4 is a good indication to evaluate the variation of convergence time for different values of \(v_{max}\). When \(v_{max}<0.5\), convergence time dramatically decreases from 1750 to 500 time steps as \(v_{max}\) increases. When \(v_{max}>0.5\), only a few sensor nodes exceed the \(v_{max}\) limit. It has ignorable impact on the convergence rate of the whole deployment process. Figure 2: Pair correlation diversion as a time-step function for the indicated maximum node velocity \(v_{max}=0.05,0.1,0.2,0.4,0.6,1.0\), and 2.0, respectively. Moreover, statistical results of PCD values evaluated from the final network topology by the VFA-SF with \(v_{max}=0.05,0.1,0.2,0.6,1.0\), and \(2.0\) are listed in Table 2. Fifty independent cases with different initial node distributions were randomly chosen for statistical analysis. Figure 4 also presents pie charts with percentages of the final PCD value for different values of \(v_{max}\). When \(v_{max}<=0.1\), there was only 10-12% possibility with final PCD values of 0-0.05, which corresponds to a perfect hexagonal topology. The PCD value was calculated in the most central area of the target region, but was only lacking at the edge of the sensor network to some extent. This means that a smaller \(v_{max}\) causes worse node deployment in WSN applications, which is consistent with Figure 1. When \(0.2<v_{max}<2.0\), the possibility of the PCD value of 0-0.1 is stable at 42-50% (the white values in the pie charts of Figure 3), which corresponds to the perfect hexagonal network. A larger \(v_{max}\) helps to improve the final network deployment but does not increase the possibility of perfect hexagonal topology. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PCD Value** & \(v_{max}\) **= 0.05** & \(v_{max}\) **= 0.1** & \(v_{max}\) **= 0.2** & \(v_{max}\) **= 0.6** & \(v_{max}\) **= 1.0** & \(v_{max}\) **= 2.0** \\ \hline
0–0.05 & 5 & 6 & 23 & 19 & 24 & 22 \\
0.05–0.10 & 0 & 0 & 2 & 2 & 0 & 0 \\
0.10–0.15 & 3 & 4 & 4 & 2 & 3 & 2 \\
0.15–0.20 & 4 & 1 & 4 & 4 & 2 & 2 \\
0.20–0.25 & 3 & 7 & 5 & 3 & 3 & 9 \\
0.25–0.30 & 6 & 7 & 4 & 5 & 5 & 3 \\
0.30–0.35 & 4 & 9 & 4 & 5 & 4 & 4 \\
0.35–0.40 & 9 & 9 & 1 & 4 & 7 & 5 \\
0.40–0.45 & 12 & 4 & 3 & 5 & 2 & 1 \\ \(>\)0.45 & 4 & 3 & 0 & 1 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of statistical cases of the PCD value evaluated from the final node deployment by the VFA-SF for different values of \(v_{max}\). Figure 3: Variation of convergence time as a function of \(v_{max}\) for the case of \(\text{PCD}=0.4\).
## 5 Conclusions  
In this study, we proposed and developed an efficient deep learning-based method for automated digit recognition and measurement-type classification (systolic, diastolic, and pulse) from images of blood pressure monitors. The core of the system is a lightweight YOLOv8s model optimized using post-training quantization (INT8 precision) to enable deployment on edge devices. The model achieved a high digit detection accuracy of 99.28%, with an inference time of 661.4 ms on a Google Colab CPU and 1867.6 ms on a mid-range mobile phone while maintaining a compact model size of only 11 MB. The proposed model outperformed the Real-Time DEtection TRansformer (RT-DETR) across multiple metrics, including accuracy, F1-score, model size, and inference time.  
The proposed model was further evaluated through integration into a mobile health (mHealth) application, demonstrating practical performance with 96.67% accuracy on a set of 40 test samples. These results confirm the model's robustness and feasibility for real-world use, especially in scenarios where manual data entry or Bluetooth-enabled devices are impractical or unavailable. Overall, the proposed deep learning approach offers a scalable, accurate, and resource-efficient solution for automated digit recognition for blood pressure monitoring.  
Conceptualization, N.A. and R.M.; methodology, N.A. and H.V.T.; software, H.V.T.; validation, N.A., H.V.T. and R.M.; formal analysis, N.A. and H.V.T.; investigation, H.V.T.; resources, N.A.; data curation, H.V.T.; writing--original draft preparation, N.A. and H.V.T.; writing--review and editing, N.A., H.V.T. and R.M.; visualization, N.A. and H.V.T.; supervision, N.A. and R.M.; project administration, N.A.; funding acquisition, N.A. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Hibah Riset Unggulan Pusat dan Penellitian (RU3P) 2024 (Contract No. 766.20/ITI.B07.5/TA.00/2024) managed by The Institute for Science and Technology Development (LPTI), Institut Teknologi Bandung.  
The original data presented in the study are openly available in Roboflow Universe at [[https://universe.roboflow.com](https://universe.roboflow.com)]([https://universe.roboflow.com](https://universe.roboflow.com)) (accessed on 17 June 2025) and [[https://cameralab.eng.ox.ac.uk](https://cameralab.eng.ox.ac.uk)]([https://cameralab.eng.ox.ac.uk](https://cameralab.eng.ox.ac.uk)) (accessed on 17 June 2025).  
Figure 10: Sample of the test results for the mHealth application on several cases: (**a**) digit color and lighting conditions, (**b**) monitor layout, (**c**) capture angle, (**d**) capture distance, and (**e**) blurred conditions. The upper panel represent the input images whereas the middle panel represent the output images predicted using the proposed deep learning model.
## 4 Simulation Results and Discussions
### Single-Objective OPF Problem
#### 4.1.2 IEEE 118-Bus System
Figure 3: Convergence curves of MaMPA for: (**a**) case 5; (**b**) case 6; (**c**) case 7; (**d**) case 8. Hence, for the SOOPF problems, MaMPA could obtain superior solutions than various other algorithms for all considered objective functions in each test system, especially the large system, with a fast computational time. MaMPA could also quickly converge on the feasible solutions for all considered objectives in each test system.
## 3 Methodology
### Network Architectures
#### 3.2.2 Feature Extraction Module  
The Feature Extraction Module (FEM) serves as the foundational stage of the proposed framework, transforming multi-view sequential input images into a structured set of spatial feature maps by leveraging MobileNetV3. Then, the extracted features from this module are subsequently fed into the structural attention modules for further processing.  
The input to the FEM is a sequence of 2D view images, denoted as \(\mathcal{I}=\{I_{1},I_{2},\ldots,I_{S}\}\), where \(I_{t}\in\Re^{H\times W\times 3}\) represents the t view captured from the 3D object, \(S\) is the total number of views, and \(H\times W\) are the height and width of the input images, equal to 224 in our work. These views are captured at regular intervals along a predefined trajectory around the 3D object, ensuring diverse perspectives.  
Each input image \(I_{t}\) is passed through the MobileNetV3 backbone to extract spatial feature maps \(\mathcal{F}_{t}\in\mathbb{R}^{C\times N\times N}\). Here, \(C\) is the number of output channels representing distinct feature filters, and N is the spatial dimensions of the feature map, both of which depend on the output layer of the basic feature extraction network. For a complete continuous viewing process, the temporal features maps can be expressed as \(\mathcal{F}\in\mathbb{R}^{S\times C\times N\times N}\).  
In this study, in fixing the parameters of MobileNetV3, every viewpoint or temporal frame is processed with identical weights, ensuring consistent feature extraction. This eliminates variability introduced by parameter updates during training, which is particularly important for tasks involving multi-view or temporal data. Consistent features enable better alignment and fusion of information across different perspectives, reducing noise and improving global pattern modeling. In addition, the alignment and concatenation of these features ensure consistency across views, enabling the model to capture both local and global dependencies in dynamic 3D object recognition tasks.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 3 Discussion
### Ethylene Production Results  
The plant hormone ethylene plays a key role in climacteric fruit ripening. Studies of ethylene signaling components have revealed a linear transduction pathway leading to the activation of ethylene response factors [35]. In NR fruit, ethylene is produced in the last fruit developmental stages, beginning approximately at 80 DASF (S4) [8], participating directly in fruit ripening, and it is biosynthesized by the action of three enzymes, S-adenosylmethionine (SAM) synthase, 1-aminocyclopropane-1-carboxylate synthase (ACS) and 1-aminocyclopropane-1-carboxylate oxidase (ACO). The last two enzymes (ACS and ACO) are critical to ethylene biosynthesis [36]. The normal expression patterns of these genes are shown in Figure 3 (blue section), where _ACS_ displayed only one expression peak in T7, and _ACO_ displayed two expression peaks in T2 and T7, respectively. By comparing the expression patterns of _ACS_ and _ACO_ with ethylene production (only observed in T7), _ACS_ seems to be the limiting enzyme in ethylene production. Similar results were reported when comparing the ACS and ACO enzyme activity with the fruits' 1-aminocyclopropane-1-carboxylate (ACC) content and ethylene production [8].  
No ethylene production was identified in SR individuals [13]. This observation was validated in the transcriptomic analysis, where no _ACS_ RNA accumulation was found in T7; this enzyme uses ado-met to produce ACC in normal conditions (Figure 3, blue section). These results suggest a lower accumulation of ACC in slow ripening individuals as there is no expression of the _ACS_ gene. On the other hand, the _ACO_ transcript accumulation was analyzed; this enzyme participated in converting ACC to ethylene [37]. In normal conditions, this enzyme has two peaks of expression in T2 and T7, but in SR individuals, transcript accumulation was found in only T2.  
In summary, the slow ripening phenotype displayed no _ACS_ and _ACO_ transcript accumulation in T7 (ripening period), and a lower ACC accumulation in the slow ripening siblings is suggested. These backgrounds could explain why no ethylene production was found in the slow ripening siblings during the ripening stage. Although we suspect an indirect correlation between the absence of _NAC072_ and _ACS_ transcript accumulation, the mechanism by which this regulation is carried out is not yet clear. Another phenotype of peach tree similar to the slow ripening phenotype that also does not produce ethylene is stony hard; a candidate gene for this phenotype is _YUC11_ which was described as regulating the synthesis of auxins and ethylene, having the same expression pattern as _ACS1_ (both genes are absent in the stony hard phenotype) [38]. However, unlike slow ripening fruits, fruits with the stony hard phenotype respond to exogenous applications of ethylene (data not shown), suggesting that slow ripening is independent of the stony hard phenotype.
###### Abstract  
Hexavalent chromium contamination in groundwater has become a very serious and challenging problem. Identification of the groundwater chemical characteristics of the sites and their control mechanisms for remediation of pollutants is a significant challenge. In this study, a contaminated site in Xinxiang City, Henan Province, was investigated and 92 groundwater samples were collected from the site. Furthermore, the hydrogeochemical characteristics and the distribution patterns of components in the groundwater were analyzed by a combination of multivariate statistical analysis, Piper diagram, Gibbs diagram, ions ratio and hydrogeochemical simulation. The results showed that the HCO\({}_{3}\)-Cl-Mg-Ca type, SO\({}_{4}\)-HCO\({}_{3}\)-Na type, and HCO\({}_{3}\)-Mg-Ca-Na type characterize the hydrogeochemical composition of shallow groundwater and HCO\({}_{3}\)-Cl-Mg-Ca type, HCO\({}_{3}\)-Na-Mg type, and HCO\({}_{3}\)-SO\({}_{4}\)-Mg-Na-Ca type characterize the hydrogeochemical composition of deep groundwater. Ion ratios and saturation index indicated that the groundwater hydrogeochemical characteristics of the study area are mainly controlled by water-rock action and evaporative crystallization. The dissolution of halide, gypsum and anhydrite, the precipitation of aragonite, calcite and dolomite, and the precipitation of trivalent chromium minerals other than CrCl\({}_{3}\) and the dissolution of hexavalent chromium minerals occurred in groundwater at the site. The minimum value of pH in groundwater at the site is 7.55 and the maximum value is 9.26. The influence of pH on the fugacity state of minerals was further investigated. It was concluded that the saturation index of dolomite, calcite, aragonite and MgCr\({}_{2}\)O\({}_{4}\) increases with the increase of pH, indicating that these minerals are more prone to precipitation, and the saturation index of Na\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) and CrCl\({}_{3}\) decreases with the increase of pH, implying that Na\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) and CrCl\({}_{3}\) are more prone to dissolution. The saturation index of the remaining minerals is less affected by pH changes. The study can provide a scientific basis for groundwater remediation.  
groundwater; hydrochemistry; hexavalent chromium contamination; water chemistry simulation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Evaluation Indicators  
The value of the intersect over union (IOU) threshold is directly related to the output prediction frame. Generally, the larger the threshold, the higher the prediction accuracy [46]. The main evaluation indicators used in this paper are the mean average precision (mAP) for an IOU threshold of 0.5 (mAP\({}_{50}\)), mAP\({}_{75}\), parameter size (MB), and the number of frames per second (FPS). mAP is an important metric for gauging detection accuracy of the target detection model. It is obtained from a precision (\(P\))-recall (\(R\)) curve. The calculations for precision (\(P\)), recall (\(R\)), and mAP are as follows.  
\[P=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[R=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{mAP}=\frac{1}{K}\sum_{K=1}^{K}\text{AP}(P,R,K) \tag{9}\]  
where the true positives (TP) are the number of positive images that are actually detected as being positive. FP refers to the number of false positives, which is the number of negative samples misclassified as positive samples. The false negatives (FN) are the number of positive samples that are wrongly detected as being negative. The quantity of detection target categories is \(K\). \(K=1\) in this article. The average precision (AP) is the region enclosed  
Figure 11: (**a**) Activation function. (**b**) Derivative of the activation function.  
by the coordinate axis and the _P-R_ curve in a single-type detection, and mAP represents the average AP for every category.
## 2 Materials and Methods
### Spatial Autocorrelation Analysis
#### 2.9.3 Application of Spatial Autocorrelation  
A spatial autocorrelation approach was carried out to study the existing correspondences between the heat intensity and impervious surfaces, represented by built-up areas. The Bivariate Local Indicator of Spatial Autocorrelation (BiLISA) approach was implemented, aiming to identify any common relationship, bundling, and the importance of the cell-based clustering grid. The BiLISA approach reveals the degree to which the nature of association amongst every pair of variables varies throughout a specific location. [PERSON]'s index (_I_) was again utilized to evaluate the spatial autocorrelation performance, as well as to measure the spatial reliance within the pair of variables applied [64]. The eventual BiLISA output is usually portrayed in a map, which helps with identifying particular features of the driven autocorrelations, which are usually grouped into four ranks. Two ranks present the affirmative clusters (high with high, and low with low), in agreement with grid cells exactly bordered by other adjacent cells possessing typical values. The contrasting two ranks include outliers (high with low, and low with high), that generally correspond to grid cells whose other bordering cells incorporate diverse values. A GeoDa software program [63] that provides a remarkably accessible environment for spatial autocorrelation data utilization and assessment was used for the analysis.
## 3 GLRT-Based Fusion Rule
### One-Bit Quantization Schemes  
In our study, we consider a non-regular quantizer using a pair of symmetric thresholds \(\pm\tau_{n}\)\((\tau_{n}>0)\)[17] (p. 314) :  
\[b_{n}=Q(x_{n})=\begin{cases}0,&\text{if }|x_{n}|\leq\tau_{n},\\ 1,&\text{if }|x_{n}|>\tau_{n},\end{cases}\quad n=1,2,\ldots,N \tag{2}\]where \(x_{n}\) denotes the local observation at the \(n\)-th sensor and \(b_{n}\) the quantized binary digit. To facilitate our analysis, we describe \(x_{n}\) under \(H_{1}\) as:  
\[x_{n}=\sigma_{1}w_{n},\quad n=1,2,\ldots,N, \tag{3}\]  
where \(w_{n}\) denotes a random variable having the same distribution as \(x_{n}\), but with unit variance; \(\sigma_{1}\) is the unknown scale parameter to be estimated. Then, under hypothesis \(H_{i}\), \(i=0\) or 1, the probability mass function (PMF) of \(b_{n}\) is:  
\[P_{B,i}(b_{n};\sigma_{i})=\left[2-2F_{w}\left(\frac{\tau_{n}}{\sigma_{i}} \right)\right]^{b_{n}}\left[2F_{w}\left(\frac{\tau_{n}}{\sigma_{i}}\right)-1 \right]^{1-b_{n}} \tag{4}\]  
where \(F_{w}(\cdot)\) denotes the cumulative distribution function (CDF) of \(w_{n}\).
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 3 Element Design  
The design process of the low-RCS 1-bit RTA, based on the design concepts outlined in Section 2, can be divided into three main steps: AFST design, 1-bit RTA design, and array design. Each of these steps is crucial in achieving the desired low-RCS and high-gain and beam scanning radiation performance of the RTA.
## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Collection to Cloud: IoT Architecture for Smart Irrigation
#### 3.1.3 Data Quality and Preprocessing  
Maintaining high data quality is paramount for automated irrigation systems. The initial steps of data processing involve cleaning, preprocessing, and fusing data from various sources to ensure accuracy and reliability. Table 2 provides an overview of the  
Figure 3: Common data types, sources, and collection methods in automated irrigation systems.  
key techniques employed in these processes, highlighting their benefits and contributions to the overall system performance. By addressing challenges such as missing values, inconsistencies, outliers, normalization, and data fusion, these techniques improve data reliability, leading to more informed and accurate irrigation decisions.
## 6 Results and Discussions  
### The Inner Loops  
#### 6.1.1 Absolute Position Loop  
In Section 4, it was demonstrated that the performance of the absolute position loop depends on the characteristics of the environment much more than the others. The worst results were expected from this loop. This hypothesis was confirmed by the experiments.  
The output of the external loop corresponds to the reference position of the robot. In some cases, this position was not in contact with the environment, which provoked bouncing. High accelerations were reached. Sometimes, the robot had hard impacts against the piece, and the security measures were activated, causing an emergency stop.  
After several experiments, this type of inner loop was discarded. It was the worst of three.  
The plots resulting from these experiments have not been shown in the article. However, we included this method since we consider that our experience, albeit negative, may be useful to other people that work in force control.
## 2 Basic Theory
### Sparse Dictionary Learning
#### 2.2.1 Sparse Decomposition  
Sparse representation of a signal involves expressing it as a sum of a minimal number of basic elements, or atoms, from a dictionary [26]. Mathematically, this is formulated as:  
\[x=D\alpha+r \tag{9}\]  
where \(x\in R^{n}\) is the signal to be represented, \(D\in R^{n*m}\) is the dictionary composed of m atoms, \(\alpha\in R^{m}\) is the sparse coefficient vector with a few non-zero entries, and \(r\) is the residual of the representation.  
The objective is to identify the most sparse coefficient vector \(\alpha\) that minimizes the residual \(r\), thereby attaining a succinct representation of the signal \(x\). Orthogonal Matching Pursuit (OMP) is commonly utilized for this purpose, as it incrementally selects atoms from the dictionary \(D\) that exhibit the highest correlation with the signal, thereby progressively building the sparsest possible representation. OMP operates on a greedy algorithmic principle, whereby it iteratively chooses the dictionary atoms that have the greatest correlation with the current residual in the approximation of signal \(x\).
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
###### Abstract  
The latest MPEG standard, MPEG-H 3D Audio, employs the virtual loudspeaker rendering (VLR) technique to support virtual reality (VR) and augmented reality (AR). During the rendering, the binaural downmixing of channel signals often induces the so-called comb filter effect, an undesirable spectral artifact, due to the phase difference between the binaural filters. In this paper, we propose an efficient algorithm that can mitigate such spectral artifacts. The proposed algorithm performs spectral compensation in both the panning gain and downmix signal domains depending on the frequency range. In the low-frequency bands where a band has a wider bandwidth than the critical-frequency scale, panning gains are directly compensated. In the high-frequency bands, where a band has a narrower bandwidth than the critical-frequency scale, a signal compensation similar to the active downmix is performed. As a result, the proposed algorithm optimizes the performance and the complexity within MPEG-H 3 DA framework. By implementing the algorithm on MPEG-H 3 DA BR, we verify that the additional computation complexity is minor. We also show that the proposed algorithm improves the subjective quality of MPEG-H 3 DA BR significantly.  
MPEG-H 3D Audio; binaural rendering; amplitude panning; active downmix Article