esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The second one is the imbalance issue since the naive multi-label classification method uses Sigmoid as the activation function and then turns it into d two-classification problems,using the sum of binary cross-entropy (BCE) as the loss. Moreover, in our model, the entity mentions were arranged and combined, and each position may have multi labels, which leads most positions of the matrix are label 0, and only a few labels are label 1, which causes an imbalance issue. During training, traditional BCE loss tends to identify all labels as 0 in the first few rounds, resulting in almost no relationship can be extracted in the early stage of training. Therefore, we adopted a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weights, and experiment results indicate that the new loss function can not only greatly improve the convergence speed in the early stage, but also increased the experimental results slightly. We proposed an end-to-end Chinese knowledge extraction model leveraging BERT and a handshaking tagging scheme [6]. The proposed model can alleviate the nested entities and nested relations issues in complex Chinese sentences; additionally, with the help of the pre-training model, the migration ability of the model can be improved. The major contributions of this paper are: (i) Our proposed method is able to handle named entity recognition, fixed domain and Open-domain Relation Extraction simultaneously. (ii) We adopted a new loss function defined in Formula (5). The new loss function does not turn multi-label classification into multiple binary classification problems; instead, it conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight, which can alleviate imbalance issues in multiple binary classifications. The new loss function brings speed and performance improvements. (iii) Experiments on open domain data (COER [1] and SpanSAOKE derived from SAOKE [7]) show that our method has a significant improvement over strong baselines. In the COER dataset, F1 increased from 66.36 to 79.63, and in the SpanSAOKE dataset, F1 increased from 46.0 to 54.91. Our method can obtain close performance compared with the SOTA method in the CMEEE [8] and CMEIE [9] datasets.
###### Abstract  
Recently, there has been a great effort to incorporate industrial waste into cement-based materials to reach a more sustainable cement industry. In this regard, the Bayer process of obtaining alumina from bauxite generates huge amounts of waste called red mud. Few research articles have pointed out the possibility that red mud has pozzolanic activity. In view of that, the objective of this research is to analyse the short-term effects in the pore structure, mechanical performance and durability of mortars which incorporate up to 20% of red mud as a clinker replacement. As a reference, ordinary Portland cement and fly ash Portland cement mortars were also studied. The microstructure was characterised through mercury intrusion porosimetry and non-destructive impedance spectroscopy, which has not previously been used for studying the pore network evolution of red mud cement-based materials. The possible pozzolanic activity of red mud has been checked using differential scanning calorimetry. The non-steady state chloride migration coefficient and the mechanical properties were studied too. According to the results obtained, the addition of red mud entailed a greater microstructure refinement of the mortar, did not worsen the resistance against chloride ingress and reduced the compressive strength compared to control binders.  
sustainability; red mud; microstructure; durability; chloride ingress resistance; mechanical properties; impedance spectroscopy +
Footnote †: journal: Applied Sciences
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 4 Results and Discussion
### Temperature Change Process  
As shown in Figure 8a, the closer the heater was, the higher the temperature was, and the temperature decreased from near the heater to the outer boundary in a linear distribution. The temperature in the buffer layer was kept at 30 \({}^{\circ}\)C on the 10 th day and increased rapidly on the 100 th day after heating. On day 1000, the temperature at the buffer layer close to the surrounding rock was below 30 \({}^{\circ}\)C, indicating that heat transfer and groundwater infiltration had penetrated the whole buffer layer. At 500 d and 1000 d, the temperature distribution changed little and tended to be stable. The changes in temperature with time are shown in Figure 8b; the heater did not release heat at the initial stage, and the whole repository was at a low temperature. When the heater began to release heat, the temperature at each monitoring point increased with time; after that, the temperature of each point tended to be constant as the heat release of the heater decreased. The temperature of the buffer layer near the heater soon reached 80 \({}^{\circ}\)C, and then the temperature increase slowed and became stable. The temperature in the middle of the buffer layer soon reached 50 \({}^{\circ}\)C and stabilized. Under the conditions of the thermo-hydro-mechanical coupling simulation, the water saturation had some effect on the thermal conductivity of bentonite, and the buffer layer had good thermal stability. According to the design of disposal banks and research experience in various countries, the design of disposal banks with buffer backfill materials generally limits the surface temperature of the waste body to 100 \({}^{\circ}\)C. Under the simulated conditions, the temperature of bentonite could reach the maximum within 500 days, and the maximum temperature was 92.3 \({}^{\circ}\)C, which is in line with the thermodynamic standard of limiting the surface temperature of waste to 100 \({}^{\circ}\)C [53; 54].  
Figure 8: (**a**) Variation curve of temperature with radial distance; (**b**) variation curve of temperature with time.  
Figure 7: (**a**) Variation curve of horizontal displacement with time; (**b**) variation curve of horizontal displacement with radial distance.
###### Abstract  
Simultaneous wireless power and information transmission (SWPIT) is essential for power regulation, load detection, and maximum efficiency tracking in wireless power transfer (WPT) systems. To eliminate the interaction between power and information transmission, this paper proposes a novel bidirectional communication method based on differential phase shift keying (DPSK). First, the steady-state and transient models of the series-series compensated WPT system are established to analyze the characteristics of power transmission and the behavior of shared power/information channel. Second, regarding the stability of power transmission, principles of the forward and backward communication that integrated with power modulation through the full-bridge inverter and the semi-bridgeless active rectifier are elaborated. Then, the demodulator and half-duplex communication protocol are designed. Finally, the simulation is carried out and the experimental prototype is established, which shows that the proposed DPSK method is effective and the transmission of power and information is quasi-independent. Owing to the integration of power and information transmission, the complexity, cost, and volume of the SWPIT system are reduced significantly.  
wireless power transfer; simultaneous wireless power and information transmission; bidirectional communication; differential phase shift keying +
Footnote †: journal: Applied Sciences
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 5 Proof-of-Concept Evaluation
### Framework Prototype and Validation Example
#### 5.1.2 PV, Load, Grid Profiles  
The PV DC generation was obtained from a model dependent in temperature and irradiance level as suggested by [44]. The temperature and irradiance data are measured with a sampling rate of 60 s and were taken over the year 2005 in Vienna, Austria from the satellite-derived HC3 Archives Web service [45]. A 60 s PV DC measurement profile is used as input data for the PV inverter model. This DC measurement is converted into AC power by considering an efficiency conversion factor. Active power generation data from Austrian households was provided by the project Autonomous Decentralised Renewable Energy Systems (ADRES) [46]. The measurements correspond to the year 2009 and are taken with a resolution of 1 s. On the other hand, the frequency profile was obtained from ENTSO-E Netzfrequenz [47] and corresponds to January 2018. The power of the grid is calculated from Equation (4) assuming that the measurements \(P_{bat}\), \(P_{pv}\), \(I_{load}\) are known:  
\[P_{grid}+P_{bat}+P_{pv}+P_{load}=0. \tag{4}\]
###### Abstract  
Monitoring battery voltage is important to ensure a steady supply of energy. A crucial aspect to avoid failure is estimating the voltage required by the battery load. Lead acid batteries play a vital role as engine starters when the generators are activated. The generator engine requires an adequate voltage to initiate the power generation process. This article discusses three prediction models for estimating the voltage and degradation values based on data-driven methods. The machine-learning models used were Gaussian process regression (GPR), Support Vector Regression (SVR), and Random Forest. The prediction results were compared using evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R\({}^{2}\)). The implementation of the Internet of Things (IoT) was demonstrated to collect real-time battery data using a voltage sensor and a temperature sensor as inputs for the prediction model. According to the experiment, the Random Forest algorithm provided highly accurate predictions, with an RMSE of 0.0401, MAE of 0.0241, and R-squared of 0.9651.  
Keywords:data-driven; real-time; internet of things; lead acid battery +
Footnote †: journal: Applied
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 3 Experiment
### Evaluation Indicators
_F\({}_{2}\)Score_ considers recall to be twice as important as precision, while _F\({}_{0.5}\)Score_ considers recall to be half as important as precision. _F\({}_{1}\)Score_ is commonly used. Its formulas (Equations (13) and (14)) are expressed as follows:  
\[F_{\beta}=\Big{(}1+\beta^{2}\Big{)}\frac{Precision\times Recall}{\beta^{2} \cdot Precision+Recall}, \tag{13}\]  
\[F_{1}=2\cdot\frac{Precision\times Recall}{Precision+Recall}. \tag{14}\]  
Mean Intersection over Union (mIoU): mIoU calculates the ratio of the intersection to the union between two sets. In the context of image segmentation, it refers to the two sets of true values and predicted values. It can be expressed as the ratio of the sum of TP to the sum of TP, FN, and FP. The calculation involves first computing the intersection over union for each class and then determining the mean. Its formula (Equation (15)) is expressed as follows:  
\[mIoU=\frac{TP}{TP+FN+FP}. \tag{15}\]  
Therefore, Precision and Recall focus on assessing specific characteristics of a particular classification, while Accuracy, mIoU, and _F\({}_{1}\)Score_ serve as overall criteria for judging the classification model. Hence, we evaluate the segmentation model in this paper by considering these five metrics. These metrics are widely employed for assessing the performance of building segmentation methods, facilitating quantitative comparisons and analyses of segmentation results.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Proposed Method
### R-\(\lambda\) Model  
In this section, the update of the encoding parameters is discussed based on the \(R-\lambda\) model [25]. Figure 3 exhibits the \(R-\lambda\) rate control model. Generally, the \(R-\lambda\) model contains two steps, bit allocation and coding parameter updating.  
Figure 3: The flowchart of the \(R-\lambda\) model.  
The average bits of per frame is defined as:  
\[R_{picAvg}=\frac{R_{tar}}{FR} \tag{10}\]  
where \(R_{tar}\) is the target bit, and \(FR\) is the frame rate.
# Biomaterials for Ophthalmic Applications
## 2 Biomaterials in Ophthalmology
### Contact Lenses
Excess polymer is expelled to the outside. The monomers are irradiated with UV or thermal energy to initiate polymerization. The dry lens is removed from the mold, which is discarded, and is inspected. The end of production follows the same method as that of spin-casting [27; 32]. Contact lenses must be cleaned, disinfected and stored until the next use (except for the single use ones). The need to clean them is due to the wide variety of debris that can adhere to the lens surface. Cleaning also improves the process of disinfection, reducing the number of microorganisms on contact lenses [34; 43]. Chlorhexidine, a biocide, is probably the most widely used antiseptic [44]. Thiomersal, while less effective in general, is generally more effective against fungi. A combination of chlorhexidine and thimerosal was used before toxic reactions were reported and hypersensitivity due to the lens absorption of these agents [43; 45]. Hydrogen peroxide has a broad spectrum effectiveness against bacteria, viruses and yeasts that produce radicals and can be chemically broken down into oxygen and water, making it eco-friendly. Although extremely effective in terms of antimicrobial action, it is toxic to the eyes and requires neutralization before use [46]. Multipurpose solutions (MPS) are the most used lens care systems today; they do not require other components in the lens care process once they combine cleaning, washing and disinfection in a single product [47]. MPS usually contain polyhexanide, being active against a wide range of bacteria, has a higher molecular weight than chlorhexidine, therefore it cannot penetrate the lens matrix decreasing the likelihood of potential toxic reactions or hypersensitivity. Usually, polyquaternium-1 is also used as a preservative. Nowadays, MPS products usually use two disinfectants instead of one, with the process being called double disinfection [27; 45; 48]. Contact lenses are nowadays also seen as a tool for ophthalmic treatments that require the delivery of a drug to the eye. Eye drops have a low bioavailability of common ophthalmic treatments; therefore, new lenses are being developed based on drug delivery materials [49; 50]. Several methods are being used, namely drug-loading by nanoparticle or polymeric encapsulation [51; 52; 53; 54], \(\beta\)-cyclodextrintrim delivery [55; 56], molecular imprinting [57; 58], and solution soaking [59; 60; 61]. These are ongoing research works; however, more studies are needed including clinical trials or tests that assess the feasibility of manufacturing.
## 3 Proposed Method
### Optimization Objectives
#### 3.1.2 Snr  
SNR is defined as the ratio of signal strength and noise strength. In the classic MTS, the SNR has the following functions: (1) serves as the basis for selecting key variables; (2) measures the performance of the MTS; and (3) increases the performance of the MTS. SNR is divided into three categories, namely nominal-the-best SNR, smaller-the-better SNR, and SNR\({}_{\text{LB}}\). Conventional MTS only calculates the SNR for abnormal samples. Considering that abnormal samples with a large MD and small variance are easily identified, SNR\({}_{\text{LB}}\) is used. The calculation method for abnormal samples is as follows:  
\[\eta_{j}=-10\lg\left(\frac{1}{n-m}\sum_{j=n+1}^{n}\frac{1}{MD_{i}}\right) \tag{21}\]  
Moreover, scholars maintain that the SNR of normal samples must be considered [36]. The calculation equation is as follows:  
\[\eta_{i}=-10\lg\left(\frac{1}{m}\sum_{i=1}^{m}\frac{1}{MD_{i}}\right) \tag{22}\]  
According to the characteristics of the MTS, the _MD_ of normal samples must satisfy the following equation:  
\[\frac{1}{m}\sum_{i=1}^{m}MD_{i}=1 \tag{23}\]  
Then \(\frac{m}{\frac{1}{\text{{{{{\frac{{{\frac{{\frac{{\frac{M_{1}_{1}}}{}{}}}{{ \text{{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{ }}}}}}}}}}}}}}}}}}}}}} \leq\frac{MD_{1}+MD_{2}+\cdots+MD_{m}}{m}=1\); i.e., \(\frac{1}{MD_{1}}+\frac{1}{MD_{2}}+\cdots+\frac{1}{MD_{m}}\geq m\). Therefore, Equation (22) is smaller than or equal to zero. According to the aforementioned analysis, we learned that abnormal samples and normal samples with a large SNR lead to favorable classification performance. Thus, the second and third objectives of model optimization are to maximize Equations (21) and (22).
## 1 Introduction
### Purpose of the Study  
In the past, vehicle durability tests were typically conducted at the proving ground (PG) to evaluate the durability of vehicle actuator systems. These tests are advantageous because they are carried out under real-world conditions, allowing for an accurate evaluation of the vehicle's durability and its components. However, there are issues with the long duration of the tests and their susceptibility to weather and other external factors. As a result, Computer-Aided Testing (CAT) technology has been developed to simulate driving conditions in a lab setting, offering improved test reproducibility and cost reduction.  
To verify the suitability of vehicle design, three tests are necessary: a lab simulation test, target durability test, and consumer environmental test. If a design passes all three tests, it is deemed suitable. However, the consumer environmental test, which is conducted under real driving conditions, is time-consuming. The main goal of the traditional durability evaluation method is to obtain the load road profile. However, in the case of electronically controlled suspension, the load condition of the system is not constant but varies significantly depending on the road surface condition and driving condition. This variability, particularly the impact of the road surface and driving condition, is a critical factor that the suspension system control algorithm must account for. In other words, a load profile that considers the appropriate control pattern for the actual road is required, which explains the necessity of RLDA-based simulation.  
The reliability of electronically controlled suspension systems is determined by the load conditions generated according to driving conditions as additional input conditions of the control system. Therefore, unlike the durability evaluation mode of general mechanical parts, an evaluation mode should be developed by identifying the characteristics of the control system that occur in general conditions. This study proposes a lab simulation environment and operating conditions corresponding to the severity of conditions to establish durability specifications and system reliability criteria for electronically controlled suspension systems and develop a reliability evaluation mode for electronically controlled dampers. Based on this evaluation method, the performance degradation phenomena and causes of electronically controlled dampers were analyzed, and the primary direction for defining durability specifications was proposed.
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 4 Simulation Studies
### Comparison with Other Algorithm Optimization Results  
The optimization results obtained using traditional GA and the improved GA are compared in Figure 9, with both algorithms starting from the same initial solution. In the optimization process, the traditional GA utilizes a roulette wheel selection operation, while the improved GA employs tournament selection. Additionally, improved GA incorporates a weighting factor \"\(a\)\" in the crossover and mutation operations as well.  
From Figure 9a, it can be observed that the paths generated by the traditional GA for UAVs are highly disorganized. The UAVs need to return to the parking point for battery replacement to continue performing the task. In contrast, the results obtained from the improved GA exhibit significant improvement, as shown in Figure 9b. It is not necessary for UAVs to return to the parking point for recharging. However, it can be noted from Figure 9b that some paths are still suboptimal, such as the partially intersected paths of UAV3.  
The optimization results obtained by ACO are shown in Figure 10. Overall, the paths observed are reasonable, and the results are superior, with fewer instances of UAVs deviating from the optimal paths. Analyzing the distribution of the formed paths, it can be observed that the quality of solutions obtained from ACO is higher compared to the aforementioned GA. This is attributed to the influence of heuristic factors and pheromones in ACO. The initial value of the heuristic factor is inversely proportional to the distance between task points, and the pheromone gradually stabilizes with iterations. Both factors jointly affect the probability of selecting a task point, making the search for solutions more informed. Consequently, the algorithm considers the impact of distance to a certain extent, making the search for solutions more efficient.  
To validate the superiority and generality of the proposed algorithm, ten independent runs were conducted for each algorithm. The statistical results of the relevant data are summarized in Table 5. Representative fitness curves for each algorithm are selected to illustrate their variation, as shown in Figure 11. From Table 5 and Figure 11, it can be observed that the ACO-GA proposed in this paper initially exhibits significantly higher quality of initial solutions compared to GA. As the iterations progress, its search capability surpasses that of ACO, resulting in smaller average optimization values compared to other algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & **UAV1** & **UAV2** & **UAV3** & **UAV4** & **UAV5** \\ \hline Operating time (s) & 708 & 577 & 504 & 586 & 545 \\ Flight time (s) & 75.88 & 209.02 & 277.46 & 194.49 & 221.11 \\ Total time (s) & 783.88 & 786.02 & 781.46 & 780.49 & 766.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The time spent by each UAV.  
Figure 9: Optimization results of GA and Improved GA: (a) GA; (b) Improved GA.
## 2 Mineralization Test of CO\({}_{2}\) and Fly Ash
### Data Handling  
During the mineralization reaction process between fly ash and CO2, the efficiency of CO2 mineralization is commonly calculated using the following empirical formula [30]:  
\[\eta=\text{(actual amount of CO2/theoretical mineralization amount of CO2) }\times 100\% \tag{1}\]  
The ideal mineralization amount of CO2 is calculated using Formula (2):  
\[\text{CO2 Theo}=0.785\text{CaO}\%+1.09\text{MgO}\%+0.93\text{K}_{2}\text{O}\% \tag{2}\]  
The actual mineralization amount of CO2 is determined as the percentage of the decomposed mass of CaCO3 and the initial dry fly ash mass, according to Formula (3):  
\[\text{CO2}_{\text{real}}=\left(\Delta_{\text{m500-m850}}\circ\text{c}[\text{g}] /\text{m}_{105^{\circ}}\text{c}[\text{g}]\right)\times 100\% \tag{3}\]  
The formula for calculating compressive strength is as follows:  
\[\sigma_{\text{C}}=P/A \tag{4}\]  
In Formula (4), \(\sigma_{\text{C}}\) represents the uniaxial compressive strength (UCS), the unit of \(\sigma_{\text{C}}\) is MPa. \(P\) represents the maximum axial pressure in the event of specimen failure; the unit of \(P\) is N. \(A\) represents the cross-sectional area of the specimen; the unit of \(A\) is mm2.