esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Work of Breathing Estimation
\[\text{J}=\ \frac{1}{\text{N}}\sum_{\text{i=1}}^{\text{N}}|\text{WOB}_{\text{r}}- \text{WOB}_{\text{e}}| \tag{7}\]  
where N is the number of samples in each group, WOBr is the real work of breathing represented by Equation (3) and WOBe is the estimated work of breathing from muscular activity, using the proposed Equation (6). In the optimization algorithm, two nonlinear constraints were proposed to find parameters with a physical meaning. The first constraint, Equation (8), looks for values of \(\alpha_{1}\) and \(\alpha_{2}\) that express the contribution of the diaphragm and accessory muscles, the sum of \(\alpha_{1}\) and \(\alpha_{2}\) must be equal to the proportion between the average work of breathing (\(\overline{\text{WOB}_{\text{G}}}\)) and the average maximum amplitude of sEMG signals of all muscles (\(\overline{\text{MaxAmp}_{\text{G}}}\)) for each group (G). \[ \begin{array}{c}\alpha_{\text{I}_{\text{G}}}+\alpha_{\text{Z}_{\text{G}}}= \ \beta_{\text{G}};\text{ where }\text{G}=1,\ 2,\ 3\\ \beta_{\text{G}}=\ \frac{\overline{\text{WOB}_{\text{G}}}}{\overline{\text{MaxAmp}_{ \text{G}}}}\end{array}  \tag{8}\]  
The second constraint, Equation (9), ensures that the highest coefficient is equivalent to the highest muscular activity. For example, if the maximum diaphragm amplitude is higher than the other two maximum amplitudes (intercostal and sternocleidomastoid), \(\alpha_{1}\) will be higher than \(\alpha_{2}\). In addition, if any of the maximum amplitudes of accessory muscles is higher than the maximum diaphragm amplitude, \(\alpha_{2}\) will be higher than \(\alpha_{1}\). \[ \begin{array}{c}\text{MaxAmpDiag}_{\text{G}}\text{> MaxAmpInt}_{\text{G}}\ \&\ \ \text{MaxAmpDiag}_{\text{G}}\text{> MaxAmpStrn}_{\text{G}}\ \implies\ \alpha_{\text{I}_{\text{G}}}\text{> }\alpha_{\text{Z}_{\text{G}}}\\ \text{MaxAmpInt}_{\text{G}}\text{> MaxAmpDiag}_{\text{G}}\ \|\ \text{MaxAmpStrn}_{\text{G}}\text{> MaxAmpDiag}_{\text{G}}\ \implies\ \alpha_{\text{Z}_{\text{G}}}\text{> }\alpha_{\text{I}_{\text{G}}}\end{array}  \tag{9}\]  
The model implementation and the parameter optimization were performed for each data set obtained from the Experiments 1 and 2. The validation technique was mainly K-fold 20%.
## 2 Review of Related Literature and Applications
### Review of Existing Market Solutions for Controlling Smart Device Use
#### 2.3.1. Parent-Centred Device Management Apps  
This category includes tools such as Google Family Link (Google LLC, Mountain View, CA, USA, reviewed in November 2024) [43], Apple Screen Time (Apple Inc., Cupertino, CA, USA, reviewed in November 2024) [44], SK Telecom ZEM (SK Telecom Co., Ltd., Seoul, South Korea, reviewed in November 2024) [45], LG U+ Child Protection (LG Uplus Corp., Seoul, South Korea, reviewed in November 2024) [46], Kidslov (Kidslov Ltd., Manchester, UK, reviewed in November 2024) [47], and MobileFence (RSUPPORT Co., Ltd., Seoul, South Korea, reviewed in November 2024) [48]. These applications allow parents to set screen time limits, block content, monitor usage, and track location. By offering a high degree of control, they prove particularly useful in households with multiple children or limited capacity for direct supervision.  
Despite their practicality, however, these systems rely heavily on caregiver-imposed rules. Young children typically act as passive recipients of preset restrictions, with little involvement in understanding or negotiating the restrictions placed upon them. Consequently, opportunities to foster reflective self-regulation remain minimal. Moreover, the sustained effectiveness of these applications often depends on consistent parental oversight--making them less viable for families with limited time or digital proficiency. Consequently, while these tools can be helpful in managing digital behaviour, they may not sufficiently address the developmental needs of preschool-aged users.
## 5 Conclusions  
This article introduces a new BSHS-EODL technique for secure medical image transmission and analysis in the IoMT environment. The presented BSHS-EODL technique enables BC assisted secured image transmission and diagnosis models for the IoMT platform. The proposed method includes data classification, data collection, and image encryption. At the initial stage, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Subsequently, image encryption is applied to encrypt the data and the key generation process is performed via the DOA. Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, BO based parameter tuning, and VELM. A comprehensive set of simulation analyses on medical datasets highlights the supremacy of the BSHS-EODL technique. In upcoming years, the performance of the proposed method can be tested on other disease diagnoses.  
Conceptualization, A.A.; Methodology, A.A.; Software, Y.M.A.; Validation, Y.M.A.; Formal analysis, Y.M.A.; Writing--original draft, A.A.; Writing--review & editing, Y.M.A.; Visualization, A.A.; Funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research work is funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia by the grant number RUP3-1.  
This article does not contain any studies with human participants performed by any of the authors.  
Data Availability StatementData sharing is not applicable to this article as no datasets were generated during the current study.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number RUP3-1.  
The authors declare that they have no conflicts of interest.
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
###### Abstract  
In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a line segment extraction algorithm with adaptive threshold value is proposed. By constructing the adjacent matrix of the line segment and judging the direction of the line segment, it can decide whether to merge or eliminate other line segments. At the same time, geometric constraint line feature matching is considered to improve the efficiency of processing line features. Compared with the traditional algorithm, the processing efficiency of our proposed method is greatly improved. Then, point, line, and inertial data are effectively fused in a sliding window to achieve high-accuracy pose estimation. Finally, experiments on the EuRoC dataset show that the proposed PLI-VINS performs better than the traditional visual inertial SLAM system using point features and point line features.  
visual inertial SLAM; indoor environment; point and line feature; nonlinear optimization 2022  
[PERSON], [PERSON], [PERSON], [PERSON], Z. PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment. _Sensors_**2022**, 22, 5457. [[https://doi.org/10.3390/s22145457](https://doi.org/10.3390/s22145457)]([https://doi.org/10.3390/s22145457](https://doi.org/10.3390/s22145457))
## 5 Discussion
### Difference Between Proposed Approach and Long Short-Term Memory Networks  
A Long Short-Term Memory (LSTM) network is a special type of Recurrent Neural Network (RNN) and has been widely used to analyze time series data due to their ability to capture long-term dependencies. Despite the abundant successful applications in other time series scenarios like voice recognition, machine translation, RNNs have also achieved well performance in the remote sensing community, for example, land cover classification [(66; 67; 68)], crop identification [(69)], change detection [(70)]. Since we have already reconstructed the original time series of three VIs so as to obtain three stable full-year time series in the proposed approach, which also makes it possible to apply an LSTM model to identify vineyards based on these three reconstructed time series. Therefore, an LSTM model, the structure of which was summarized in Table 5, was established to test the difference between itself and the proposed approach.  
The construction environment, deep learning framework, and data used for training/testing of the LSTM model are consistent with the CNN approach. Note that three full-year time series vegetation indices were simultaneously used, so, the input shape of the LSTM was \(356\times 3\) (i.e., \(365\) days \(\times\) 3 VIs), and only the last neural of LSTM returned a sequence to serve as the input of last dense layer; it only has one output neural and was designed to do binary classification (i.e., Vineyard or Non-Vineyard).  
The LSTM model achieved an overall accuracy of 85.28%, and the detail of its identification map is illustrated in Figure 10. LSTM has a similar performance in terms of OA with respect to the approach of CWT+CNN, however, the fact that the user's accuracy (51.24%) is lower than that of the CNN approach (60.72%) indicated that LSTM has misclassified more non-vineyard pixels into the vineyard class, which is consistent with the final distribution map (Figure 4b, Figure 10). In general, the performance of LSTM is slightly inferior to the CWT+CNN but much better than the RF model. It demonstrated that both CW+CNN and LSTM had captured time-dependent features of long time series data. Regarding the difference in identification accuracy between CWT+CNN and LSTM, the potential reasons may include two aspects: (i) CWT and LSTM have totally different principles in extracting temporal information in long time series data; (ii) CNN and LSTM have different structures and different numbers of trainable parameters, but there is no doubt that both methods captured and made full use of the time-dependent information in the long time series data in our vineyard identification research.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences
#### 4.1.3 Almost Perfect Autocorrelation Sequences  
Binary APASs are PRBSs whose non-oversampled PACFs are nearly ideal in the absence of Doppler shifts. In this article, APASs based on p-ary m-sequences [30] are considered [31]. Such APASs have a strong main lobe at \(n=0\) and null sidelobes, with the exception of an additional peak at \(n=N_{\text{PRBS}}/2\), having therefore usable PACF length \(n=N_{\text{PRBS,usable}}=N_{\text{PRBS}}/2-1\). Additionally, the considered APASs have length \(N_{\text{PRBS}}\) that is a multiple of 4 and satisfies the constraint of \(N_{\text{PRBS}}/2-1\) being a prime power [17; 32].
## 2 Related Work
### Nature-Based Algorithms for DSM
The proposed technique achieved better energy efficiency while the user comfort was heavily sacrificed because of the missing time, and the other limitation of this proposed technique is that it uses only one type of HVAC equipment for evaluating its proposed proposal. Information transmission in underwater wireless sensors is majorly disturbed by the energy holes. A localization-free interference and energy hole minimization routing protocol has been proposed in [38]. Simulation results of the proposed technique has shown the better performance in the number of packets received to the final destination. In order to increase the efficiency of demand response in HEMS, different types of research work are going on from a traditional method of control to a more efficient feedback controller. In [39], the occupancy of the user has been used to review Model Predictive Control (MPC) for HVAC buildings. A limitation of this approach is its high computational cost and complexity of designing a particular system. A hybrid technique using two optimization approaches of Bacterial Foraging Algorithm (BFA) and genetic algorithm [40] with the objective of reducing cost and PAR has been proposed. PAR is an important performance metric used to evaluate the effect of peak electricity consumption on the system. There are a variety of appliances that require either high voltage or low voltage. Demand-side Management (DSM) persuades the electricity consumer to shift the high voltage required appliances to the low peak hours so that less energy is consumed during peak hours. However, most of the schedulers often result in the increase of PAR as they increase the load during low peak hours. The proposed algorithm used a fitness function of cost reduction, but it also minimizes PAR using day ahead RTP. In [41], hybrid meta-heuristic techniques for scheduling of appliances have been proposed. The main point of designing a hybrid technique is a balance of exploration and exploitation. In order to achieve this balance hybrid of Optimal Stopping Rule (OSR) with Genetic Algorithm (GA), Teaching Learning Based Optimization (TLBO) and a Firefly Algorithm (FA) have been proposed. The objectives of this research were to reduce energy consumption and minimizing waiting time along with peak-to-average ratio and cost as optimized parameters. MATLAB (R2013a, MathWorks, Natick, MA, USA) simulation has been conducted while considering user priorities and RTP for heterogeneous homes. A limitation of the proposed methodology is the use of a limited number of appliances. This proposed scheme has not considered the effect of HVAC, which is the major energy consumer in the residential sector.
## 1 Introduction
This data set was used in the Brain Tumor classification (BraTS) 2021 challenge and includes information on the Weighted signal T1 (T1w), which provides anatomical details of brain tissue and facilitates the differentiation between grey and white matter. In addition, Kaggle provides a subset of 87 patients without associated diagnoses, which is employed as a test set for evaluating the developed model. The predictions generated via the model are submitted to Kaggle, which quantitatively assesses their classification performance by computing the area under the receiver operating characteristic curve (AUC-ROC). Subsequently, the evaluation results are returned to facilitate further analysis and the refinement of the model. This procedure privately evaluates the generalisation of the model using this specific data set [22]. Likewise, the Erasmus Glioma Database (EGD), curated by the Erasmus Medical Center (University Medical Center Rotterdam), provides a collection of 774 patientsdiagnosed with glioma, including T1w imaging data and demographic information, such as age and sex [23]. The EGD data set is used primarily for the development and validation of machine learning algorithms aimed at the classification, segmentation, and prognostic assessment of gliomas from grades I to IV. Each data set plays a role in advancing medical imaging research and enhancing clinical decision-support systems. Machine learning algorithms can inadvertently learn and replicate patterns from training data that reflect demographic imbalances, such as disparities related to age or sex [24]. To examine whether such biases are present, demographic fairness is evaluated using statistical techniques, including the Shapiro-Wilk test for assessing normality, the Mann-Whitney U test for comparing age subgroups, and the Chi-squared test for associations based on sex [25]. By incorporating power analysis and confidence interval estimation, the analysis also provides insights into the robustness of performance metrics and supports a methodological framework for assessing demographic invariance [26]. In this study, a CNN-based model was developed using a data set provided by RSNA and MICCAI. This model was trained with the objective of comparing its performance with that of other researchers who also used this database. Subsequently, inference was conducted using the EGD database, demonstrating the ability of the model to generalise in detecting other types of gliomas using F1-score, accuracy, and a false negative rate. Furthermore, a statistical analysis was performed on the demographic information available in the new data set to investigate whether the model exhibited significant bias as a function of patient age or sex.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices
### Saturation of Measurement of Reflectance Indices  
The saturation that decreases the sensitivity of RIs to plant characteristics at high values of vegetation density is an important problem of the measurement of reflectance indices, especially RIs based on the NIR reflected light (e.g., vegetation indices). The non-linear changes in the NIR/red ratios that are caused by the high plant density, structural properties, and changes in the chlorophyll concentration [158; 159; 10] are some reasons for this saturation. This problem can be solved by using specific weights for values of the reflected light intensity at an NIR range to correct vegetation indices [69; 159]. Consideringthis point, the WDRVI can be used as a corrected analogue to the NDVI; the MCARI corresponds to CARI [10; 69]; there are other indices with these corrections.
## 6 Simulation Result and Analysis
### Experiments Establishment  
#### 6.1.1 Scenario Parameters  
In this paper, a multibeam LEO satellite is used to simulate the experimental environment of S-IoT, whose detailed parameters are listed in Table 1. The limited backhaul transmission power of the satellite limits the IoT terminals that the system can support.  
Furthermore, in the simulation environment, 10,000 IoT terminals are unevenly distributed in the total beams of a single satellite, whose request arrival follows the Poisson distribution with \(\lambda\) times per hour.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Satellite altitude & 550 km \\ Beams & 19 \\ Data transmission channels & 8 \\ Channel bandwidth & 10\(\,\)kHz \\ Frequency band & 14\(\,\)GHz \\ Terminals’ antenna max power & 300\(\,\)mW \\ Terminals’ antenna power level & 3 \\ Terminals’ antenna gain & 10\(\,\)diBi \\ Satellite receiving antenna G/T & 3.7\(\,\)db \\ Path loss & 170.38\(\,\)db \\ Satellite backhaul power limitation & 300\(\,\)W \\ Beam power limitation & 25\(\,\)W \\ Satellite amplifier magnification & 5 \\ _SINR_ threshold & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters of S-IoT scenario.
## 3 Results
### Chemical Characterization
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**VOCs**} & \multicolumn{1}{c}{**Days**} & \multicolumn{3}{c}{_Ultra Lactuca_} \\  & & **CTRL** & _MAP_ & _VAC_ \\ \hline _2,4-Decadienal (isomer)_ & Day 0 & 27.565 \(\pm\) 0.082 & — & — \\ CAS\# 25152-84-5 (7) & Day 3 & 9.401 \(\pm\) 0.596 & 8.748 \(\pm\) 0.206 & 10.866 \(\pm\) 1.603 \\  & Day 6 & 2.166 \(\pm\) 0.003 & 9.163 \(\pm\) 0.299 & 9.387 \(\pm\) 1.902 \\  & Day 9 & 0.695 \(\pm\) 0.105 & 9.796 \(\pm\) 0.081 & 2.521 \(\pm\) 0.002 \\ Threshold: 0.00013 mg/kg & Day 12 & 0.506 \(\pm\) 0.021 & 8.731 \(\pm\) 0.827 & 6.464 \(\pm\) 0.024 \\  & Day 15 & 0.810 \(\pm\) 0.163 & 4.250 \(\pm\) 0.712 & 6.128 \(\pm\) 0.142 \\ \hline _2,4-Decadienal, (E,E)-_ & Day 0 & 12.425 \(\pm\) 0.412 & — & — \\ CAS\# 2363-88-4 & Day 3 & 4.018 \(\pm\) 0.248 & 8.110 \(\pm\) 1.208 & 6.853 \(\pm\) 0.463 \\  & Day 6 & 1.775 \(\pm\) 0.022 & 12.590 \(\pm\) 0.687 & 8.237 \(\pm\) 0.381 \\  & Day 9 & 0.701 \(\pm\) 0.118 & 12.584 \(\pm\) 0.104 & 5.377 \(\pm\) 0.004 \\  & Day 12 & 0.950 \(\pm\) 0.027 & 20.548 \(\pm\) 1.950 & 10.449 \(\pm\) 0.038 \\ Threshold: 0.0003 mg/kg & Day 15 & 1.210 \(\pm\) 0.253 & 12.312 \(\pm\) 1.823 & 11.744 \(\pm\) 1.109 \\ \hline  & Day 0 & 0.133 \(\pm\) 0.025 & — & — \\ _1-Hexanol_ & Day 3 & 6.025 \(\pm\) 0.050 & 0.856 \(\pm\) 0.053 & 0.504 \(\pm\) 0.029 \\ CAS\# 111-27-3 & Day 6 & 14.377 \(\pm\) 1.316 & 1.074 \(\pm\) 0.070 & 0.611 \(\pm\) 0.011 \\  & Day 9 & 17.684 \(\pm\) 0.261 & 0.443 \(\pm\) 0.004 & 0.879 \(\pm\) 0.001 \\ Threshold: 1.21 mg/kg & Day 12 & 24.474 \(\pm\) 0.584 & 1.102 \(\pm\) 0.103 & 0.850 \(\pm\) 0.003 \\  & Day 15 & 4.670 \(\pm\) 0.477 & 0.828 \(\pm\) 0.030 & 1.594 \(\pm\) 0.176 \\ \hline _Benzaldehyde_ & Day 0 & 2.287 \(\pm\) 0.027 & — & — \\ CAS\# 100-52-7 & Day 3 & 2.939 \(\pm\) 0.571 & 4.004 \(\pm\) 0.207 & 5.146 \(\pm\) 0.094 \\  & Day 6 & 5.586 \(\pm\) 2.270 & 4.136 \(\pm\) 0.325 & 3.371 \(\pm\) 1.052 \\  & Day 9 & 3.804 \(\pm\) 0.431 & 8.441 \(\pm\) 0.070 & 15.221 \(\pm\) 0.010 \\  & Day 12 & 6.023 \(\pm\) 1.384 & 11.037 \(\pm\) 0.812 & 17.494 \(\pm\) 0.064 \\  & Day 15 & 9.642 \(\pm\) 0.367 & 33.800 \(\pm\) 9.784 & 11.8 \(\pm\) 2.147 \\ \hline \hline \end{tabular} Notes: The results are expressed as the percentage (%) of the respective peak area relative to the total peak area. Odor thresholds in water were calculated using the median for the values compiled by [PERSON] (1995).  
\end{table} 
Table 2: Main VOCs responsible for the variation in _U. lactuca_ samples according to the PCA. day 9 and to subsequently decrease, albeit remaining relatively above the initial value of 6.000%.
## 5 Conclusions  
Data security is an important basic condition for financial institutions to build a data-based ecology, and how to accurately identify massive data for classification and grading control has become a key issue. Under the overall framework of financial sensitive data classification and grading research work, we propose a self-enlarging and broadening financial sensitive data classification and grading method (UP-SDCG), which combines the traditional recognition technology with NLP technology, effectively solves the problem of low accuracy rate of the traditional recognition technology. The experimental results show that it has a significant advantage of effect compared with other publicly available platform algorithms, and also has been validated in real financial institutions. The results have also been verified in real financial institution business scenarios. Compared to existing classification and grading frameworks, our approach offers a finer granularity, enabling more precise implementation of protective measures tailored to various data types and levels, which significantly mitigates the risk of high-sensitivity data leakage. Our subsequent work will focus on the research of Unstructured Financial Data Classification and Graded Recognition (UP-UDCG), mainly realizing the two major functions of data classification and grading based on text classification and data classification and grading based on entities, and essential research methodology can be referred to in Appendix A. By deploying the sensitive data classification algorithm at the regulatory outposts, we aspire for our work to contribute to enhancing data security in open banking.  
Figure 13: Scatter plots of pairwise variables.  
**Author Contributions:** Conceptualization, L.Z. ([PERSON]); Methodology, L.Z. ([PERSON]), W.Q. and H.L.; Software, W.Q.; Validation, H.L. and X.M.; Resources, L.Z. ([PERSON]) and X.M.; Data curation, W.Q.; Writing--original draft, L.Z. ([PERSON]), W.Q. and H.L.; Writing--review & editing, Z.L.; Supervision, Z.L., J.Y. and L.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Key Research and Development Program of China (Grant 2021 YFC3300600).  
**Data Availability Statement:** The data can be shared up on request. The data are not publicly available due to the sensitivity and confidentiality of financial industry data.  
**Conflicts of Interest:** Author [PERSON] was employed by Unionpay Co., Ltd. Her position is engineer. Author [PERSON] employed by Huawei Technologies Co., Ltd. His position is senior engineer. Author [PERSON] was employed by Huawei Technologies Co., Ltd. Her position is engineer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.