esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section
### Overall Results on Part II Experiment  
#### 4.6.1 Basic Experiment  
In this section, the constraint extraction model and the question reasoning model are evaluated as a whole and compared to other baseline models on WQSP dataset and CQ dataset, respectively, with the findings presented in Figure 6. The figure shows that the \(F1\) values of the proposed model outperform the baseline models on both datasets, by 74.5% and 77.8%, respectively.  
SRN is a weakly supervised model that ignores the relations' overall semantic information. Both IRN and UHop are relational supervised models that neglect the local information of the relations, and both use a delayed termination mechanism that does not adequately filter the information during the hop-by-hop updating of the questions. PullNet and TransferNet are designed to be able to use both documents and knowledge bases as knowledge sources, and because only the knowledge graph is employed as the information source in this experiment, the effect would be affected. For MRP-QA, although it uses multi-paths reasoning, when the number of hops increases and the constraint relations become complex, it instead performs worse than our proposed model due to the increased  
Figure 6: Performance of each model on the test set.  
choice of paths. Because the knowledge base schema was not incorporated as inputs during the Hereditary Tree-LSTM training, HTL is difficult to identify between distinct multi-hops.  
Because of the independent treatment of the constraint information in the question, our proposed model performs better in handling complex questions with constraints. Furthermore, the model employs a question reasoning system based on beam search, which increases search efficiency while decreasing search space.
## 3 Results
### TOC, \(\delta^{13}\)C\({}_{\rm{org}}\) and \(\chi_{\rm{fd}}\) Characteristics  
Figure 3 shows the results for the \(\delta^{13}\)C\({}_{\rm{org}}\) composition, TOC concentration, C\({}_{4}\) abundance and \(\chi_{\rm{fd}}\) of the CQS loess section. The values of all indicators showed similar variations with depth. In the weak paleosol layer (L1S1), the values of all indicators were higher than in the loess layer (L1L2) (Figure 3). The TOC concentration for the loess section ranged from 0.11% to 0.47%, and the average was 0.24% (Figure 3a). The \(\delta^{13}\)C\({}_{\rm{org}}\) composition varied between 24.56% and \(-\)20.80%, and the average was \(-\)22.19% (Figure 3b). The value of \(\delta^{13}\)C\({}_{\rm{org}}\) in the weak paleosol layer (L1S1) gradually increased with increasing depth, while the value of \(\delta^{13}\)C\({}_{\rm{org}}\) in the loess layer (L1L2) gradually decreased with increasing depth. The minimum value of \(\delta^{13}\)C\({}_{\rm{org}}\) is displayed in the loess layer (L1L2). Figure 3c shows the variation of the relative abundance of C\({}_{4}\) plants in the CQS loess section. The value of C\({}_{4}\) abundance ranged from 3.35% to 32.32%, and the average was 21.31%. The curve of \(\chi_{\rm{fd}}\) for the CQS loess deposits showed fluctuation (Figure 3d). The variation range of \(\chi_{\rm{fd}}\) was from 3.40 \(\times\) 10\({}^{-8}\) m\({}^{3}\).kg\({}^{-1}\) to 13.92 \(\times\) 10\({}^{-8}\) m\({}^{3}\).kg\({}^{-1}\), and the average value was 9.70 \(\times\) 10\({}^{-8}\) m\({}^{3}\).kg\({}^{-1}\). The highest value of \(\chi_{\rm{fd}}\) appeared in the L1S1 layer.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
###### Abstract  
We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.  
rairy environment identification; channel state information; deep learning; vehicular networks; autonomous vehicles 2024143788103390/app14093788  
1  
[PERSON]; [PERSON]; [PERSON] Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles. _Appl. Sci._**2024**, _14_, 3788. [[https://doi.org/10.3390/app14093788](https://doi.org/10.3390/app14093788)]([https://doi.org/10.3390/app14093788](https://doi.org/10.3390/app14093788))  
1
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 3 Injection Timing
### Two-Stage Early Injection
#### 3.2.3 First Injection Quantity
Because the heat release processes of the first and second injected fuel combustion were too close, resulting in higher combustion temperature and NOx emission, with the increased proportion of the first injection, more injected fuel was premixed and combusted, and less second injected fuel was combusted in the high-temperature atmosphere, resulting in lower NOx emissions. Table 6 shows a summary of the variation of performance and emissions of the PCCI engine after increasing the first injection quantity. In general, increasing the first injection quantity will increase HC and CO emissions due to wall-wetting. NOx emissions also increase due to the faster heat release rate and higher combustion temperature caused by a large portion of premixed combustion. Soot emission is determined by the opposite effects of wall-wetting, higher combustion temperature, and lower proportion of diffusive combustion. As an experimental value, the first injection quantity should be limited to 40% of the total injection fuel. Figure 10: Flame characteristics as a function of the first injection quality ratio [79]. (**a–d**): first injected fuel mass portion: 20%, 30%, 40%, and 50%, respectively; the images of flame change with crank angle.
## 1 Introduction
Nevertheless, most of the currently available methods are still based on laboratory settings and have limitations regarding the capture rate, capture volume, and even uncontrolled lighting conditions [36, 37]. In a system developed by [38], the authors developed a 3D marker-less motion capture technique using OpenPose, a state-of-the-art machine-learning, posture-tracking algorithm, from cameras. Several other researches are adopting motion-capture technology leveraging on deep-learning algorithms [39, 40]. The current challenge this and other strategies are trying to solve is to make detection systems applicable in vast environments, such as open air or welfare fields. Besides the camera systems, in recent years, laser range scanners have been gaining interest to be used for gait analysis. This is because the cost of a laser range scanner has decreased greatly and real-time multi-line LiDAR (Light Detection and Ranging), such as Velodyne HDL-64E [41], Puck, and SICK TiM7 xx [42], provide three-dimensional range information to a target using multiple laser beams in real time. Additionally, it has a fairly long range of measurement of over 25 m. However, this type of particular LiDAR is mainly used for person identification and recognition activity [43, 44]. There have been few studies on detecting spatiotemporal gait parameters only using Velodyne LiDAR. A research study on spatiotemporal parameters gait analysis based on a LiDAR approached was conducted by the authors in [45]. The study focused on step length measurement using LiDAR utilizing dual laser range sensors. The authors reported that the measurement using a laser range sensor is quick and easy to set up. The proposed method involved installing dual laser range sensors at the opposite ends of a 5 m walking path. The objective was to analyze a longer-range gait measurement from ten healthy participants. The authors proposed a walking test on three different speeds--slow, normal, and fast walking--and compared the validity of the proposed method to the instrumented walkway measurements. In this paper, we propose a measuring system to estimate the step length of the subject using a single LiDAR system. The aim of the present study is to develop and validate the usability of the single LiDAR system as a step length measurement that can perform outside the laboratory setting, building on the setup as reported in [45]. Two-dimensional LiDAR data of below the ankle area, as the subjects walked forward, were used to evaluate the walking experiments of two different walking speeds, namely, a normal walk and fast walk. Normal walking speed is defined as the participants' preferred walking speed in a comfortable manner. Meanwhile, for fast walking speed, the participants were instructed to walk twice as fast as one's preferred normal walking speed. We employed polynomial fitting methods to identify the foot off and step lengths. We utilized visual inspection to confirm the validity of the proposal and compared the performance of the two approaches.
## 3 Result and Discussion
### Calibration
#### 3.4.1 CO\({}_{2}\) Concentration Measurement  
The developed sensor node and CO\({}_{2}\) probe (validator instrument) were placed in the test chamber to measure CO\({}_{2}\) concentration, as shown in Figure 10b. Various CO\({}_{2}\) concentrations were supplied to the test chamber and measured by sensor node and CO\({}_{2}\) probe. The sensor node recorded the CO\({}_{2}\) measurement result to the terminal monitor using the Adafruit dashboard, while the CO\({}_{2}\) probe recorded the CO\({}_{2}\) measurement results to the Testo 440 terminal. The CO\({}_{2}\) measurement results include comparing the sensor-node and CO\({}_{2}\) probe readings, shown in Table 4.  
Figure 10: The calibration process of sensor node: (**a**) measurement setup; (**b**) sensor node placement in a controllable test chamber.  
Based on the data in Table 4, the average percentage deviation of the sensor-node reading is 11.62%. The difference needs to be reduced to improve sensor-node accuracy.
## 3 PVC Gel Lens  
### Working Principle  
Most polymeric actuators can only be driven in water, and the deformation can only be obtained by bending. As a comparison, PVC gels including the plasticizer do not need any solvent to work, so they can be operated in air. PVC gels are unique actuator materials and can be used to develop various actuators with large deformation and different motions by designing appropriate boundary conditions. It has been reported that maximum strain could reach 1000% and still recover to its original shape in an electric field. It is suggested that the deformation accompanies a huge electrorheological process [50; 51]. PVC gels are the most promising smart materials for an artificial muscle in terms of  
Figure 5: Structure and operation method for a DEA film to shift a solid lens in the transverse direction: (a) without voltage and (b) with voltage.  
Figure 6: Device structure of an FZP lens using a DEA film: (a) no voltage and (b) with a voltage.  
the applied DC voltage and the response rate. The electric efficiency of PVC gels is better than that of conventional polymeric actuators.  
PVC gels that deform in response to an electric field have been investigated by [PERSON] et al. [41; 42]. A PVC gel is a mixture of PVC power and a plasticizer. It is a jelly-like, soft, and dielectric material. Figure 7 shows the operation mechanism of a PVC gel device [50]. The PVC gel is sandwiched by two stripe electrodes. Suppose the PVC gel has a symmetrical shape between the electrodes in the voltage-off state, as shown in Figure 7a. By applying a sufficiently high DC voltage to the electrodes, electric charges (electrons) are injected from the cathode into the PVC gel, and the charged plasticizer molecules shift toward the electrode connected to the anode. The charged molecules accumulate in the region near the positively charged electrode. The electric charges promote the electrostatic adhesiveness of the PVC gel onto the anode, and a creeping deformation appears on the positively charged electrode surface, as shown in Figure 7b. When the DC voltage is removed, the PVC gel can return to the original shape. The PVC gel acts as a dielectric (it is definitely not a good conductor), so the current leakage is very small (typically about several micro-amperes).  
In a PVC gel, the PVC and plasticizer can be mixed at any weight ratio. However, the concentration of the plasticizer in the mixture can largely affect the properties of the PVC gel [28; 42]. For example, Table 1 lists some parameters of a PVC gel prepared using PVC and dibutyl adipate (DBA) with different concentrations. DBA is a typical plasticizer. When the concentration of DBA is increased, the PVC gel tends to be softer. This leads the Yong's modulus to decrease and the transmittance to increase, and results in a larger strain with a lower driving voltage.
## 2 Materials and Methods
The architecture comprised a primary dense layer with 64 units followed by a dropout rate of 0.2, a second dense layer with 32 units and dropout rate of 0.2, and a third dense layer with 16 units followed by dropout of 0.1. The dropout layers were incorporated to mitigate overfitting by randomly deactivating a proportion of neurons during training. The model was compiled using the Adam optimizer with a 0.0005 learning rate. The confusion matrix was mainly used to evaluate algorithm performance [39]. This matrix provided the metrics on non-defaulters accurately classified (true positives (TPs)), defaulters accurately classified (true negatives (TNs)), defaulters classified as non-defaulters (false positives (FPs)), and non-defaulters classified as defaulters (false negatives (FNs)). Measurements of accuracy, precision, recall/sensitivity, and F1 score were computed as key performance indicators. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) were also computed, with the distance of the ROC curve above the diagonal random classifier line (AUC = 0.5) used to determine how significantly the algorithms performed against random guessing. Lastly, significance testing of models was performed using the DeLong test function [40] to compare the areas under two or more ROC curves and to determine whether the difference in AUC between two models was statistically significant.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Various Eye Diseases  
#### 3.4.1 Ocular Oncology  
A conditional probability estimation neural network (CPENN) for survival prediction in patients with choroidal melanoma was implemented by [PERSON] et al., and they compared the outcomes with the standard Kaplan-Meier analysis [56]. All-cause survival curves were similar in the two techniques (\(p<0.05\)), not including older patients for whom the NN assessed decreased death than the Kaplan-Meier method did.  
[PERSON] et al. used a two-step model to obtain automatic segmentation of uveal melanoma images. First, they implemented a class activation map, a conditional random field, and an active shape model to identify tumors in magnetic resonance images. Secondly, a 2D-Unet CNN segmented the tumor [57].  
[PERSON] et al. classified uveal melanomas for BAP1 (tumor predisposition syndrome) expression with an AUC of 0.99 by means of histopathological images. They created 8176 images of 256 \(\times\) 256 pixels and processed them with a large CNN. BAP1 expression is related to a reduced prognosis in those tumors because it provides metastatic capacity [58].
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Proposed Framework
#### 3.1.4 Analytic Dashboard  
Another outcome of our proposed framework is an interactive analytic dashboard, as shown in Figure 2, which visualizes the results of topic modeling using BERTopic. The dashboard is designed to support rapid diagnostics and anomaly classification by presenting detailed information about individual log messages through the following six components:* **Anomaly status:** This section prominently displays whether the analyzed log message is classified as an anomaly or normal. It enables operators to immediately assess the urgency of the issue.
* **Log message content:** This part presents the original content of the log message, allowing users to understand the raw system output. It provides a whole context for interpreting the detected anomaly.
* **Classified topic:** This shows the topic number assigned to the log message by BERTopic, indicating the cluster it belongs to. This helps group similar incidents and track recurring issues.
* **Log message information:** This section contains key metadata fields extracted from the log, including severity, system, detector, and facility. These metadata elements are crucial for routing alerts to the correct subsystem experts and understanding the operational scope of the issue.
* **Topic terms:** This panel lists the top keywords and multi-word expressions that define the assigned topic, along with their corresponding c-TF-IDF scores. These terms offer insight into the semantic themes captured via the model and aid in identifying anomaly terms.
* **Topic distribution:** This pie chart illustrates the probabilistic topic distribution for the log message, showing how strongly the message aligns with other topics beyond the primary one. This probabilistic view supports uncertainty estimation and highlights potential overlaps or ambiguities in the message's classification.  
Together, these components provide a comprehensive and interpretable interface for analyzing log data, supporting both high-level system monitoring and in-depth anomaly investigation.  
According to both outcomes, this framework aims to enhance the anomaly detection process significantly, improving both the detection accuracy and comprehension of log messages. The framework enables a deeper understanding of anomalous behaviors by offering detailed insights through an intuitive dashboard. This facilitates more effective  
Figure 2: Dashboard information: (**1**) anomaly’s status, (**2**) log message’s content, (**3**) classified topic, (**4**) log message’s information, (**5**) topic terms, (**6**) topic distribution.  
identification and mitigation of potential issues, advancing log analytics and contributing to the development of more reliable systems.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 3 Results
### Energy Harvesting Module  
We first check the solar energy and then the mechanical energy. The tag turns on when the voltage in the supercapacitor reaches 2.7 V. Figure 4 shows the time it takes for the solar module to charge the supercapacitor on a sunny day. The vertical grey line in 30 min and the horizontal orange line in 2.7 V indicate the time and voltage, respectfully, needed to turn on the device. This means that the device must be exposed to the sun for 30 min before it turns on because this is the time needed to reach 2.7 V. The highest value for the supercapacitor is 4.2 volts, which is maintained after being reached.  
Figure 4: Voltage in the capacitor with solar energy (blue trace). Thresholds refer to the working limit. The cross between the vertical line (grey trace, 30 minutes) and the horizontal line (orange trace, 2.7 V) is when the system starts to work.  
There are four graphics in Figure 5, which show the drop of voltage throughout the day in a shady place for several days. The first shows the drop during the night, the next two show the drop during some hours with daylight, and the last is a zoom of the curve of the last picture. The horizontal orange line is the threshold at which the system is on, which is checked when the LED turns on and, as is already known, happens at 2.7 V. The blue line is a square curve that represents when the system turns on with \"1\", and when system turns off with \"0\".  
The mechanical module was tested with the tag in the same place for several days, and the charge measurements of the supercapacitor are reflected in Figure 6. SYS is represented by points because it collected a lot of energy with every car that passed; but then, if no cars passed, the voltage dropped quickly, so a continuous signal would, in this case, not be real. In BAT, the voltage is maintained for longer because it is saved in the supercapacitor, so the line in the graphic is continuous. The threshold voltage to turn on the tag is represented by the horizontal orange line.  
Figure 5: Voltage of supercapacitor with tag in a shady place for solar panel. There are four images that represent how the voltage changes in the supercapacitor for different days represented by the yellow line. (**a**) Shaded conditions during the night between 23 and 24 July, 2019. The voltage drops because there is no energy source. System is off. (**b**) Shaded conditions during 8 August 2019 at noon. The voltage rises but it starts at low value, so this increase is not enough to turn on the system. System is off. (**c**) Shaded conditions during 9 August, 2019 at noon as (**b**), but for more hours. The voltage rises and the start point of the voltage is close to the threshold that turns on the system, so the system turns on; however, when system is on it needs more energy, which explains why it always oscillates between on/off. (**d**) Reflects the graphic (**c**), but with a delimited voltage range to see the variation in the voltage and the zones in which the system is on.