esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Dataset  
The dataset used in this paper is an open dataset, BCI Competition IV 2a [24]. It is an EEG motor imagery dataset consisting of 9 participants, with 576 trials per participant. Each participant had two experimental sessions, T and E, 288 trials each. These two sessions were used to form train and test sets, respectively. There are four classes of movements, imaged by participants, marked with numbers from 0 to 3, with respect to the body parts of motor imaging, namely: left hand, right hand, feet, and tongue. The EEG data was recorded using 22 channels and with a 250 Hz frequency. Each trial was six seconds long. In the last three seconds, the motor imagery was performed. The data was bandpass-filtered between 4 Hz and 38 Hz, followed by extraction of trials from the recording. Although informative features could likely occupy a narrow frequency range, being related to mu- and beta-rhythms, we opted for this frequency range in order to be consistent with [PERSON] et al. [23]. In our view, using their parameters makes sense because a narrower frequency range is not always an optimal solution because the features of interest could have a wider spectrum than the standard definition of a rhythm's band; moreover, the network should fit a filter for separating the informative components from the non-informative ones, and they also can surpass the predefined bounds. Finally, features that are informative for OOD data detection are not necessarily the same that are useful for movement imagery detection. After preprocessing, each trial could be presented as a matrix sized 22 \(\times\) 1125.  
To model the presence of the out-of-domain data, the training set was modified as follows. From the training set, we deleted two classes of trials, labeled as \"feet\" and \"tongue\", leaving another two classes, \"left hand\" and \"right hand\". We decided to split the dataset in this way, considering the similar nature of the EEG signals related to left and right hands compared to the other types of motor imagination. Thus, the modified training set consisted of two classes and 144 trials per participant, whereas the test dataset has not undergone any changes. Therefore, we could treat the two classes used for training as \"in-domain\" data and the two classes not used for training as \"out-of-domain\" data.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
###### Abstract  
Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.  
road engineering; lightweight LiDAR; 3D point cloud; registration stitching; semantic segmentation; feature extraction
## 3 The Use of Supercontinuum Lasers in Biomedical Diffuse Optics  
The focus of the present review was to evaluate the use of SCL in biomedical diffuse optics, from the systems and methodological developments to their medical applications.  
Figure 1: Example of commercial SC laser sources and their typical spectra. (**a**) Fianium source, (**b**) Leukos source, (**c**) FYLA source, (**d**) YSL source. (**e**) Schematic of the classical triangular cladding single-core photonic crystal fiber in which light is guided in a solid core embedded in a triangular lattice of air holes. The fiber structure is determined by the hole-size, d, and the hole-pitch, \(\Lambda\). Like standard fibers, the PCF is coated with a high index polymer for protection and to strip off cladding-modes. Extracted from the application note on supercontinuum generation in photonic crystal fibers by NKT. (**f**) Example of full spectrum generated by a SC laser, showing the width of the broadband emission and the central peak wavelength that is a residual of the single-mode laser used for SC generation. The spectrum is taken from the SuperK COMPACT SC laser manufactured by NKT Photonics ([[https://www.nktphotonics.com/lasers-fibers/product/superk-compact-supercontinuum-lasers/](https://www.nktphotonics.com/lasers-fibers/product/superk-compact-supercontinuum-lasers/)]([https://www.nktphotonics.com/lasers-fibers/product/superk-compact-supercontinuum-lasers/](https://www.nktphotonics.com/lasers-fibers/product/superk-compact-supercontinuum-lasers/)), accessed date: 1 November 2021).  
Therefore, papers were identified using PubMed, Scopus, and Web of Science, searching for a combination of keywords including (supercontinuum laser | white laser) and (diffuse optics | NIRS). Moreover, a manual search from articles' references was performed and the personal reference library of the authors of the current papers has also been searched. Papers were rejected if no SCL was used in the method section of the reported work, if the application was not medically related, if they were reviews or if the method was not purely optical (i.e., no photoacoustic papers). A total of 95 publications covering a variety of focus were identified. We have identified 4 main categories of publications: system development (31 studies), optical properties estimation (16 studies), methodology (43 studies) and application (5 studies). Table 1 provides a summary of the main characteristics of the SCL used in the studies reported in this review, Figure 2 shows pictures of the instrumentation typically used, and Figure 3 presents typical output in terms of datatypes of such systems. Moreover, a summary of the studies included in this review is presented in Table 2 and includes the system used, the year, the category of the study and the target of the study. Finally, Figure 4 summarizes the main topics and characteristic of the systems used in the reviewed publications and shows the number of publications over the last 30 years that report the use of SCL in biomedical diffuse optics.
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 4 Conclusions and Future Work  
HDP is a common obstetric complication that severely threatens the life of pregnant women. Therefore, it is of great significance to detect and prevent HDP in advance. However, pregnancy examination data have the characteristics of high dimensions, large amounts, being in a time series and having a high missing rate, so a good data processing method plays an extremely important role in predicting and preventing HDP. This paper first introduces pregnancy examination data in detail and then proposes a series of data processing strategies, including basic data processing methods such as feature relation analysis, data filtering, feature extraction, balancing the dataset, etc. This paper also puts forward a method of missing value filling based on a bidirectional LSTM model and makes a comparison with cubic spline interpolation, KNN filling and an LSTM model, the results of which show that the bidirectional LSTM model achieves good results in filling missing data. Although this paper has made some progress in data processing, especially in filling missing data, there is still much work to be further studied and explored, including the following aspects:  
* In data processing, there are still some factors that are not considered, such as region, age, etc.
* The method of filling missing valuesis relatively simple at present, and the future research direction is to combine multiple algorithms to deal with different features.
* At present, the data preprocessing process is basically manual processing, so it can save a lot of time and stamina to standardize the processing process and build ETL (extract-transform-load) tools automatically.
* After prediction, screening proper drug treatment programs [33] is also important for doctors and patients. Building a complete intelligent medical system is very meaningful.
* At present, the amount of research data in the medical field is very large, but these data are often chaotic. The format of diagnostic data in different medical institutions is not uniform, so it is difficult to use it directly for research. Constructing a complete standardized medical research database is of great significance for disease prevention, drug development, and other research.  
Conceptualization, X.L., L.Y. and R.L.; methodology, X.L., N.Y., R.L. and Z.X.; software, X.L., Z.X., Y.Y. and N.Y.; resources, X.L., R.L., N.Y. and L.Y.; writing--original draft preparation, R.L., Z.X., Y.Y. and N.Y.; writing--review and editing, X.L., R.L., Z.X. and L.Y.; supervision, X.L. and L.Y.; project administration, X.L. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: Prediction effect of different pregnancy weeks.
# Evaluation of Metal-Organic Frameworks as Potential Adsorbents for Solar Cooling Applications
## 6 Comparison of MOFs with Other Adsorbents
Figure 11: Comparison between dehumidification efficiency (\(\eta_{\text{deh}}\)) for a MIL101@GO-6 (MILGO) and a silica gel-based desiccant wheel as a function of the relative humidity (\(\varphi\)) of process air [94]. Reprinted with permission from [94]. Copyright 2017 Elsevier. Figure 12: Water adsorption isotherms of conventional adsorbents at 25 \({}^{\circ}\)C [4]. Apart from the above-mentioned studies, a few reviews involving related topics of CO\({}_{2}\) capture with MOFs have appeared very recently [120; 121; 122; 123; 124]. [PERSON] et al. [125] also compared the performance of HKUST-1 with zeolite for cooling applications using the TRNSYS model. The MOF adsorbent for solar adsorption refrigeration is a nanocomposite made from an MOF matrix with carbon nanotubes incorporated therein and is preferably used as a powder [126; 127]. A comparison of different adsorbent materials including MOFs is presented in Table 3[128]. It can be observed that MIL 101(Cr)-H\({}_{2}\)O has a better water uptake capacity and it can be regenerated at a temperature of 90 to 140 \({}^{\circ}\)C. The temperature required for the regeneration of SAPO 34-H\({}_{2}\)O is also below 100 \({}^{\circ}\)C. The higher uptake capacity and lower requirement of regeneration temperature is an indication that MOFs can be a suitable material for adsorption cooling. Figure 14: Average enthalpy of adsorption of different MOFs [77]. Reprinted with permission from [77]. Copyright 2015 ACS Publication. Figure 13: Water adsorption isotherm of MOFs at 25 \({}^{\circ}\)C [4].
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 3 Results and Discussion
### Electrical Characterization of RF Transistors
Figure 2: Structure of a twenty-finger-configuration RF transistor based on randomly oriented carbon nanotube films and its electrical characteristics: (**a**) schematic diagram of a twenty-finger CNT-based RF transistor at high-magnification SEM with a total channel width of 100 \(\upmu\)m; (**b**) typical transfer characteristics of a 50 nm gate length twenty-finger RF transistor with \(V_{\text{ds}}\) at \(-1.2\) V; (**c**) typical output characteristics of a 50 nm gate length twenty-finger RF transistor with \(V_{\text{gs}}\) from \(-1\) V to 0.2 V with the step of 0.2 V; (**d**) frequency characteristics of a twenty-finger RF transistor with a measured cut-off frequency (\(f_{\text{T}}\)) of 78 GHz and a maximum oscillation frequency (\(f_{\text{max}}\)) of 60 GHz.
## 5 Conclusions and Outlook  
Detecting smoke is an important way to control straw incineration, which is an important for ensuring the ambient air quality and for sustainable agriculture. In this study, the YOLOv5s network model was used to detect smoke in remote sensing images. CBAM was added to YOLOv5s, and the Mish activation function was used instead of the leaky ReLU function. Our results indicated that the improved YOLOv5s maintained the original detection speed, with a detection speed of 476 frames per second, and achieved mAP75 and mAP50 values of 74.03% and 94.69%, respectively, which are 3.67 and 1.15% higher than those for the original algorithm. The improved model had both higher precision and recall rates than the original one. Therefore, the improved method proposed in this study can quickly and accurately identify smoke in the crop residue burning. This study may serve as a reference for improving the detection of smoke, and enhancing the effective management and control of straw burning.  
In this study, the data only included cultivated land, and the smoke was entirely caused by straw burning. However, we found that the burning of biomass fuel, wood,coal, and other materials in the surrounding area also released smoke, which had a certain impact on the identification results of smoke produced by straw burning. In the future, we will identify the smoke shape through the model. Based on the shape of the smoke, factors such as wind speed, wind direction, and humidity will be taken into account to determine the source of the smoke. According to the source of the smoke, we can classify smoke into three categories: smoke from straw burning, smoke from domestic burning, and smoke from factories. Based on the characteristics analysis of different types of smoke, the ability of the model will be enhanced to distinguish smoke produced by straw burning.  
Conceptualization and design of the experiments, H.L., J.L. and J.D.; performed the experiments, H.L., B.Z. and W.Y.; data analysis and writing, H.L., Y.H. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded as an environmental protection scientific research project of Jilin Provincial Department of Ecology and Environment, grant number E139S311; Jilin Province Development and Reform Commission, grant number 2020C037-7; and the Science and Technology Development Plan Project of Changchun, grant number 21 ZGN26.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [H.L.], upon reasonable request.  
The authors would like to thank the ESA website for providing the Sentinel-2 data. All data and images in this paper were used with permission.  
The authors declare no conflict of interest.
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 1 Introduction  
Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection [1], and is a significant public health challenge and economic burden in the industrialized world [2].  
The incidence of sepsis spans between 650,000 and 750,000 new cases per year in the United States, where it is the 10 th highest cause of death [3, 4]. Sepsis numbers are rising as the incidence of sepsis increases with age, and the proportion of elderly patients accessing the emergency department increases [5].  
Early care of sepsis and septic shock is essential for the patient's prognosis, and include early diagnosis, resuscitation, and prompt antibiotic treatment, which is often performed inthe emergency department (ED) [6; 7; 8]. Postponed diagnosis of sepsis increases morbidity and mortality [9].  
In the emergency department (ED) population, predictive scores have been extensively studied to identify sepsis and stratify mortality in septic patients [5; 10]. Most published models adopt traditional statistical techniques for mortality prediction and identification of septic patients [10; 11; 12; 13; 14]. Recently, machine learning has been proposed in this field, primarily for early identification of septic patients [15; 16]. By discovering predictive patterns in the data beyond linear relations, machine learning models can naturally handle the abundance and complexity of digital patient data, allowing for precise forecasts of which patients will develop sepsis. Although a few recent ML studies also focused on mortality prediction [17], none of them integrated information from patient provenience and validated it against well-known critically ill scores such as APACHE II, qSOFA, and SOFA [18; 19]. Therefore, the aim of the current study was to develop an interpretable and scalable ML model for identification of high-risk patients based on clinical, laboratory, and pre-hospital data, and to compare its performance with qSOFA, APACHE II, and SOFA scores.
## 4 Results and Discussion  
### Model Validation  
Figure 5 shows the comparison between the simulated and the experimental values of the oxygen-enriched area in the un-air-conditioned environment. The cross-section of the oxygen-enriched area is placed at a height of 1.5 m, which is consistent with the position of the oxygen sensor in the experiment. The oxygen-enriched effect is achieved when the oxygen volume fraction predicted by the model is greater than 22%. [PERSON] et al. (2016) pointed out that the hypoxic environment formed by factors such as elevated altitude, closed places and environmental pollution affects people's normal life and occupational safety, and the standard for supplying oxygen to the hypoxic environment is that the volume fraction of oxygen reaches about 22%. The simulation values of the oxygen-enriched area formed in the axial and radial directions are larger than the experimental values, regardless of the single oxygen supply vent or the double oxygen supply vents. This is because, during the experiment, there is low wind speed in the confined building room in addition to human interference. Thus, the diffusion effect of indoor oxygen is strengthened, and the loss of  
Figure 4: Computational grid of the artificial climate chamber model.  
Figure 3: Verification results of grid independence.  
oxygen during the diffusion process is increased. It can also be seen that the axial and radial errors of the simulated and experimental values of the oxygen-enriched area are less than 0.25 m; the difference between the simulated value and the experimental value of the axial velocity is small. Therefore, the mathematical model can better predict the oxygen-enriched characteristics and effects in a confined building room.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results  
### Dataset Descriptions  
#### 3.1.1 Massachusetts Road Dataset  
The Massachusetts Road Dataset, globally recognized as the most extensive road dataset accessible to the public, was established by [PERSON] and [PERSON] [34]. This dataset covers approximately 500 square kilometers, includes a diverse array of geographic regions from urban to rural, and encompasses an assortment of road types such as highways, rural dirt roads, and asphalt roads. Notably, it also includes confounding factors, such as rivers and railways, which can mimic the appearance of roads. The dataset comprises 1171 images, each with a per-pixel resolution of 1.2 m and dimensions of 1500 \(\times\) 1500 pixels. The dataset is categorized into 1108 training images, 14 validation images, and 49 test images. The training and validation sets feature binary image labels, where road pixels are denoted as 1 and background pixels as 0.  
In light of hardware constraints and the necessity of optimizing model performance, the utilization of large-sized images for training was deemed inefficient. Therefore, in this study, the 1108 training images and their corresponding labels were segmented into 27,700 sub-images, each with dimensions of 256 \(\times\) 256 pixels. The model was subjected to a training-validation split at a 9:1 ratio. Consequently, the finalized training set encapsulated 20,776 images, each with dimensions of 256 \(\times\) 256 pixels, whereas the validation set  
Figure 3: HRU-Net structure. Given a remote sensing image, abstract features are extracted from the input to obtain the final pixel-wise road map prediction. Each blue box represents a multi-channel feature map, with the number of channels annotated beneath the box. Gray boxes symbolize operations from UMR modules, while orange-yellow boxes correspond to operations from MRF modules. These are arranged in ascending order from the bottom of the model upwards, numbered 1, 2, 3, 4. Arrows indicate distinct operations.  
contained 6924 images of identical dimensions. To prevent overfitting and to guarantee a comprehensive performance evaluation, a test set was compiled, incorporating 63 images from the original dataset, including the 14 validation images and the 49 test images.
## 5 Simulation Results and Discussion
### Comparison with the Existing Sensor FDIs  
Table 2 presents a brief comparison of model-based current sensor FDIs. These schemes are proposed for an IM system [8; 28; 29; 30], DFIG system [9; 10] and PMSG system [2; 31]. The schemes in [28; 29; 30] are presented to isolate single sensor fault. In these approaches, a bank of observers is established to monitor each sensor fault on the basis of the rest sensors are health. T-S fuzzy observer [9] and sliding mode observer [31] are proposed to isolate single type of faults in \(\alpha-\beta\) and \(d-q\) frame. The measurements of three-phase currents are utilized to generate estimation errors that can only isolate faults in a stationary frame or synchronous reference frame. The TVKF scheme is presented for the PMSG system in [2]. It utilizes a generalized likelihood ratio maximum-shift strategy to evaluate the faulty residuals generated by the frequency domain model, which results in higher computation complexity. This work is presented for multiple sensor fault isolation of PMSG or PMSM based system with a simple observer based algorithm framework. In general, the proposed method can detect and isolate multiple sensor faults simultaneously in a short diagnosis time. It employs mechanical signals and current signals in the control loop without any additional measurements and hardware circuits, and can be integrated into the control loop. In addition, it shows excellent performance in multiple types of faults including gain fault, biased fault, and disconnection fault. The fault detection threshold is related to disturbances with respect to Theorem 1 and Theorem 2. Furthermore, the maximum and minimum power for WECSs are specifically defined by the operational region shown in Figure 2.