esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Predictive Model Building
#### 2.3.1 Feature Selection  
The training/test dataset was composed of 239 patients having only parenchyma reconstructed CT scans. The 175 RFs obtained from each Par-CT scan were first scaled using the z-score and then given as input into a Cox proportional-hazards model regularized using the Least Absolute Shrinkage and Selection Operator (LASSO-Cox).  
The Cox proportional-hazards model is widely used in survival analysis to study time-to-event data presenting censored occurrences and covariates that may change over time. The LASSO-Cox was chosen to predict the patient's OS. The Par-CT dataset was split into training and test sets following a stratified 70/30 split. The best hyperparameter, in particular, the penalty parameter, (\(\lambda\)) choice were obtained with glmnet cross-validation function to solve the minimization problem of the partial likelihood. The final feature selection was performed with the glmnet function specifying the previously estimated \(\lambda\) value.  
The Lasso regularization method was chosen because it shrinks to exactly zero the coefficients relative to the superfluous features compared to other approaches (e.g., Ridge or elasticNet) which make the parameters small but never zero. Hence, this regularization phase also works as a feature selection step. These operations were repeated 100 times by changing the seed of the random process leading the training and test sets while keeping the stratified nature of the groups. A score of frequency was calculated for the LASSO-Cox selected features, evaluating the number of times that each feature appeared in the built models to guarantee the robustness of the feature selection phase (named occurrence rate). Sensitivity analysis was conducted by increasing the cut-off of the number of retained features which resulted from each of the 100 developed models. The occurrence cut-off of each variable was regularly increased in steps from 10% to 60%. The number of RFs versus the occurrence cut-offs were determined, while the Bayesian information criterion (BIC) versus the occurrence cut-offs was used to identify the optimal model. The significance of the prediction was assessed through the \(p\)-value associated with the Kaplan-Meier survival curves obtained from the scores given as output by the optimal model determined following the procedure mentioned above. The Hazard Ratio (HR) was computed by taking the exponential of the coefficients associated with each RF. Values greater than one indicate that higher values in the feature are associated with increases in the risk of death, whereas values smaller than one indicate that higher values tend to indicate lower risks.  
The analysis was performed in R Studio version 4.0.2.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 3 Experiments
### Retrieval Results and Generated Sentences  
While demonstrating the performance of proposed approach in this work across a range of examples is crucial, the limitations of the BLEU score as a measure of text generation quality must be acknowledged. This score may not fully capture the superiority of our method in EEG signal retrieval over baseline approaches. Therefore, examples chosen at random may not present an accurate portrayal of the comparative performance of our method. As a remedy to this, specific queries were selected representing both normal and abnormal EEGs, shown in Figures 5 and 6, respectively. The results are organized based on relevance score. Since the signals span about 30 min and are too lengthy for visualization in a single figure, a 5 s subset of each signal is presented. The associated original impression is displayed below the signal. It is important to note that every signal is indexed using the impression generated by each method, rather than the original impression.  
For the query \"normal eeg in wakefulness and brief stage 2 sleep\" showcased in Figure 5, the proposed method in this work accurately indexes normal signals exhibiting both wakefulness and stage 2 sleep. These results are presented in each column. Contrarily, the baseline methods misinterpret some abnormal signals as normal. In the case of an abnormal EEG, as represented in Figure 6, the query \"abnormal eeg due to: low voltage suppressed background. absence of reactivity or variability\" is utilized. The proposed method accurately retrieves the precise signal displayed in the  
Figure 4: Comparison of average NDCG scores between the proposed model and baselines for each set of five relevant documents. \(k\) denotes the number of relevant documents.  
rightmost column, whereas the baseline methods return signals which only partially match the query sentence.
# Applications of Smart Helmet in Applied Sciences: A Systematic Review
## 3 Results
### Types of Wireless Communication Technology Used in Smart Helmet Studies  
The data obtained from smart helmets can be transmitted to other smart devices or cloud servers using several wireless communication technologies. Of the 103 articles, 88 reported studies that used wireless communication technology, such as RF, Bluetooth, Wi-Fi, and Zigbee for this purpose, whereas the remaining did not. Figure 10 shows the cumulative number of uses of wireless communication technology by year for sharing data from smart helmets. According to the analysis results, RF and Bluetooth were the most commonly used, and the number of uses has been increasing rapidly since 2015. Zigbee has been used for the longest time; however, its usage has not increased significantly until recently.  
Figure 8: Cumulative number of microcontrollers used in smart helmet studies by year.  
The reason why RF and Bluetooth are used frequently is related to the microcontroller used in smart helmet applications. Until now, the chip MCU and single-board MCU have been mainly used for smart helmet development, and these two types of microcontrollers often use RF and Bluetooth as shown in Figure 11. In the future, as the use of single-board computers increases, the frequency of use of Wi-Fi is expected to increase. In addition, the uses of Bluetooth and Wi-Fi may increase when the use of smart devices increases in smart helmet studies.  
Figure 10: Cumulative number of wireless communication technologies used in smart helmet studies by year.  
Figure 9: Number of uses by sensor and microcontroller types in smart helmet studies.
###### Abstract  
Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current-Voltage (I-V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.  
photovoltaic (PV) system; autonomous monitoring; support vector machine (SVM); genetic algorithm (GA); line-to-line (LL) faults; feature selection +
Footnote †: journal: Applied Soft. 2020, 10, 5527
## 3 Architecture of the Platform and Related Software and Hardware Aspects  
The architecture of the platform is outlined in Figure 2, which depicts three main modules. One of the modules is responsible for the localization process, which periodically provides updated values for the position and velocity of the mobile node. Another module undertakes the beacon-based signaling, used to periodically advertise the node's presence (also announcing the node's current position and velocity) and to sense the existence (and status) of other nodes in its neighborhood. The information about neighbor nodes is collected into an appropriate data structure (see \"Beacon Table\" in Figure 2). The third module manages the messages to be forwarded to other nodes. For each such message, this module determines the most suitable next-hop node in the neighborhood and forwards the message to it. Next-hop selection occurs in a sub-module labeled \"logic for the routing metric\". Different routing protocols can be implemented by modifying only this localized part. Section 3.1 provides further details on the structure and functionality of the aforementioned modules, also discussing some additional auxiliary mechanisms (not depicted in Figure 2).  
Figure 1: Use-case for the ATLAS experiments.
## 7 Recommended Framework
### Simulation Tools for Validation  
Validating energy-efficient strategies presents challenges due to the limited availability of effective testing and simulation in real-world IoT scenarios. Simulation tools play a critical role in overcoming this limitation by enabling the validation and optimisation of proposed strategies for IoT networks. The following tools are recommended to test the effectiveness of the framework:  
* Data Collection for CoAP: Using historical environmental data (e.g., temperature and humidity metrics) from sources such as the Intel Berkeley Research Lab can help evaluate CoAP modifications and assess how protocol adjustments impact energy efficiency.
* Eclipse 4.19 and MATLAB 2020b for Fuzzy Logic Systems: These platforms enable the simulation of IoT environments to test fuzzy-logic-based solutions. MATLAB, in particular, can generate fuzzy outputs that inform resource allocation and task prioritisation, providing insight into how fuzzy logic improves energy performance.
* PureEdgeSim for Bio-Inspired Algorithms: PureEdgeSim is effective for modelling IoT environments across various computing layers. It allows researchers to test algorithms such as the IoT-Mist Bat-Inspired Algorithm (IMBA) under different workloads, providing a clear assessment of energy savings and system efficiency improvements in real-world scenarios.
## 3 System Overview
### Multi-Distance Scheduler in LiDAR Scans  
A multi-distance scheduler (MDS) is inspired from the idea of multiple resolution maps in Google's Cartographer utilizing a multi-stage scan matcher from low to high resolution. An MDS can be used to reduce computational cost as compared with using full high-resolution scans. It uses a scan matcher with low-resolution map data for the initial pose estimation, which then is used to bypass scan matcher iteration of every possible translation and rotation in a high-resolution map. Thus, it minimizes the performance penalty while maintaining accuracy.  
The multi-distance approach is similar to the multi-resolution map. Instead of controlling resolution, we propose controlling sensor distance that generates point cloud frame (\(\mathcal{F}\)), whereby contains an array of object point (\(\mathcal{F}\)) detected from the LiDAR in local sensor coordinates. Limiting sensor distance results in different details of the scan, as shown in Figure 2.  
The controlling method is done via a scheduler, which swaps between two sensor states, namely a short distance state and a long distance state at a fixed frequency. It is known in scan matching algorithms that the larger the image data or map, the greater the amount of processing time required as compared with small images [23]. However, because the large images from the sensor scan capture more features, the computation results using these features generate less rotational and translational errors. Thus, it is hoped that the combination of these two scan states, i.e., short and long distance, achieves a similar result of increasing the processing speed performance while maintaining accuracy. Some additional modifications are needed to accommodate the proposed method. Table 1 shows the scheduler algorithm to control the distance of the scan. It uses a time variable which is based on the period for trigger. The period ratio is configured to, for example, 1:1, 1:2, 1:3, etc. The weight of scan matcher also needs to be weighted differently. A more extensive distance sensor scan requires higher weight on the SLAM pose estimation as compared with a shorter scan distance because a larger map contains more feature data, and thus is likely to have low covariance as more features are used to compute the pose.  
Figure 2: Sensor scan state with different distances: (**a**) LiDAR scan 25 m distances and (**b**) LiDAR scan 60 m distances.  
\[odds(p) = \frac{p}{1-p^{*}}*Weight_{map\_size} \tag{2}\] \[M_{new}(x) = clamp(odds-1(odds(M_{old}(x)).odds(p_{hit}))\]
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 2 State of the Art
Symmetrical uncertainty and SVM algorithms were deployed for feature weighting, with clustering being determined by a function-based model. The marked improvement in the prediction model's accuracy served as evidence of this approach's validity. A feature selection method based on the extreme gradient boosting (XGBoost) algorithm was melded with a temporal convolution network (TCN) by [PERSON] et al. [19]. This combination markedly reduced the root mean square error (RMSE) and mean absolute error (MAE) indicators when excluding the wind direction and air relative humidity features from the model's input. The adaptive dynamic grey-wolf dipper-throated optimization (ADGWDTO) algorithm, a novel method developed by [PERSON] et al. [20], optimizes weight values in a weighted ensemble model. The novelty lies in incorporating the foraging behavior of dipper-throated birds, thus enhancing the algorithm's exploration capability. The ADGWDTO revealed superior performance in feature selection and outperformed state-of-the-art feature selection algorithms. Moreover, [PERSON] and [PERSON] [21] proposed an ingenious method by coupling the filter-wrapper non-dominated sorting differential evolution algorithm with K-medoid clustering to select pivotal weather-related factors (FWNSDEC-SSA). The initial features were several meteorological variables like temperature, humidity, and dewpoint and were processed by a convolutional LSTM network. This technique provided a more efficient set of features to be processed, resulting in a remarkable improvement in nowcasting accuracy compared to various benchmarks. Through the review of these innovative approaches, it becomes evident that feature selection plays a pivotal role in enhancing the performance of wind speed prediction models. The techniques range from biologically inspired algorithms like CRO to complex deep learning networks like the SDAE-LSTM network. Each of these methods offers a unique approach to feature selection and contributes to the overarching goal of improving the accuracy and efficiency of wind speed prediction models. The analysis reveals a predominant focus on wind speed and power prediction within the realm of feature engineering. This leaves a discernible void in predicting wind direction and gusts, which are significant across various sectors, particularly within aeronautical operations, where localized wind gusts can be indicative of critical hazards affecting aircraft stability and safety during takeoff and landing, such as wind shear [22; 23]. Therefore, research must persist in probing these innovative methodologies, exploring different target variables to optimize ML performance in new fields.
## 4 Case study Example--Beverage Production and Distribution Machine
### Decomposition of the System
In the discussed example, the following result is obtained after this operation:  
\(S_{1}=\{p_{1},\ p_{4},\ p_{7},\ p_{10},\ NOP_{1},\ p_{19},\ p_{20}\}\), \(S_{2}=\{\text{NOP}_{2},\ p_{2},\ p_{5},\ p_{8},\ p_{11},\ p_{14},\text{NOP}_{3}\}\) \(S_{5}=\{\text{NOP}_{4},\ p_{3},\ p_{6},\ p_{9},\ p_{12},\ p_{15},\text{NOP}_{5}\}\), \(S_{6}=\{\text{NOP}_{6},\ p_{13},\ p_{16},\ p_{17},\ p_{18},\text{NOP}_{7}\}\). The applied NOPs strictly refer to the functionality of the system. For example, \(p_{18}\) in the first component is replaced by \(NOP_{1}\), since operation assigned to this place is executed within \(S_{6}\). Similarly, \(p_{1}\) occurs only within \(S_{1}\), while in all remaining components it is replaced by NOPs (\(NOP_{2}\) in \(S_{2}\), \(NOP_{4}\) in \(S_{5}\), and \(NOP_{6}\) in \(S_{6}\)), etc. Note that enumeration of NOPs does not influence on the final result (it is just an indexation), thus designers can do it according to their needs. In the presented example, three NOPs are initially marked: \(NOP_{2}\), \(NOP_{4}\), and \(NOP_{6}\) since they refer to the initially marked place \(p_{1}\). Furthermore, there are NOPs that replace more than one place. For example, \(NOP_{3}\) replaces four subsequent places: \(p_{17},p_{18},p_{19},p_{20}\). Such a joining of NOPs is not necessary (the system can be decomposed by replacing a place per one NOP) but may reduce the utilised area of the FPGA. Figure 4 illustrates the decomposed Petri net. It should be underlined that obtained four components strictly refer to the functionality of the CPS. The first SMC is in charge of managing the trolley (with all associated conditions and actions). Components \(S_{2}\) and \(S_{5}\) control containers (first and second, respectively). Finally, actions assigned to \(S_{6}\) are related to the third container.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 5 Multi-Hazard Assessment and Vibration Control of Buildings
### Multi-Hazard Assessment of Buildings
The risk-cost-benefit framework, based on life-cycle and scenario-case analyses presented by [PERSON] [132], incorporate probabilistic modelling of hazards, structural fragility, and expected costs during different service intervals. Multi-hazard consideration in performance-based engineering and performance-based design criteria, addressing wind and seismic forces, has been researched extensively in the literature. [PERSON] and [PERSON] [133] present one of the first applications of the performance-based engineering approach in a multi-hazard scenario. A probabilistic framework, for the multi-hazard risk assessment of reinforced concrete buildings subjected to seismic and blast loads, is discussed in [PERSON] et al. [134]. Annual risk of structural collapse, considering seismic action and progressive collapse due to blast forces, is formulated in this study. They conclude that the Monte Carlo (MC) simulation is suitable for calculating probability of progressive collapse, as well as for identifying critical blast scenarios. Multi-hazard performance of different structural elements, such as columns, frames, plates, walls, etc., have been reported by many researchers. Resistance capacity of precast segmental columns, subjected to impact and cyclic loading, is investigated experimentally by [PERSON] et al. [135]. They found that, compared to monolithic columns, segmental columns (precast segments joined together, often with pre-stressed tendons) possess better ductility and sustain lower residual drift under cyclic loading. Under impact loading, segmental columns were found to have better self-centering capacity. They showed that shear resistance of such columns can be significantly improved by introducing concrete shear keys, but it comes at some cost related to stress-concentration and potential damage to concrete segments. [PERSON] [136] presents a methodology for the resilience assessment of buildings subjected to seismic, wind, fire, and various post-earthquake scenarios. The results of this study showed that post-earthquake fire resilience in moment frame buildings is independent of seismic damage if frame connections are intact. The results also showed that multi-hazard resilience of moment resisting frame buildings can be improved by strengthening and/or fire-proofing gravity columns. [PERSON] [137] presents multi-hazard performance evaluation matrices for retrofitted non-ductile reinforced concrete buildings subjected to seismic and blast loads. [PERSON] and [PERSON] [138] investigated multi-hazard interaction on the performance of low-rise wood-frame buildings. [PERSON] and [PERSON] [139] present an algorithm to optimize building systems, with suspended floor slabs subjected to wind and seismic hazards, and observe that tall buildings with such systems are effectively optimized for both wind and seismic forces without a significant trade-off on performance to individual hazards.
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
## 3 Meshed Feedline and Antenna  
In implementing a transparent flexible antenna, thick dielectrics and multiple layers impair transparency and flexibility. In this study, the parameters of the mesh pattern were studied by applying the mesh to the planar rectangular monopole antenna and the coplanar waveguide (CPW) feed line. The CPW feeding method is advantageous for the implementation of a transparent antenna because the ground layer and transmission line can be implemented in one layer. In addition, the monopole antenna can be implemented without difficulty even with a thin dielectric. Figure 2 shows some of the manufactured samples to be used in the study.