esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Task Allocation  
In a conventional task allocation problem with a group of \(i\) agents and a set of \(j\) tasks, each agent can execute a single task and each task can only be assigned once. The process costs and rewards depend on the kind of task and agent. The goal is to minimize costs with the maximum reward and efficiency. Recently, numerous methods have been developed to resolve the multi-agent task allocation problem with either the centralized or distributed algorithm. In distributed multi-agent task allocation problem, the task allocation algorithm can affect the overall performance as one of the leading research topics. The task allocation problem is a dynamic decision-making problem because it depends on time and changes of environment and/or mission requirements [18]. Adding specific requirements will also increase the complexity of the task allocation problem [19]. Many solutions had been proposed to use a smart multi-agent system for task allocation. These solutions focused on two topics. The first is to assign a set of tasks to a group of agents successfully. [PERSON] et al. [20] shows an incremental task allocation algorithm based on the Contract-Net protocol. They proposed a parameter in order to balance the workload between the different robots and to control the triggering of the auction process. Second, agents formed by operators need to reach an optimal and robust consensus for mission requirements. In 2008, [PERSON] et al. [14] and [PERSON] et al. [21] proposed the CBAA method with a market-based decision strategy to solve the distributed multi-assignment problem. In 2011, [PERSON] et al. [15] extended CBAA to a multi-team structure on the distributed multi-assignment problem and assigned a sequence of multiple tasks by one task to one UAV at a time. In 2014, [PERSON] et al. [22] proposed another extension method to reduce the number of communication volume to complete a task allocation process used in unmanned vehicles. [PERSON] et al. [23] applied this problem to the military to develop the task allocation of wandering ammunition group and considered the unique attack constraints in the bomb combat mission.  
Based on [PERSON] et al. [15], we propose a committee-based dynamic task allocation algorithm not only to keep CBAA's advantage of reaching an agreement within the distributed system but also improve it to allow each task to be assigned to autonomous multi-vehicle in the committee to reach consensus. In our autonomous vehicle system, we define the target as the task and a autonomous vehicle as an agent.
## 2 Experimental and Numerical Method
### Experimental Conditions  
Using the mass flow rate measured using a pressure gauge and the jet velocity, the jet momentum injected from the slit is expressed as the jet momentum coefficient (\(C_{j}\)) as shown in Equation (1).  
\[C_{j}=\frac{\dot{m}V_{jet}}{\frac{1}{2}\rho SV_{R}^{2}} \tag{1}\]  
\(\dot{m}\) is the mass flow rate, \(V_{jet}\) is the velocity of the jet injected from the slit, \(\rho\) is the density, \(S\) is the surface area of the propeller, and \(V_{R}\) is the relative velocity at r/R = 0.7 of the propeller. The jet velocity from the slit can be obtained through the flow rate.  
Since the pump is attached to the towing carriage and moved together, the volume of water entering the pump varies depending on the towing speed. As a result, the jet momentum coefficient changes for each advance coefficient. The average jet momentum coefficient of the experimental conditions is 0.00420, and the jet momentum coefficient obtained using a pressure gauge is set equal to the computation conditions.  
Table 2 shows the experimental conditions. The experimental conditions were selected to increase the Reynolds number as much as possible within the allowable measurement range of the POW equipment. The Reynolds number used in this paper can be defined as Equation (2).  
\[Re=\frac{c\sqrt{U^{2}+(2\pi nr_{0.7R})^{2}}}{\
u} \tag{2}\]  
\(U\) is the advance speed of propeller, \(n\) is the rate of rotation (in RPS) and \(\
u=\rho/\mu\) the kinematic viscosity. In this study, RPS was fixed at 8 RPS, and the advance coefficient (\(J_{A}=U/nD\)) was changed by the advance speed. Due to the simple shape of the propeller, the thrust is a negative value after \(J_{A}=0.6\). Therefore, the experiment was performed only up to \(J_{A}=0.6\).
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Image Compression
#### 3.4.1 Direct Modeling  
Direct modeling is implemented with the use of stationary context maps. This type of map takes as input a context value and outputs a weighted stretched probability and a weighted probability centered around zero (skewing). It is implemented using a direct lookup table where each entry stores a probability (which is then stretched and skewed) and a hit counter. On the update phase, an error is computed as the difference between the stored probability and the value of the bit. The error is weighted with a value dependent on the hit counter. Fewer hits on the context value indicate a more rapid update rate. This is implemented via a lookup table containing the values of an inverse linear function of the hit count.  
For each context that requires direct modeling, a new map must be created. This protects the contexts from colliding with each other.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
###### Abstract  
While traditional i-vector based methods are popular in the field of speaker recognition, deep learning has recently found more and more applications to the end-to-end models due to its attractive performance. One effective practice is the integration of attention mechanism into the Convolution Neural Networks (CNNs). In this work, a light-weight dual-path attention block is proposed by combining the self-attention and Convolutional Block Attention Module (CBAM), which helps to capture more multi-source features with neglectable extra time expense. Additionally, a Weighted Cluster-Range Loss (WCRL) is proposed to enhance the identification performance of Cluster-Range Loss (CRL) on indecisive samples. Besides, to address the low efficiency in the initial training stage of CRL, a novel Criticality-Enhancement Loss (CEL) is also presented. Both of the proposed loss functions could significantly promote the training efficiency and globally improve the recognition performance. Experimental results are presented to show the effectiveness of the proposed scheme, which achieves a competitive top-1 accuracy of 92.0%, top-5 accuracy of 97.6%, and Equal Error Rate (EER) of 3.5% on the VoxCeleb1 dataset.  
speaker recognition; attention; loss function Article
## 2 Materials and Methods
### Description of Test Sites
#### 2.1.3 Site C: South Chungcheong Province  
We chose a tunnel construction site in South Chungcheong Province for the tunneling-induced ground deformation analysis. The site has notable geologic characteristics, as follows. (a) This AOI is a reclamation area, which had been subjected to dramatic land development, transforming itself into a coastal industrial zone which houses several manufacturing factories and steel mills. (b) The site is located seaside close to a shore. (c) The geologic strata are composed of a Songak genesis rock formation and a Pyeongtaek migmatitiseis rock formation with overlaying Quaternary alluvial deposits. In the chosen site, excavation of a vertical shaft for the tunnel construction using a shield tunnel boring machine (TBM) began in 2017. The excavation caused excessive inflow of groundwater into the excavated vertical shaft and called pumping-out of the inflowing groundwater with intermittent suspensions of excavation works. After 13 months, the horizontal tunneling of the TBM started. However, when the TBM advanced approximately 95 m after 3 months, it was again temporarily terminated, as damages in neighboring structures and ground cracks along the roads near the vertical shaft were reported, as shown in Figure 2. Table A2 briefly outlines the construction sequence of the tunnel construction schedule.  
Figure 2: Tunnel alignment and vertical shaft location. Roads A and B intersect near the vertical shaft. Photos were taken in June 2019. Road pavement cracks (photo (**e**)) and masonry fences cracks (photos (**c**,**d**,**g**,**h**)) were observed during the field investigation. Path sections near the vertical shaft were either depressed or raised (photo (**f**)).
## 2 Materials and Methods  
### Assimilation of Agricultural Data Sources by Remote Sensing  
In recent years, DA has made remarkable progress in agricultural practice fields such as crop monitoring, yield prediction, and evaluation [53; 54; 55; 56]. On the one hand, RS data, as an important data source of DA, play an important role in promoting scientific agricultural decision-making; on the other hand, RS data quality, acquisition, management, full chain error, and scale effect all constitute a key bottleneck restricting the effectiveness of DA [48]. This means that selecting and evaluating the universality of RS data is very important to maximize the accuracy of crop yield prediction and agricultural management decision-making. Fortunately, with the rapid development of RS technology, the spatial resolution has kept from several hundred meters in the past to several meters or even better today, which not only brings unprecedented spatial detail richness and feature recognition refinement to DA but also greatly broadens its depth and breadth in agricultural applications (see Figure 3). In this context, Multispectral data [57; 58; 59], Hyperspectral data [60; 61], Visible light and near-infrared spectra [62; 63; 64; 65; 66], as well as various types of RS data such as LiDAR [67; 68], are being widely and creatively integrated into the DA process to more comprehensively and accurately characterize the interaction between crop growth status and the environment [69].
## 3 Materials and Methods
### Methods
#### 3.2.4 ICP-MS/ES  
Additional chemical analyses were performed at a certified laboratory (ISO 17025) Activation Laboratories Ltd. (Actlabs) in Kamloops, BC, Canada. Two analytical programs available from Actlabs, the 4B2 Research and 1B2-Fire Assay were used for analysis. In the 4B2 Research approximately 10 g of sample was ground and then mixed and fused with lithium metaborate/tetraborate and then dissolved in a mixture of aggressive acids. Oxides and rare earth elements (REE) were analyzed in this program. In the 1B2-Fire Assay program, approximately 50 g of sample was ground and fused with nickel sulphide (NiS). Platinum group elements (PGEs) were analyzed in this program.  
All samples in programs were carried out using ICP-MS (Inductively coupled plasma mass spectrometry) and ICP-ES (Inductively Coupled Plasma Emission Spectroscopy) methods. The level of accuracy and analytical precision can be found at (www.actlabs.com; accessed on 24 July 2022). The laboratory (Actlabs) carried out quality control by performing duplicates analyses, analytical blank, and using reference materials. Loss on ignition (LOI) was calculated by weight difference after ignition at 1000\({}^{\circ}\). All chemical analyses can be found in Table A1.
## 5 Simulation Results and Discussion  
The simulation results are performed using Simulink environment of MATLAB (R2018b) environment to check the applicability of the developed HGD based AOSMC. The solar array system is connected with load using DC-DC buck-boost converter. Solararray system used for the simulation in this study has 16 PV modules. The specification parameters of a single PV module are given in Table 2 and the specification of the buck-boost converter and designed constants of the proposed controller are listed in Table 3. Section 5 results are presented and discussed into two sections. The results under varying irradiance levels are given in Section 5.1 and results under varying temperature levels are shown in Section 5.2.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 3 Materials and Methods
### Capsule Network-Based Module  
We fed the vector X generated by the transformer module into the capsule module, and the architecture of the third part of the proposed model is shown in Figure 5. First, we used a convolutional layer to process the feeding vector X. Conv1 had a 9 \(\times\) 9 convolutional kernel with a stride of 1 and an activation function of ReLU. This layer converted pixel intensity into activities of the local feature detector, which were then used as input for PrimaryCaps.  
The PrimaryCaps module was a convolutional capsule layer containing 32 channels of convolutional 8D capsules. Each primary capsule contained 8 convolutional units, a 9 \(\times\) 9 kernel, and a stride of 2. PrimaryCaps had [32 \(\times\) 6 \(\times\) 6] capsule output. Each layer of the DigitCaps module had a 16D capsule, and each capsule received input from all capsules in the layer below.  
We used dynamic routing between PrimaryCaps and DigitCaps, as shown in Figure 5. For the capsules, the input \(u_{i}\) and output \(v_{j}\) of the capsule were vectors. We applied a transformation matrix \(W_{ij}\) to the capsule output \(u_{i}\) of the previous layer, and then calculate the weighted sum \(s_{j}\) of the weight \(c_{ij}\). \(c_{ij}\) was the coupling coefficient calculated by the iterative dynamic routing process.  
\[\hat{u}_{j|i}=W_{ij}u_{i} \tag{9}\]  
\[s_{j}=\sum_{i}c_{ij}\hat{u}_{j|i} \tag{10}\]  
We applied a squashing function (Equation (11)) to ensure that short vectors shrank to almost zero in length and long vectors shrank to almost one length.  
\[v_{j}=\frac{\left|\left|s_{j}\right|\right|^{2}}{1+\left|\left|s_{j}\right| \right|^{2}}\frac{s_{j}}{\left|\left|s_{j}\right|\right|} \tag{11}\]  
Figure 5: The third part of ViT-Cap model architecture.  
Prediction vector \(\hat{a}_{j|i}\) was the prediction of the output of capsule \(j\) from capsule \(i\). If the activity vector was very similar to the prediction vector, we concluded that both capsules were highly correlated. This similarity was measured using the predicted scalar product and the activity vector.  
\[b_{ij}\leftarrow\hat{a}_{j|i}\cdot v_{j} \tag{12}\]  
The coupling coefficient \(c_{ij}\) was calculated as the Softmax of \(b_{ij}\).  
\[c_{ij}=\frac{expb_{ij}}{\sum_{k}expb_{ik}} \tag{13}\]  
To make \(b_{ij}\) more accurate, it was iteratively updated in 3 or 4 iterations in finger vein recognition experiments.  
\[b_{ij}\gets b_{ij}+\hat{a}_{j|i\cdot v_{j}} \tag{14}\]  
Finally, the model-encoded finger vein image was fed into a dense layer with a Softmax activation function that mapped the embedded vector to the desired class in one-hot format.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## Discussion  
### Overall Increasing Trend in Tropical GPP  
The results of this study showed that the mean GPP of vegetation in the tropics exhibited an increasing trend from 2001 to 2020. Among them, the tropical regions located near the equator had the highest GPP, with GPP unit area estimates exceeding 2000 g:cm\({}^{-2}\)year\({}^{-1}\), which is consistent with what has been reported so far [16; 17; 46].  
Figure 8: Comparison of impacts and contribution rates of climate change and land cover change on the total GPP of different land cover types from 2001 to 2020.  
[PERSON] et al. showed that GPP increased by 3.9% per decade. The GPP obtained in this experiment increased by 7.82% over the 20-year period, with an average increase of 3.91% per decade [47], which is basically consistent with the experimental results of [PERSON] et al. The regions with more significant growth are mainly located in South America, Central Africa, and parts of South Asia. While [PERSON] et al. found that most of the vegetation GPP in the highlands showed a decreasing trend [48], the higher altitude areas in this experiment, such as the Andes in western South America, also showed a decreasing trend in the vegetation GPP; however, the GPP in the plains increased more rapidly, which is hypothesized to be affected by topographic factors. Overall, the regions with increasing trends in GPP in the tropics are mostly north of the equator, and the regions with decreasing trends are mostly south of the equator. Among them, the change in GPP in the Australian region is not significant, while the Southeast Asian region primarily exhibits a decreasing trend, which is somewhat consistent with existing studies [18; 19].
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 1 Introduction
The push-out test study is the base of the finite element simulation, which can verify whether the finite element simulation is correct, and the FEM is more convenient for parameter analysis, which can supplement the shortcomings of experimental parameters. Therefore, FEM has become one of the important means to study the mechanical properties of bolted shear studs and steel concrete composite components. [PERSON] [17] focused on finite element simulations of push-out tests with headed shear studs embedded in UHPC, with the goal of investigating the engineering parameters concerned with a novel bridge repair method. [PERSON] [18] developed a three-dimensional finite element model to represent the real physical behavior of complex shear wall structures. The analysis escalates with parametric variation in the reinforcement ratio, compressive strength of the concrete wall, layout of the shear stud, and yield stress of the infill steel plate. In a study of headed studs by [PERSON] [19], a finite element model was validated by a series of push-out tests to study the shear behaviors of headed studs in steel-UHPC composite slabs. A three-dimensional numerical model was validated using previously conducted experimental tests and was used as a reference for a parametric study by [PERSON] [20]. The height to diameter ratio, friction coefficient of the steel-concrete interface, and concrete strength were selected as the main variables for the parametric study, along with the effects of the bolt-hole clearance. According to the existing finite element research results involving multi-parameter analysis, researchers usually use a single-factor rotation method, and the parameters are not always preferably set. This situation directly leads to a huge number of calculated quantities, and the results are not easy to sort and analyze, which greatly wastes computing resources and exacerbates time consumption. The goals of the present work are to explore a more efficient, fast, and economic research method, and at the same time obtain meaningful results. Then, the shear performance of precast shear studs is researched, based on the combination of push-out tests and finite element simulation, and the orthogonal design method by Japanese statistics expert [PERSON] [21] is brought into the multi-parameter finite element simulation program.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 6 Conclusions
**Nomenclature**  
\(U_{g}\), \(U_{g\text{ref}}\)  Actual and reference voltage of the unit  
\(n\), \(n_{\text{ref}}\)  Actual and reference unit speed  
\(P_{g}\), \(P_{g\text{ref}}\)  Actual and reference active power  
\(U_{\text{dc}}\), \(U_{\text{dcref}}\)  Actual and reference DC voltage  
\(Q_{\text{I}}\), \(Q_{\text{Iref}}\)  Actual and reference reactive power of the GSC  
\(Q_{R}\), \(Q_{\text{Rref}}\)  Actual and reference reactive power of the RSC  
\(y\), \(y_{\text{ref}}\),\(y_{\text{max}}\)  Actual value, reference value, and maximum value of the guide vane opening  
\(P_{\text{m}}\), \(P_{\text{s}}\), \(P_{\text{g}}\)  Mechanical power of the unit, the active power delivered to RSC, and the active power delivered to the grid  
\(R_{\text{g}}\), \(L_{\text{g}}\)  Incoming resistance and filtering inductance of the GSC  
\(\omega_{g}\)  Unit speed per unit value  
\(J_{\text{T}}\)  Rotational inertia of the rotor  
\(e_{\text{d}}\), \(e_{\text{q}}\)  Grid voltage d-axis and q-axis components  
\(u_{\text{gd}}\), \(u_{\text{gq}}\)  d-axis and q-axis components of the output voltage of the GSC  
\(i_{\text{d}}\), \(i_{\text{q}}\)  d-axis and q-axis components of the input current of the GSC  
\(e_{\text{gd}}\),\(i_{\text{gd}}\)  Grid voltage and active current  
\(\omega\)  Grid angular frequency  
\(f_{e}\)  Total loss coefficient  
\(i_{\text{dref}}\), \(i_{\text{qref}}\)  Reference values of \(d\)-axis and \(q\)-axis current, respectively. \(\theta_{pll}\)  Output angle of PLL  
\(k_{\text{gR}}\), \(T_{\text{gR}}\)  Gain of PWM and modulation wave period  
\(k_{\text{pp}}\), \(k_{\text{ip}}\)  Proportion and integral gains of the d-axis outer loop  
\(k_{\text{p1}}\), \(k_{\text{i1}}\)  Proportion and integral gains of the d-axis inner loop  
\(k_{\text{w}}\)  Speed measurement gain  
\(k_{\text{p2}}\), \(k_{\text{i2}}\)  Proportional and integral gains of the q-axis inner loop  
\(k_{\text{pq}}\), \(k_{\text{iq}}\)  Proportional and integral gains of the q-axis outer loop  
\(b_{p}\)  Guide vane opening deviation adjustment coefficient  
\(k_{p}\), \(k_{i}\), \(k_{d}\)  Speed control proportional, integral, and differential gains  
\(k_{\text{pm}}\), \(k_{\text{dm}}\), \(k_{\text{dm}}\)  Proportional, integral, and differential gains of the servo mechanism  
\(Y_{\text{pid}}\)  Output of the speed of power control PID  
\(T_{\text{o}}\), \(T_{\text{C}}\)  Water gate opening and closing times are constant. \(T_{2}\)  Power delay time  
\(k_{\text{pg}}\), \(k_{\text{ig}}\)  Proportional and integral gains of the power control  
\(H_{0}\), \(H_{d}\), \(H_{\text{loss}}\)  Initial water head, dynamic water head, and water head loss  
\(T_{\text{w}}\)  Rated water hammer efficiency time constant  
\(Q_{d}\)  Dynamic water flow  
\(\eta\)  Pumping efficiency
### Contribution  
As mentioned above, there are conventional and swarm intelligence-based MPPT techniques in which a few deal with uniform irradiance conditions, and others deal with partial shading conditions in the literature. However, choosing a suitable MPPT method might be confusing because each technique has its own set of advantages and disadvantages. Consequently, a thorough review of these methods is necessary.  
Recognizing the crucial importance of MPPT, various researchers made an effort to combine their findings and present them as reviews in [73; 74; 75; 76; 77], taking into account the function of MPPT in solar PV systems. The approaches discussed in the literature cannot be conclusive because these reviews are out-of-date and date back to before 2017. Additionally, in recent years, we have witnessed a notable rise in novel approaches for both traditional and optimization techniques. A critical assessment and analysis are, therefore, essential to complete the MPPT study with current methodologies and to cover the undiscovered ways.  
For this purpose, it requires writing a review paper on recent MPPT methods based on uniform and nonuniform irradiance conditions. This review paper provides MPPT techniques classifications based on the tracking MPP under normal and abnormal conditions, such as offline, online, and intelligent MPPT techniques. It furnishes the complete details and comparison of these techniques. This paper also provides a comprehensive evaluation of each technique based on the following factors: (1) tracking accuracy, (2) efficiency, (3) complexity, (4) parameter tuning, and (5) PV array dependency. Furthermore, this review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for future research and development in the field of MPPT. The rest of the paper is organized as follows.  
After this introduction part, the solar PV modeling and its characteristics are presented in Section 2. Section 3 presents MPPT techniques classifications and Section 4 deals with the comparative study of various MPPT techniques and their merits and demerits of MPPT techniques. Finally, Section 5 presents the conclusion of this research study.