esa-sceva/satcom-chunk-collection huggingface dataset:
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 5 Hyperparameter Optimization and Model Training
### Bayesian Optimization  
Bayesian optimization is one of the common hyperparameter optimization algorithms [30]. The Bayesian optimization constructs the probabilistic surrogate model of the objective function based on the historical evaluation results, so as to make full use of the previous evaluation information to select the next set of hyperparameters, which greatly reduces the number of evaluations. Therefore, the Bayesian optimization takes up less computing resource and has higher efficiency than manual tuning, grid search and metaheuristics algorithms [31].  
The hyperparameters that need to be optimized in MIMT-CNN are divided into two categories. One is the size of the convolutional layer and the fully connected layer in the feature fusion processing layer, which can be used to determine the optimal attention range of the network. The other is the training parameters, including the batch size, the initial learning rate, and the training iteration. By determining the appropriate network parameters, a model with high training efficiency and accuracy can be obtained.  
For the output structure of the MIMT-CNN, the optimal classification accuracy and regression performance cannot be obtained simultaneously in general, besides it is also unreasonable to only meet the classification or regression performance alone. Therefore, in order to strike a balance between the classification accuracy and the regression performance, the black box function \(f(\lambda_{n})\) in this paper is defined as the ratio of the predicted RMSE to the classification accuracy  
\[f(\lambda_{n})=\frac{RMSE(\lambda_{n},D_{Train},D_{Validation})}{Acc(\lambda_{ n},D_{Train},D_{Validation})} \tag{15}\]  
where the combination of hyperparameters involved in the optimization is \(\{\lambda_{n}\}_{n=1}^{N}\) and the optimization space is \(\mathbb{R}^{N}\). According to the number of hyperparameters in this paper, N is determined to be 10. \(D_{Train}\) is the input of the training set and \(D_{Validation}\) is the input of the validation set.  
Bayesian optimization estimates the posterior distribution of the objective function through the selected parameter points. The distribution results are used to search for the next hyperparameter combination so as to further sample and update the posterior distribution [32]. After a specified number of iterations, the hyperparameter combination \(\lambda^{*}\) of the optimized black-box function is obtained by  
\[\lambda^{*}=\arg\min_{\lambda_{n}\in\mathbb{R}^{n^{\prime}}}f(\lambda_{n}) \tag{16}\]  
By the Bayesian optimization method, the hyperparameters of the model are determined in Table 3, according to which the model will be trained and tested based.
## 2 Experimental Apparatus and Procedure  
### Experimental Setup  
#### 2.1.1 After-Treatment System  
The vehicle used in this study was functionally normal. Furthermore, maintenance was completed in advance to ensure its suitability for analyzing the effects of DPF installation on different types of damage. A vehicle experiment was conducted with a front-wheel-drive mid-size 2.0 L diesel-fueled SUV having a monocoque body type (2011 Santa Fe (the best-selling SUV), Hyundai Motor Company, Ulsan, Korea). It is equipped with a retrofit DPF (Econix Inc., Paju, Korea), which has a matrix of compounds coating the passages (such as cordierite), and an active-regeneration system for cleaning and emptying a DPF (see Figure 2). Considering the operating characteristics of the vehicle, it is important to permit the DPF to be regenerated normally. A DOC was used as an auxiliary catalyst in the DPF post-treatment system. The main functionality of the DOC in the emission system is the generation of NO\({}_{2}\) to support the DPF operation. In this study, a DOC ceramic monolith substrate (7.5 (diameter) \(\times\) 3 (length) in, 400 cells per in\({}^{2}\)) and cordierite wall-flow type DPF (7.5 (diameter) \(\times\) 8 (length) in, 200 cells per in\({}^{2}\)) were used for the vehicle experiment. These devices were installed on a diesel SUV with a 2.0 L CRDi engine. The specifications of the diesel engine used in this study are listed in Table 1.  
As explained above, NO emissions are oxidized to NO\({}_{2}\) on the front DOC and react with the trapped PM on the DPF. The DPF has the shape of a catalytic converter and is located at the rear end of the DOC (see Figure 3). A frame-by-frame reaction of NO\({}_{2}\) with trapped PM is likely. The DPF generates a marginal back pressure during the operation. Thereby, the OBD device of the vehicle is effective for DPF diagnosis to monitor its functionality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Engine Type** & **Unit** & **D4 EA (CRDi VGT)** \\ \hline Type of fuel & - & Diesel \\ Rated power & PS/RPM & 126/4000 \\ Displacement & cc & 1991 \\ DOC + DPF & cpsi & 400 (ceramic type DOC) \\ Curb vehicle weight & kg & 1745 \\ Gross vehicle weight & kg & 2185 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engine specification of the vehicle used in this study.
# Development of Fire Consequence Prediction Model
###### Abstract  
Recently, various dynamic risk analysis methods have been suggested for estimating the risk index by predicting the possibility of accidents and damage in real time. It is necessary to quickly estimate the risk of an accident by predicting the probability and consequences of accidents, which are quantitative criteria for ship risk assessment. This study aimed to develop an algorithm for predicting the consequences of accidents in real time to perform a dynamic risk assessment and, using this algorithm, formulate a ship accident-response procedure that can be used in fire accidents during LNG bunkering. The risk of fire was estimated by predicting the hole size due to changes in the liquefied natural gas (LNG) transport conditions and the consequence based on the fire accidents scenario in LNG-fueled ships. The prediction model was trained with the hole prediction data using Ansys CFX and with the consequence data using DNV Phast, and the consequences of fire were compared and evaluated by applying the trained results. A method for estimating the size of the fire based on the predicted consequence is proposed, which supports fast decision making in fire accidents during LNG bunkering by identifying the potential size of the fire at the beginning of an accident.  
LNG bunkering; deep learning; fire consequence; hole diameter; accident consequence; support for decision making; dynamic risk analysis Article Development of Fire Consequence Prediction Model in Fuel Gas Supply System Room with Changes in Operating Conditions during Liquefied Natural Gas Bunkering.
## 2 Materials and Methods  
Bi\({}_{x}\)Sb\({}_{2-x}\)Te\({}_{y}\)Se\({}_{3-y}\) (BSTS) single crystals were grown by a self-flux method. The high-purity raw materials were weighed and mixed according to the desired stoichiometry. The specimens were heated up to 1000 \({}^{\circ}\)C for 12 h, then slowly cooled down to 500 \({}^{\circ}\)C for over 100 h, before finally being cooled to room temperature. Single crystals of several millimeters in size were obtained (see Reference [28] for more details). Here, we focus on the following compounds: Sb\({}_{2}\)Te\({}_{3}\), Bi\({}_{0.5}\)Sb\({}_{1.5}\)TeSe\({}_{2}\), Bi\({}_{0.8}\)Sb\({}_{1.2}\)Te\({}_{3}\), Bi\({}_{2}\)Te\({}_{3}\), Bi\({}_{2}\)Te\({}_{2.65}\)Se\({}_{0.35}\), and Bi\({}_{2}\)Te\({}_{0.5}\)Se\({}_{2.5}\).  
Figure 1 schematically illustrates our TARPES experimental apparatus. The laser source is a commercial Yb-based system (Pharos, Light Conversion) generating 300 fs pulses with photon energy \(h\
u=1.2\) eV (wavelength \(\lambda=1030\) nm) at 100 kHz repetition rate (for details about the optical system see Reference [29]). The laser feeds a home-built non-collinear optical parametric amplifier (NOPA) that provides ultrashort pump photons of energy \(h\
u=1.85\) eV (\(\lambda=670\) nm) with a pulse duration of 30 fs. A sum frequency generation (SFG) stage generates UV probe photons with energy \(h\
u=6\) eV (\(\lambda=206\) nm) and a pulse duration of 65 fs. The instrumental response function (IRF) given by the cross-correlation of pump and probe pulses, thus corresponds to an overall time resolution of about 72 fs. Pump and probe beams, focused respectively on spots of about 100 \(\upmu\)m and 50 \(\upmu\)m in diameter, converge on a sample placed inside an ultrahigh vacuum chamber (pressure \(\approx 10^{-10}\) mbar) and cleaved in situ before each measurement.  
Photo-emitted electrons are detected by a home-built time of flight (ToF) spectrometer equipped with a micro channel plate (MCP). Rotation of the sample's normal with respect to the analyzer axis provides the angular resolution (for more details of the ToF analyzer and acquisition system see Reference [30]). All spectra were recorded at room temperature.
## 2 Materials and Methods
### Procedure  
Each patient's HVAs, defined as the angle between the first metatararsal and proximal phalangeal axes, were measured using a goniometer in a non-weight-bearing sitting position [14] by the same physical therapist with 6 years of experience. Pain was measured by the Taiwanese version of the Brief Pain Inventory (BPI) [21; 22]. The 4-item intensity scale assesses the worst, least, average, and current pain intensity, and the 7-item interference scale assesses the interference of pain in 7 life domains (general activity, mood, walking ability, normal work, relations with others, sleep, and enjoyment of life) on a 0 to 10 rating scale.  
Plantar pressure was monitored with the F-Scan System(r) (Tekscan, Boston, MA, USA) at a sampling rate of 60 Hz. The F-Scan 3000E sensor comprised of 960 sensing elements was placed on top of the tested insole. Validity and repeatability of the F-Scan System(r) was generally acceptable [23]. The test-retest reliability was dependent on the measure and region of the foot; the forefoot and toes regions displayed good to high reliability for all plantar pressure variables, and that contact area had high reliability for all regions of the foot [24].  
The participants wore the same shoes (ARWR92280, Amor, Taipei, Taiwan) and socks and were instructed to walk at a self-selected comfortable pace along a 7 m gait way under 2 testing conditions, implemented in random order: (1) walking with FOs and (2) walking with flat insoles (FIs); a 5-min rest period between the FOs and FIs testing conditions was provided. For each testing condition, five consecutive walking trials were collected. The first and last strides were excluded, and the rest of the strides were analyzed for each trial. The plantar pressure parameters of the foot with the larger HVA were analyzed. For feet with equal HVAs, the data from the dominant leg were selected. The dominant leg was determined in relation to the foot normally used to kick a ball [25]. Eighteen participants with a total of 18 feet were selected for subsequent analyses.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 7 Conclusions  
In this work, an energy manager combined with an asynchronous MAC protocol has been proposed for energy harvesting wireless sensor networks. The proposed solution leverages two complementary technologies, energy harvesting and ultra-low power wake-up receivers, to increase the energy efficiency of wireless sensor networks and to enable energy neutrality. This new scheme is designed to be implemented on real hardware and therefore solely requires the measure of the residual energy achieving a negligible overhead. The proposed approach has been experimentally validated in the context of data-gathering sensor networks with a star topology and compared with two state-of-the-art MAC protocols. Experimental results show that a 2.5 gain in term of throughput can be achieved by SNW-MAC compared to PW-MAC The energy efficiency was evaluated using a new metric introduced in this work, the energy utilization coefficient. Moreover, the better scalability of the proposed MAC protocol compared to traditional pseudo-asynchronous MAC has been analytically demonstrated.  
All authors contributed to the main idea of this paper. [PERSON] designed the algorithms and protocols under the expertise of [PERSON] on energy management and the one of [PERSON] on wake-up radio. [PERSON] conceived and performed the experiments. The article was initially written by [PERSON] and revised by [PERSON] and [PERSON]. [PERSON] and [PERSON] supervised the overall research work, critically evaluated and revised the paper.  
This work was supported by \"Transient Computing Systems\", an SNF project (200021_157048), by the SCOPES SNF project (IZ74Z0_160481) and by the \"POMADE\" project, funded by CD22 and the Brittany region.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: Behavior of the EM and achievable throughput with variable lighting conditions.
# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 4 The Application of CT in Loess Microstructure
### Initial Stage: Exploration and Foundation  
The concept of soil microstructure was first introduced by [PERSON] in 1925, and its introduction provided a new perspective and method for addressing the macroscopic physical properties of loess-like soil. Reviewing the development of loess microstructure research, it can be observed that significant advancements at various stages have invariably benefited from progress in observational techniques and experimental equipment, as well as the emergence of new methodologies.  
In the early stages, due to the limitations of resolution as well as the constraints of scanning equipment and costs, CT technology was primarily used to explore the structural and damage evolution laws of loess, thereby reflecting its impact on the collapsibility of loess. For instance, in 2000, [PERSON] et al. [22] conducted CT scans and image analysis on loess after loading and water immersion, arguing that the infiltration channels formed by loose capillary pores in the loess were the cause of loess collapse, providing a micro  
Figure 7: Top 25 keywords of WoSCC burst-ranking.  
perspective for understanding the mechanism of loess collapse. In 2004, [PERSON] et al. [21] performed CT scans on the original loess and, after water immersion collapse tests, used damage theory to analyze the alterations within the microfabric of the loess during the tests; they then studied the hardening and yield failure process of the original loess during the triaxial shear process through CT scanning and defined various damage variables, offering new insights into the application of damage theory in loess research [104]. In 2006, [PERSON] et al. [105] utilized versatile geotechnical triaxial testing apparatus in conjunction with CT to investigate the intrinsic structural alterations within pristine Q\({}_{2}\) loess during the triaxial shear process, finding that the loess had clear initial damage and anisotropy and that CT values and variances could effectively describe the laws of damage evolution. In 2009, [PERSON] et al. [106] used CT-triple axial collapse tests to reveal the changes in the pore structure of the original collapsible loess during loading and collapse, determining its structural yield stress, proposing structural parameters based on CT values, and establishing equations for damage evolution. In 2010, [PERSON] et al. [23] studied the collapse characteristics of the original Q\({}_{3}\) loess through CT-triple axial tests and quantitatively analyzed the impact of structure on collapse using CT values.
# A Novel Suspended-Sediment Sampling Method: Depth-Integrated Grab (DIG)
## 1 Introduction
However, these samplers and methods are not widely used due to being more expensive, time consuming, and requiring more specialized equipment and training than  
Figure 1: Three suspended-sediment sampling methods: (**a**) equal-width increment (EWI) suspended-sediment sampling method with 10 verticals and a D-74 suspended sediment sampler; (**b**) depth-integrated grab (DIG) suspended-sediment sampling method with 1 vertical and a US WBH-96 suspended sediment sampler with a hole in the bottle’s cap; (**c**) Grab suspended-sediment sampling method with 1 vertical (near surface) and a US WBH-96 suspended-sediment sampler with an open-mouth bottle. grab sampling and TSS laboratory methods. For example, a weighted bottle sampler [16], commonly used for grab samples, is approximately 7 percent of the total cost of a US D-74 isokinetic sampler [17]. Technology and methods involving turbidity, acoustics, and laser diffraction (called surrogates) have shown great potential for accurately and cost-effectively estimating SSCs and particle sizes [18; 19; 20]. These methods and technology allow for increased spatial and/or temporal resolution in fluvial suspended sediment data and, once relations are developed, allow for rapid estimates of SSCs and particle sizes when collecting samples are not feasible or cost effective. However, those methods do not replace physical samples because physical samples are still used to calibrate the sensors. There is a need for alternative samplers and methods that are more representative of river conditions than Grab-TSS methods but easier and cheaper to deploy than EWDI-SSC methods. The USGS Upper Midwest Water Science Center developed a suspended sediment sampling method that combined the elements from both grab and EWDI-SSC sampling methods. The goal of the new method was to improve the accuracy compared to Grab-TSS and reduce costs associated with EWDI-SSC sampling. The developed sampling method is called a depth-integrated grab (DIG; Figure 1b). It was originally hypothesized that the DIG would not be as accurate as an EWDI-SSC because it only consisted of one vertical measurement and was assumed to be non-isokinetic. However, the results suggest that there was not a significant difference between DIG-SSC and EWDI-SSC.
# Review of Photodetectors for Space Lidars
## 2 Space Lidar Detectors from UV to near Infrared
### Silicon APDs in Geiger Mode Operation
When multiple SPADs in the same subarray are illuminated, the output pulse amplitude is roughly proportional to the total number of detected photons by the subarray. SiPMs have a large active area, single photon sensitivity, and the ability to resolve the photon number in the pulse. SiPMs require a much lower bias voltage than PMTs. They are relatively low-cost to fabricate. However, SiPMs are still Geiger mode APDs with the same nonlinear behaviors, such as afterpulsing. They have not been used in space lidars. Geiger-mode silicon APDs are more susceptible to space radiation damage since they are sensitive to single electron events [34]. Fortunately, the damage rate for silicon SPADs is still acceptable for a typical multi-year Earth science mission. For example, the dark count rate of the SPCMs on the ICESat mission increased from several hundred per second to several hundred thousand per second at the end of its 9-year mission [35], which was still much lower than the rate of solar background photons for daytime measurement.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 1 Introduction
Among the evaluated input variables, the cross relationship between the power variables under study has also been considered and exploited to improve the forecasting results. The inclusion of this information is performed using what is called an \"external baseline\" (EXBL), i.e., adding a baseline of some other variable of the microgrid (e.g., load consumption) as an input of a machine learning method; this idea is one of the proposals of the present paper. The methodology of the proposed framework is applied in a study case to forecast five different power variables (load, generation metrics of various types, and others) for the Savona Campus of the University of Genoa in Italy. The rest of the paper is organized as follows: Section 2 reviews state-of-the-art forecasting techniques for generation and load consumption. Section 3 presents the utilized methodology, which is applied in the framework proposed in Section 4. Section 5 presents the details of the case study, whose results are described in Section 6. Finally, the conclusions are depicted in Section 7.
## 3 Results
### Model Performance and Comparison
#### 3.2.3 Prediction Performance of Sentinel-2 Bands Combined with Laboratory Spectral Indices  
Combining laboratory spectral indices with the Sentinel-2 bands to calibrate models to predict SOC content influenced model performance strongly (Figure 10, Table A6 (Appendix B)). For both the RF and DNN algorithms, the validation criteria with mean RPIQ > 1.7 were obtained when including at least the two indices most correlated with SOC content (i.e., NBR2 and NDVI). However, doing so generated high variability in validation performance for the RF models (i.e., standard deviations of RPIQ of 1.13-2.10), while the validation performance of the DNN models remained relatively stable (mean RPIQ of 1.84 (\(\pm\)0.21) to 3.07 (\(\pm\)0.72)).  
Figure 9: Mean performance of the three iterations during model calibration and validation using the random forest (RF) and deep neural network (DNN) algorithms when using Sentinel-2 indices, Sentinel-1 indices, and Sentinel-1 soil moisture: (**a**) Ratio of performance to the interquartile (RPIQ); (**b**) Root mean square error (RMSE); (**c**) Ratio of performance to deviation (RPD). Error bars indicate 1 standard deviation. Horizontal red lines indicate performance thresholds (see Section 2.6.4).
## 4 Data Pre-Processing
### Execution of Synthetic Minority Oversampling Technique (SMOTE)  
After constructing the original Data U set, the class imbalance issue of the dataset was addressed using SMOTE. This approach significantly improves the distribution of each class. Consequently, newly generated instances exhibit high similarity to the original data distribution [35,36].  
Using the SMOTE method, the number of groups related to MTSA-related accidents and non-accidents within the original U set was equalized to 50.0%.  
The SMOTE Data U set, which underwent oversampling using the SMOTE technique, was then divided into a training set consisting of 1560 groups, accounting for 80%, and a validation set consisting of 390 groups, representing 20%, using random sampling. The distribution of classes in each dataset was balanced, as depicted in Figure 9.