esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Baselines  
This paper incorporates five comparative methods to evaluate the flame segmentation performance. There are three CNN-based methods (FCN, Unet, and Deeplab v3+) and two Transformer-based methods (SegFormer and GGMSeg). These semantic segmentation methods are migrated from the generic domain to the flame segmentation domain. The baseline methods are described as follows.  
* FCN (CVPR 2015) [27]: FCN (Fully Convolutional Network) is a CNN-based semantic segmentation model. It replaces fully connected layers with convolutional layers, allowing for end-to-end pixel-level classification across the entire image.
* Unet (MICCAI 2015) [28]: Unet is a segmentation method initially designed for medical images, employing an encoder-decoder architecture. It utilizes a skip connection to establish connections between the encoder and decoder layers, enabling the extraction of information from feature maps at different scales.
* Deeplab v3+ (ECCV 2018) [29]: Deeplab v3+ is an image semantic segmentation model that utilizes dilated convolution and multi-scale feature fusion. It employs dilated convolutions to expand the receptive field and incorporates the ASPP (Atrous Spatial Pyramid Pooling) module to fuse multi-scale features.
* SegFormer (NeuIPS 2021) [30]: SegFormer is a Transformer-based image segmentation model. It captures global information through a self-attention mechanism and handles features at different scales through hierarchical feature fusion.  
* GMMSeg (NeuIPS 2022) [31]: GMMSeg is an image semantic segmentation model that incorporates a Gaussian mixture model and adaptive clustering. It adopts a separate mixture of Gaussians to model the data distribution of each class in the feature space. The model is trained using the adaptive clustering algorithm.
## 3 Results
### E. coli Flushing Test
#### 3.2.2 E. coli Removal Efficiency in Large Column Experiments  
Figure 6 illustrates the _E. coli_ removal efficiencies of the sand and raw and activated hydrochar-amended columns during the 30 days of experiments with intermittent operation. Similar to the results from the small column experiments, only the activated hydrochar amendment was effective at _E. coli_ removal. The removal efficiency of the activated hydrochar-amended column was the highest (99.7%) on the first day, and declined gently until the last day of the experiment (78.9%). The sand-only and raw hydrochar-amended columns showed a comparable _E. coli_ removal performance, in the range of 11.4-57.2%. Table 2 summarizes the overall _E. coli_ removal efficiencies and the effect of the idle time during the large column experiments. Under all experimental conditions applied, the activated hydrochar-amended columns showed greater _E. coli_ removal performances than the other columns. During 30 days of operation, the sand columns with activated hydrochar supplements showed an average total _E. coli_ removal efficiency of 91.2%. In contrast, the sand-only and raw hydrochar-supplemented columns showed an average total _E. coli_ removal efficiency of only 24.4% and 36.5%, respectively. The effect of the idle time on the _E. coli_ removal was only observed in the sand-only columns; throughout the experimental period, the removal in the first PV was significantly greater than that of the second PV. The _E. coli_ removal efficiency in the second PV, which represents the direct removal of _E. coli_ when passing through the column media, was 17.2%. In contrast, the removal efficiencies in the first PV (stored in the sand media for 24 or 72 h and thus representing the effect of the idle time) were 52.1% and 66.9%, respectively. Throughout the experiments, the idle time had no clear effect on the _E. coli_ removal performance of both types of hydrochar amendments, except for the 72 h idle time applied to the raw hydrochar-amended columns. Such an extended idle time increased the _E. coli_ removal efficiency by 14.8%. The direct removal of _E. coli_ (without idle time) was similar, at \(\sim\)19% in the second PV in the sand-only and raw hydrochar-amended columns. This indicates a close correspondence with the results from the small column experiments.  
Figure 5: Breakthrough curves of _E. coli_ from small column experiments carried out at a flow rate of 1 mL (0.20 cm)/min.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems
### Solid Lipid Nanoparticles (SLNs)  
SLNs were pioneered by [PERSON] et al. [74] (Figure 1e). SLNs consist of solid lipids such as fatty acids and surfactants. The combination ratio of lipids and surfactants used in preparing SLNs affects the particle size, particle size distribution, long-term storage stability, drug loading and release rate. Various types of lipids that are used in formulating SLNs include fatty acids, steroids, waves, monoglycerides, diglycerides and triglycerides, and are present in a solid state at room temperature. Similarly, various surfactants can be employed based on their hydrophobic-lyophobic balance in SLN formulation. In SLNs, solid lipids are used in order to increase the control over release kinetics of encapsulated drugs and also enhance the stability of chemically sensitive lipophilic excipients [75]. Their advantages include low toxicity and high biocompatibility. However, they are not flexible due to the presence of solid lipids and cannot therefore incorporate high amount of a drug [76]. Some drugs which have been entrapped within SLNs for lung cancer therapy are paclitaxel [77], epirubicin [78] and docetaxel [79] (Table 1). Intravenous delivery of doxorubicin within SLNs was investigated and the pharmacokinetic studies showed that the SLNs exhibited a notably higher AUC, a lower clearance rate and a smaller volume of distribution compared to the commercial doxorubicin solution [80]. Ocular [81] and oral [82] delivery have also been studied for SLNs, showing positive results against the free respective drugs.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 7 Simulation Results
### Network Reconfiguration Effect  
In order to evaluate the performance and complexity of the proposed schemes on the real networks, we consider the topology shown in Figure 3. Three parameters are evaluated: (1) number of routing matrix elements; (2) maximum link utilization and (3) optimization time. To investigate the proposed schemes from the computational complexity perspective, the impact of forwarding table entries compression on the number of routing matrix elements is examined.  
Figure 6: Prediction effect on the link utilization.  
Figure 7: Prediction impact on throughput. Conventional algorithms incur congestion when there are big flows while the proposed algorithm exploits the prediction algorithms along with the resource re-allocation ability of (Software defined networks) SDNs to increase the network throughput.  
Figure 8 depicts the _number of routing matrix element_ versus the _number of active flows_ in the network. Based on this figure, it is shown that the number of routing matrix elements dramatically decreases due to forwarding table entries compression. As it can be seen, the effect of this compression method is multiplied by increasing the number of flows. This happens because increasing the number of active flows increases the probability of flow aggregation and consequently decreases the number of routing matrix elements.This behavior makes the proposed compression technique proper for practical environments.
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 5 Discussion
[35], the classification performance of four conventional supervised ML models was compared: Ordinal Logistic Regression (OLR), Elastic-net Ordinal Regression(ENOR), SVM, and Random Forest (RF). Here, the RF was more robust than other models when the variations in the size of training data were examined. [PERSON] et al. [32] assessed the risk factors associated with low vitamin D levels in a dataset consisting of 50 patients from the southern part of Bangladesh and evaluated their clinical and demographic data. This study utilized supervised ML algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-fold cross-validation strategy was implemented. The LR model yielded an RMSE of 4.83 (CI: 2.70, 6.76) and an MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieved better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and an MAE of 2.68 (CI: 1.83,3.52). Based on the previous results in this research, three ML models for clinical data analysis were selected: SVM, Random Forest, and Logistic Regression, and similar results were obtained in Figures 11-13, demonstrating that the RF algorithm achieved higher accuracy (87.73%) in classifying both classes concerning SVM (80.0%) and LR (70.70%). Likewise, according to the statistical data analysis focused on a local population of 440 individuals, levels of 25 (OH)-D--a key indicator of vitamin D status--were measured to develop predictive models on a web platform. This platform integrates a public database containing records of 25 (OH)-D levels. The results of the local populations revealed that 55% of the population had levels below 30 ng/mL, which are considered insufficient. The origin of the population is a significant factor when assessing vitamin D status, as Ecuador is located on the equator at a latitude of \(-\)0.95, where solar radiation and UV ray intensity are higher. One might expect the population to have adequate vitamin D levels; however, Ecuadorians often take measures to avoid sun exposure. Thus, the online web application \"VITA-D\" was designed based on the RF model, selected after employing ML training models using both private and public datasets as a class model predictor, which achieved better performance in terms of the accuracy and precision of classification. In the production phase, we successfully implemented the graphical interface through the front end and back end, allowing for the development of the web platform as a preclinical tool.
## 3 Knowledge-Guided Parallel Hybrid Local Search Algorithm
### Frequent Pattern Data Mining  
Data mining can enhance algorithmic efficiency by uncovering specific patterns that frequently occur in solution sets. The results of data mining are treated as knowledge.  
In this context, the proposed frequent pattern (FP)-based data mining method is employed [19]. The primary process can be summarized as follows:  
* Step 1: Data transformation. In AEOS, the execution time of a task depends on its preceding tasks, making the identification of patterns in adjacent task executions crucial. These adjacent executions are defined as frequent patterns and are considered knowledge. As illustrated in Figure 3, this transformation process involves acquiring task execution data and identifying frequent patterns.
* Step 2: Data mining. To obtain a deep insight into AEOS scheduling, we employ the FP-growth algorithm based on the Apriori principle. This algorithm efficiently explores frequent patterns by storing the dataset in a frequent pattern tree (FP-tree), as depicted in Figure 4. The process involves constructing a tree structure to store task data, facilitating efficient mining of frequent patterns. As the scale of tasks increases, the number of frequent patterns grows, resulting in increased computation time required for the FP-growth algorithm to generate results. Tasks are partitioned into smaller subsets (as shown in Figure 5), and the FP-growth algorithm is applied to each subset individually to speed up the search. This approach reduces computation time and enhances the accuracy of frequent pattern mining.
* Step 3: New solutions construction. Based on the selected frequent patterns. We construct new solutions and employ a roulette strategy to select high-quality solutions to guide the algorithm. Initially, a set of potential new task execution sequences is generated using knowledge extracted from frequent patterns. Subsequently, using the roulette strategy, high-quality solutions are chosen based on their performance metrics, guiding the optimization process of the algorithm. This step aims to improve the overall efficiency and effectiveness of the algorithm by leveraging known frequent patterns and high-quality solutions.
## 2 Materials and Methods
### In Vivo Activity  
#### 2.6.1 The Middle Cerebral Artery Ischemia (MCAO) Model  
Male SD rats were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. The animal studies were approved by the Institutional Animal Care and Use Committee of Capital Medical University, with the ethics number AEEI-2020-188. Humane care was provided to all animals, following the protocol and the Regulations on Laboratory Animal Welfare issued by the Chinese Ministry of Science and Technology.  
A selection of 6-8-week-old Sprague-Dawley (SD) male rats weighing 250 to 280 g were divided into five groups, with 12 rats in each group: sham group, model group (MCAO), edaravone group (3 mg/kg), LA-1 group (10 mg/kg), and loganin group (30 mg/kg). In all groups of rats, anesthesia was induced with 3% isoflurane and then maintained at 2.5% isoflurane during surgery. Briefly, a 1-2 cm incision was made along the midline of the rat's neck to expose the right carotid artery. Then, a nylon suture (A5-243650, Beijing Cinnotech Co. Ltd., Beijing, China) coated with silica gel was inserted through the external carotid artery (ECA) into the middle carotid artery (MCA), which was then occluded. In this instance, the monofilament was not inserted in the sham group. After 2 h, the nylon suture was removed to allow reperfusion, and different drugs were immediately administered to the rats via the tail vein. Additionally, the sham and model groups received an equivalent dose of distilled water, with a dosage of 2 mL/kg. Cerebral blood flow was measured using a laser speckle flow imaging system (RWD Life science Co., Ltd., RFLSI ZW, Shenzhen, China) at three distinct time points: before ischemia, 2 h after ischemia, and 22 h after reperfusion. Neurological function scores for the treatment groups were assessed blindly at 22 h post-MCAO using the ZelLonga scoring system, after which the anesthetized rats were quickly euthanized. The brain tissue was harvested and then sectioned into seven slices, with six slices subjected to 2,3,5-triphenyltetrazolzolium chloride (TTC) staining to calculate the brain infarct area, while the remaining slice was preserved for future use.
## 5 Experimental Verification and Analysis  
### Experimental Settings  
The proposed method is experimentally evaluated in this section. The experimental setup for the tests is shown in Figure 2. The setup consists of three lithium-ion battery cells connected in series, an electronic load, and a host computer. The tested lithium-ion battery cells are type 18,650, whose nominal capacity is 2200 mAh, nominal voltage is 3.7 V, charging and discharging cutoff voltages are 4.2 V and 3 V, respectively. The type of the electronic load is IT8516S produced by ITECH, whose current measurement accuracy is \(\pm(0.1\%+0.1\%\) full scale), and voltage measurement accuracy is \(\pm(0.02\%+0.02\%\) full scale). The load current, terminal voltage, and SOC can be recorded via the host computer during the discharge test.  
First, a sequence of pulsed discharging experiments were implemented to determine the relationship of open circuit voltage (OCV) and SOC. By using the curve-fitting toolbox in MATLAB, \(k_{0}\), \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), \(k_{5}\) were identified, which are shown in Table 1. The measured data and fitted curve are presented in Figure 3. The R-square is used to represent the goodness of fit. The normal value range of the R-square is 0-1 and a value closer to 1 indicates a better fitting curve [32]. It can be seen that the curve fits well with the measurement data, indicating that the selected fifth-order polynomial model can describe the relationship of OCV and SOC very well.  
Then, a constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test were performed. They are commonly used to verify the performances of SOC estimation methods in EVs. In the constant current discharge test, the current keeps invariant but the terminal voltage declines continuously. In the pulse current discharge test, the current stays at 1 A for 10 min and then decreases to 0 and lasts for 30 min. The process is repeated until the battery reaches the lower cut-off voltage. UDDS, also known as FTP72, was used as a test procedure to certify vehicle emissions by the US Environmental Protection Agency. It can simulate the actual driving conditions of vehicles on the road. The battery current and voltage are both sampled at 1 s. In each test, the true SOC was obtained using CC method. The estimation accuracy, convergence rate, and robustness of the proposed VB-ADEKF are evaluated by comparison with DEKF under different tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(k_{0}\) & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & \(k_{4}\) & \(k_{5}\) \\ \hline
9.50 & 14.01 & \(-\)62.64 & 137.40 & \(-\)133.40 & 47.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The identification results of \(k_{0}\), \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), \(k_{5}\).  
Figure 2: The experimental setup.
# Web Applications for Teaching the Respiratory System: Content Validation
## 2 Materials and Methods
### Application and Modules' Description
#### 2.1.1 Anatomy and Physiology Module  
The \"Anatomy and Physiology\" module has been developed to evaluate the respiratory response to changes in a patient's mechanics and breathing pattern. It comprises a ventilation section (see Figure 2) that, in turn, is divided into four sub-sections: (I) equation of motion, in which the respiratory system equation of air motion is presented, with a detailed description of each of the terms that compose it; (II) anatomy, which shows graphs referring to ventilatory mechanics, respiratory muscles, and the alveolar ventilatory model--each representation contains points with explanatory information; (III) ventilatory signals, which displays airflow, tidal volume, pleural and muscular pressure waveforms, and pressure-volume and airflow-volume loops in a breath cycle; and (IV) controls, which are the components that allow for modifcations to the variables related to a patient's effort and mechanical properties, e.g., muscle effort pressure (P\({}_{\max}\)), respiratory rate (RR), lung compliance (C), and airway resistance (R). From these controls, users can interact with the behavior of the ventilatory curves after making changes to every one of them, according to their relationship, through the equation of air motion (Equation (1)):  
\[P_{musc}(t)+P_{mw}(t)=R\dot{V}(t)+C^{-1}V_{\rm T}(t), \tag{1}\]  
where \(\dot{V}\) is the inspired airflow, and V\({}_{\rm T}\) represents the tidal volume. In this case, restrictive and obstructive pathologies can be simulated by changing the R and C values, so students can interactively learn about ventilatory behavior in these situations.
## 3 Instrument Validation
### DC Offset Compensation  
The DC offset compensation circuit is capable of driving the DC offsets of the input signals to zero in less than 20 s. Figure 11 shows the DC offsets in a steady state regime for the eight up-looking channels during a scientific flight over the Baltic Sea in December 2015. The theoretical standard deviation of the DC offset measurements when the actual offset is zero can be computed using basic statistics and is given by \(\sigma=\frac{1}{\sqrt{N}}\), where \(N\) is the number of independent samples used to estimate the offset. The DC offsets are estimated every second, so using \(80\cdot 10^{6}\) samples and taking into account that the noise equivalent bandwidth of the receivers is 34 MHz, the number of independent samples is reduced to  
\[N=\frac{2\times 34\;\mathrm{MHz}}{80\;\mathrm{MHz}}80\times 10^{6}=68\times 10^{6}\]  
Figure 10: Deviation of the system reference frequency with respect to the nominal value. **Top**: initial convergence period of less than 10 min. **Bottom**: stabilized clock during 14 h.  
which results in an expected variance of \(1.213\times 10^{-4}\). The measured standard deviation of the offsets is in the range between \(1.27\times 10^{-4}\) and \(3.32\times 10^{-4}\), and this shows that the offset compensation circuit is working almost the same as in the ideal case.  
From Figure 11 it can also be appreciated that some spikes, which are coincident in all channels, happen at particular instants. We have verified that these happened when the airplane was turning, which resulted in off-nadir antenna pointing. Whether the DC offset compensation spikes are attributed to a change in the input signal power due to off-nadir antenna pointing or due to the external interferences acquired during these turns remains unclear. Spectral analysis of the collected signals during the intervals is precluded by the fact that raw-data recording was switched off when the airplane was turning.  
Figure 11: Measured DC offset of the recorded signals with working real-time DC compensation circuit in a steady state regime.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Mixture Alignment Encoder
#### 2.2.1 Encoder Architecture  
In this paper, both the letter encoder and the character encoder share an identical architecture. Each encoder block includes a multi-head self-attention mechanism, residual connections, normalization, and a one-dimensional convolutional neural network (1D CNN). The multi-head attention mechanism enables enhanced extraction of cross-positional information. Residual connections are employed to address the issues of gradient vanishing and explosion. Normalization adjusts the mean and variance of the input values of each layer to achieve uniformity, thereby enhancing the convergence efficiency. The specific model structure is shown in Figure 3b.  
To establish closer connections between adjacent Latin letters, a one-dimensional convolutional neural network (1D CNN) with ReLU activation is employed instead of fully connected networks. A 1D CNN is particularly effective for processing sequential data, such as text sequences.  
For example, in Tibetan speech synthesis, consider the Latin representation \"bkra shis\", which is the result of transliterating Tibetan text into Latin letters. A 1D CNN applies convolutional filters to this sequence of Latin letters. Using a filter of length three, the network slides over the sequence and processes overlapping subsets such as \"bkr\", \"kra\", \"ra s\", and so on. This convolutional operation enables the model to capture local dependencies and patterns within the sequence, including specific combinations of letters or syllables. This capability to learn from adjacent characters or syllables is essential for generating accurate and natural Tibetan speech.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Multipath Performance Analysis
Figure 7 shows the carrier and subcarrier MEEs of three techniques for the BOC(15, 2.5) signal, where the multipath relative amplitude is 0.5. Two forward offsets are tested for the OC and PAOC techniques, which are 0.5 and 0.8 chips, respectively. Figure 7 demonstrates that the PAOC technique effectively mitigates carrier and subcarrier multipath errors when the multipath relative delay is greater than \(1-\tau_{oc}\) chips. The multipath mitigation performance of the PAOC technique improves with the forward offset. Moreover, the PAOC technique has the same multipath performance as the OC technique with the same forward offset.