esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Optimization of Al-Ti Oxide for Adsorption of Phosphate  
Al-Ti bimetal oxide with different Al:Ti molar ratios (1:0, 4:1, 2:1, 1:1, 1:2, 1:4, 0:1) were synthesized and their P removal capacities are compared in Figure 1. The comparison shows that sorption capacity was 50.7 mg/g on aluminum oxide (Al(III):Ti(IV) = 1:0) and 24.7 mg/g on titanium oxide (Al(III):Ti(IV) = 0:1). The adsorption capacities of Al-Ti bimetal oxide first increased and then decreased with an increase in Al-Ti molar ratio. The highest adsorption capacity was achieved at an Al-Ti molar ratio of 2:1, indicating that an appropriate amount of Al- and Ti-doping of the oxide hybrid enhanced the P adsorption capacity compared with individual oxides. The adsorption capacity of P on Al-Ti oxide was higher than that of Fe-Ti bimetal oxide, 32.95 mg/g (pH 6.8) [31]. It was also slightly higher than that of Fe-Al hydroxide, 51.8 mg/g (pH 4.5) [33], and that of Mg/Al layered double hydroxides, 54.9 mg/g (pH 6.0) [34]. A small amount of Ti doping into bimetal oxide obviously enhanced the P sorption capacity, while excessive Ti doping was detrimental to its sorption capacity. The results are similar to those reported in the literature [31]. The Ti\({}^{4+}\) ions can make the surface charges more positive while attracting more phosphate ions. However, when the Ti content continues to increase, the P adsorption capacity of the adsorbent decreases. It is speculated that the effect of Ti content on P adsorption may be the result of the interaction of surface charge and surface microstructure. Therefore, the sorbent with an Al-Ti molar ratio of 2:1 was selected and applied in subsequent characterization and adsorption experiments.
## 1 Introduction
### Research Background
Therefore, the joint deployment of an IRS and UAV as the low-altitude relay of a wireless communication system is conducive to the optimization of the performance of the system.
## 2 The Operation Characteristics of an MMC System
### DC Fault Analysis of an MMC after Blocking  
In an HB-MMC, as soon as a fault is detected, the IGBTs are blocked for their protection, but the anti-parallel freewheeling diodes still provide a path for the AC current to the DC line, thus feeding the fault. Therefore, DCCB is essentially required to block the fault current.  
An FB-MMC system has the capability of suppressing the fault current. Initially, a current surge is allowed to flow through the IGBTs and feed the DC fault. However, as soon as the IGBTs are blocked, there is only one available current path through the series and reverse-connected DC capacitors of the submodules.  
After the IGBTs are blocked, the fault current passes through the freewheeling diodes. In the process, the capacitors of FB submodules develop opposite polarity compared to the fault current during the DC fault. The complete blocking of the DC fault current takes place when the total voltage of each submodule capacitor becomes higher than the maximum peak line-to-line AC voltage, as shown in Equation (11). As a result, the fault current can be limited and a DC breaker with a low current rating can be used to isolate the faulty transmission line.  
\[\mathrm{V_{AC,max}<V_{arm,a}+V_{arm,b}}, \tag{11}\]  
where \(\mathrm{V_{arm,a}}\) and \(\mathrm{V_{arm,b}}\) represent the arm voltages which are stacked at each arm through the fault path.  
The operation of FB- and HB-MMCs under DC side faults have been extensively covered in References [10; 19; 20].
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 5 Results and Discussion
### Effect of Airgap  
A thermal pad with a conductivity of approximately 5.2 W/(mK) was inserted to minimize the temperature difference \(T_{DIF}\) resulting from the approximately 60 \(\upmu\)m airgap between the temperature-sensitive part of the sensor and the baseplate and interface mismatch. This serves to occupy the airgap and enhance the heat transfer at the PSD's baseplate and FBG interface. The process employed to obtain the thermal profile of the IGBT in Figure 9 was replicated in Figure 10, which displays a noticeable enhancement in the precision of the sensor measurements since temperatures \(T_{FBG}\) and \(T_{IR}\) are closely correlated. The green line (FBG1) overlapped with the blue line (FBG2) as expected since  
Figure 8: Observed reflection spectra for the FBG sensors on the optical spectrum analyzer.  
Figure 9: FBG temperature response to varying load power.  
IGBT1 and IGBT2 have similar thermal profiles due to their parallel connection on a common heatsink in close proximity.  
In comparison to the previous measurement, a mean squared error of 0.37 was recorded. The fluctuation in the temperature curve of the IR camera and the estimated temperature of the FBG array demonstrate that the thermal gradient across the layer of the device is dependent upon \(P_{L}\). The error bars provide a visual representation of the uncertainty in the measurements, with the maximum deviation of 1.54. This implies that the FBG sensor is a dependable technology for temperature distribution mapping in power electronic applications.
## 4 In-Loop Testing of the Control Model
### Comparative Analysis of Energy Management Strategies
It can be seen that in the experiment, the equivalent hydrogen consumption of the whole vehicle with the wavelet rule-based multi-layer decoupling control strategy is the lowest. That means it is the most energy efficient under the same operating conditions. The hydrogen consumption of the PEMFC under the multi-layer decoupling strategy in Figure 21a is lower than that of the power-following control strategy but higher than that of the fuzzy control energy management strategy. This is because the latter two do not consider the optimization of the hydrogen fuel cell operating point, and the LiFePO4 battery or ultracapacitor takes up too little or too much load demand power, so the PEMFC hydrogen consumption is increased or decreased. The equivalent hydrogen consumption of the whole vehicle based on the multi-layer decoupling control strategy with wavelet rule is the lowest throughout the experimental cycle, as shown in Figure 21b. Table 4 compares the PEMFC and the equivalent hydrogen consumption of the whole vehicle under the three control strategies at the end of the cycle. It can be seen that the equivalent hydrogen consumption of the whole vehicle is reduced by 17.06% and 5.41% for the multi-layer decoupling control strategy based on wavelet rules compared with the power following and fuzzy control strategies in the experimental cycle, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Control Strategies** & \
\begin{tabular}{c} **The Comparison of the** \\ **PEMFC/kg** \\ \end{tabular} &
\begin{tabular}{c} **The Equivalent Hydrogen** \\ **Consumption of the Whole** \\ **Vehicle/kg** \\ \end{tabular} \\ \hline power-following & 0.0194 & 0.0242 \\ fuzzy control & 0.0106 & 0.0218 \\ multi-layer decoupling control & 0.0153 & 0.0203 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hydrogen consumption results for the three control strategies. Figure 21: **(a)** Comparison of the PEMFC; **(b)** comparison of the equivalent hydrogen consumption of the whole vehicle.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 4 Experiments
### Multi-Robot Experiments
#### 4.3.1 Formation Construction  
In the formation construction experiments, eight mobile robots are deployed in the indoor scene. The robots are initialized at different positions with various environmental characteristics. We set the target formation of the robots as a rectangle; then, the eight robots will be trained together with the proposed Parallel Deep Deterministic Policy Gradient (PDDPG) algorithm. The hyperparameters of the PDDPG are listed in Table 2. In the training stage, we add the random bias to the target position values in each episode. It can enrich the formation data and reduce the overfitting problem of multi-robot training. Figure 5a,b illustrate one of the formation construction experiments in the testing stage. Figure 5a shows the initialization of the multi robot system; the robots are deployed in the indoor scene with various poses. Figure 5b shows the final results of the formation construction task. The curves with different colors represent the navigation trajectories of different mobile robots. As illustrated in the figures, all the mobile robots arrive at the target and construct the formation properly.  
Figure 4: The trajectory of the mobile robot in the single robot mapless navigation tasks. The left part is the navigation environment in Gazebo, the right part is the trajectory of the mobile robot.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 2 Fundamental Principles of Mechanics
### Particle Contact Contact Model  
In the discrete element method (DEM), the physical behavior of particles is governed by the relationship between contact forces and relative displacements, as defined by the constitutive contact model--that is, the physical model describing how contact forces arise from particle interactions. The contact constitutive model is thus the fundamental component of DEM calculations. DEM captures the macroscopic mechanical behavior of a material by simulating the microscopic motions of a large number of particles, including displacement, rotation, and collision. The contact model defines the force-displacement behavior at individual contact points, incorporating mechanical properties such as elasticity, plasticity, and viscosity. The collective response of these microscopic interactions--through statistical averaging or superposition--gives rise to the macroscopic mechanical parameters of the material, such as elastic modulus, internal friction angle, and cohesion. Therefore, the contact constitutive model serves as the essential physical link between microscale interactions and macroscale engineering behavior.  
(1) Contact constitutive model of overburden  
To more accurately capture the physical and mechanical behavior of the overburden, inter-particle mechanical interactions are modeled using the linear contact bond model (LCBM). The LCBM incorporates a range of contact parameters with strongly nonlinear relationships, while maintaining constant normal and shear stiffness at contact points. This allows it to effectively represent particle bonding and failure behaviors in overburden materials. The LCBM contact constitutive model is illustrated in Figure 1a.  
(2) Contact constitutive model of cement slurry  
Circular particles with small diameters are used to simulate stable cement slurry for grouting, and the mechanical behavior of cement particles is described using the adhesive rolling resistance linear model (ARRLM). ARRLM is a contact mechanics model based on a linear model. Its core significance is to more truly simulate the complex mechanical behavior of granular materials at the contact point by introducing the adhesion mechanism. Based on the traditional linear model, ARRLM can describe the interaction between particles more accurately by adding the coupling mechanism of adhesion and rolling resistance. Specifically, unlike adhesive materials, this model does not incorporate the concept of damage. As a result, loose yet solid cohesive particles can form, and the rolling kinetic energy of particles is dissipated through viscous forces and friction [21]. The constitutive diagram of the ARRLM contact is shown in Figure 1b.  
Figure 1: Schematic diagram of contact constitutive model (two-dimensional space). (**a**) Linear contact bond model. (**b**) Adhesive rolling resistance linear model.
## 4 Data and Methods
### SAR Dataset and InSAR Analysis  
In this study, Sentinel 1A interferometric wide (IW) single-look complex (SLC) C-band data are used, which cover 250 km swath with 5 m \(\times\) 20 m spatial resolution. The Sentinel mission, launched in April 2014 by European Space Agency (ESA) under Copernicus program, is distributed free of charge. Detailed information about the Sentinel data is given in Table 2, and the technical information about the dataset obtained from Sentinel Scientific Hub before and after the earthquake that was used in SAR analysis is given in Table 3.  
Open-source GMTSAR academic analysis software on Ubuntu (22.10 version) platform was used to process Level 1 single-look complex (SLC) data [31]. The InSAR analysis relies on estimating the phase difference given by the complex conjugate product operation between master and slave images. Analyses were carried out in two basic steps. First, the pre-process step is the co-registration between master and slave images using precise orbit file and Digital Elevation Model (DEM) data. Next is the processing step, in which co-registered data are processed with DEM for the removal of the topographic phase to develop wrapped phase fringes. After that, wrapped phase information is unwrapped to obtain displacement by using SNAPHU and, thus, results are geo-coded to obtain the LOS displacement caused by the earthquake [32]. Deformation maps created separately for ascending and descending pass directions from the SAR analysis can be found in [26].
# A New Bloom Filter Architecture
## 2 Related Works
### Bloom Filter  
A Bloom filter [19] is an efficient bit-vector-based data structure that represents the membership information of a set. A Bloom filter has an array of \(m\) bits, which are initialized with 0 s. Since a single cell of the Bloom filter is composed of a single bit, each cell can only indicates either 0 or 1. Bloom filters support two operations: programming and querying. In programming, for each element in a set, the \(k\) cells indicated by the hash indexes generated by \(k\) independent hash functions are set to 1.  
By contrast, querying is used to determine whether or not a given input is included in the programming set. In querying an input, \(k\) hash indexes are also generated using the same hash functions that had been previously used to program the Bloom filter. If any of the \(k\) bits are 0, the input is definitely not a member of the set, and thus termed a _negative_ result. If all of the \(k\) bits are 1, the input is identified as a member of the set and termed a _positive_ result. Bloom filters never generate false negatives, but they may generate false positives resulting from hash collisions.  
The false positive rate (\(f\)) is calculated as follows [20; 21], where \(m\) is the size of a Bloom filter and \(n\) is the number of programmed elements:  
\[f=(1-p)^{k}=\Big{(}1-(1-\frac{1}{m})^{kn}\Big{)}^{k}. \tag{1}\]  
Figure 1: Name prefix trie.  
The number of optimal hash functions minimizing the false positive rate can be found as follows [20; 21]:  
\[k=\frac{m}{n}ln2. \tag{2}\]  
Recently, numerous variants of the Bloom filter have been proposed [22; 23; 24; 25] and applied to various network applications [26; 27; 28]. A functional Bloom filter [24; 25] allocates multiple bits in a single cell, and hence it can return the value corresponding to an input as well as the membership information of that input by checking the stored value. In programming, for each element in a set, the return value corresponding to the element is stored in the \(k\) cells indicated by \(k\) hash indexes. In the case that two or more different values need to be programmed to a single cell by different elements, the cell is denoted as a _conflict_ value.  
The purpose of querying to a functional Bloom filter is to both return the stored value and determine the membership of each given input. The querying result can be classified as either _negative_, _positive_, or _indeterminable_[24]. In querying an input, if any of the \(k\) cells is 0, or if some of their values differ from one another, the input is definitely not a member of the programming set, and therefore termed a _negative_ result. If all of the cells, excluding conflict cells, have the same value, the input is identified as a member of the set, and the Bloom filter returns the stored value, termed a _positive_ result. If all of the \(k\) cells have _conflicts_, the membership and return value of the input cannot be identified by the functional Bloom filter, and therefore termed an _indeterminable_ result.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 2 Methods
### Loss Function  
During the training process of the edge-guided change detection network algorithm, there are two types of supervision: one for the change detection results themselves and the other for the edge information of the change results. Let \(Y\) represent the labels of the data, \(Y_{e}\) represent the edge maps of the data, \(\hat{Y}\) represent the change map results output by the network, and \(\hat{Y}_{e}\) represent the edge map results output by the network.  
The proposed algorithm supervises the change detection results using a weighted binary cross-entropy loss \(L_{WBCE}\), which focuses more on the difference between the output value and the true value, converges faster, and assigns different weights to the pixel values. Its formula is as follows:  
\[L_{WBCE}(\hat{Y},Y)=-\frac{1}{N}\sum_{i=1}^{N}[\beta y_{i}\cdot\log\hat{y}_{i} +(1-\beta)(1-y_{i})\cdot\log(1-\hat{y}_{i})] \tag{10}\]  
where \(\beta\) denotes the category balances coefficient.  
The number of pixels in the edge and background portions of the edge labels is extremely imbalanced, which constrains the ability of the cross-entropy loss to supervise the model.: Therefore, Dice Loss is used in this section to supervise the edge information of the change results. Dice Loss is used to evaluate the similarity metric between two samples, and its performance is better than the cross entropy loss when dealing with data with an imbalanced distribution of positive and negative samples. The value of Dice Loss is in the range of 0 to 1, and the larger value indicates that the similarity of the two values is higher, which is defined as follows:  
\[L_{dic}=1-\frac{2\cdot\big{|}\,Y_{e}\cap\hat{Y}_{e}\big{|}}{\big{|}Y_{e} \big{|}+\big{|}\hat{Y}_{e}\big{|}}=1-\frac{2\cdot TP}{2\cdot TP+FP+FN} \tag{11}\]  
where TP, FP, and FN denote true positive, false positive, and false negative, respectively.  
In summary, the overall loss of the network is:  
\[L=L_{bce}(Y,\hat{Y})+\lambda L_{dic}\big{(}Y_{e},\hat{Y}_{e}\big{)} \tag{12}\]  
where \(\lambda\) denotes the weight balance coefficient, which was set to 5 in the experiment.
## 2 Materials and Methods
### Stability Analysis  
Using the assumption (A.2) mentioned in Section 2.2 and expressions (6) and (17), the equation of the closed-loop state space model can be written as:  
\[L(k+1)=A_{c}L(k)+BS_{w}w(k) \tag{19}\]  
In (19), \(A_{c}=A+BS_{L}+BS_{y}C\). Based on assumption (A.1) in Section 2.2, it is obvious that the elements of the C vector are bounded. The \(A\) matrix and \(B\) vector of the model are time-invariant. According to (18), \(S_{L}\), \(S_{w}\), and \(S_{y}\) are bounded as well. The values of set points, \(w(k)\) are assumed to be always bounded. By considering linear control law and using discrete form of Lyapunov stability theorem, closed-loop poles can be computed.
## 1 Introduction
There is currently no official dataset except that for Part 5, which can be found on Kaggle ([[https://www.kaggle.com/tiend95/toeic-test](https://www.kaggle.com/tiend95/toeic-test)]([https://www.kaggle.com/tiend95/toeic-test](https://www.kaggle.com/tiend95/toeic-test)), accessed on 20 May 2022). In addition, the cloze test [1] of Part 5 can be used for the evaluation of language ability [2; 3; 4], which is a widely used task for evaluating the language comprehension ability of the NLP system [5]. As mentioned earlier, for Part 5, an officially open dataset exists in Kaggle. However, as there are only 3625 problems, it is insufficient to learn the machine. Therefore, this study proposed two data augmentation techniques using POS-tagging and lemmatizing of NLTK [6] based on the characteristics of the problems in Part 5 of TOEIC. In addition, since BERT is used in studies for different tasks [7], this study also tries to solve the TOEIC \begin{table}
\begin{tabular}{l l l l l} \hline \hline Model & BERT-Base-Uncased & BERT-Base-Cased & BERT-Large-Uncased & BERT-Large-Cased \\ \hline Accuracy & 73.46\% & 76.38\% & 75.29\% & 72.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of TOEIC-BERT, an existing study that conducted experiments with a deep learning model for TOEIC Part 5. Figure 1: Distribution of the number of people by the score for the 455 th test (implemented on 20 February 2022). problem with various versions of BERT, and through this, an excellent deep learning model can be created for solving the TOEIC problems. The contributions of this study are as follows. * Through the POS-tagging based data augmentation methodology, when solving semantic problems comprising options with similar parts-of-speech but with words that have different meanings, the intention of the problem can be better understood by comparing the options focusing on the meanings;
* Through the lemmatizing-based data augmentation methodology, when solving grammar problems where various forms of words with a similar meaning are presented, the problem can be better understood by comparing the options focusing on the grammatical relationship;
* The effectiveness of the methodologies proposed in this study was verified through experiments for each methodology and experiments according to the amount of data, and we confirmed that the data scarcity problem could be solved through this methodology. This paper is structured as follows. Section 2 reviews related work in the area of the cloze test. Section 3 discusses the proposed method, while Section 4 describes the experiments and results. Finally, Section 5 concludes the paper.