esa-sceva/satcom-chunk-collection huggingface dataset:
# Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
## 3 Methodology
### Hypothesis Development  
This study focuses on investigating the behavioral intentions of users regarding their readiness to adopt ERS. Specifically, it seeks to identify and understand the factors that shape this behavioral intention, making the behavioral intention (BI) for ERS adoption readiness the dependent variable in this study.  
Furthermore, the study aims to assess whether the existing infrastructure, policies, and programs are adequate to facilitate ERS adoption. In this context, \"facilitating conditions\" (FC) from UTAUT [49] is introduced as the first set of independent variables. It is important to recognize that ERS practitioners and engineers must innovate in alignment with the infrastructure supporting ERS utilization. Prior research has indicated the significance of FC when there are sufficient infrastructure and policies encouraging technology adoption [64; 60; 65; 66; 67]. It is crucial to note that FC directly impacts the intentions of individuals to either embrace or reject the technology.  
The second determinant, attitude (AT), is derived from TPB [47]. The concept of attitude has been a fundamental factor in the adoption of various technologies, as it plays a pivotal role in gauging an individual's readiness to engage in a specific behavior [68; 69; 70], such as the adoption of ERS. This is because an individual's attitude is a key internal factor that influences their willingness to embrace new technologies [52; 53; 68; 69; 70]. Thus, it is imperative to assess the attitudes of Malaysian youth towards ERS.  
The third determinant, subjective norms (SN), the second antecedent from TPB [47], is another component proposed in this study, representing the perceived social pressure that influences an individual's decision to engage in a particular behavior [69; 70; 71]. It suggests that exposure to ERS can help in demonstrating its utility and foster its adoption among one's social circle.  
The fourth determinant, perceived behavioral control (PBC), the final antecedent from TPB [47], pertains to an individual's assessment of their own ability to carry out the behavior. As discussed earlier, a person's capability and proficiency in using technology significantly impact their likelihood of adopting it [68; 69; 70]. In the context of this study, the goal is to ascertain whether Malaysian youth possess the necessary skills and abilities to effectively use ERS technology.  
The final determinant identified is awareness (AW). Awareness has been recognized as a significant factor influencing users' behavior in the adoption of technology, as suggested by prior studies [72; 73; 74; 75]. It plays a crucial role in shaping users' willingness to engage with technology.  
Additionally, this study proposes the possible moderating effects, of technology aptitude (TA), derived from the adopter categories from DOI [48]. Specifically, it is suggestedthat there might be moderating effects of TA towards the relationships between FC, AT, SN, PBC, and AW regarding BI.
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 2 Proposed Fish Counting Algorithm
### Theoretical Background
#### 2.1.3 Fish Count Estimation  
Theoretically, fish density is obtained by diving \(s_{a}\) by \(\sigma_{bs}\). However, there can be multiple targets of different sizes and spices in a water body. Therefore, the mean acoustic backscattering cross section of targets denoted by \(<\)\(\sigma_{bs}\)\(>\) is used. The target density, \(\rho_{a}\), expressed as the number of fish per unit surface area is given by  
\[\rho_{a}=\frac{s_{a}}{<\)\(\sigma_{bs}\)\(>} \tag{4}\]  
Finally, the fish count, \(N\), within an area of \(A\) can be calculated as,  
\[N=A\rho_{a} \tag{5}\]  
Note that \(N\) is the estimation for a layer or a water column only. To estimate fish for a depth range, a summation of estimation of all the layers between the range is required.
## 2 Materials and Methods
### BN Algorithms  
Bayesian Network (BN) is a non-parametric model that represents multivariate probability distributions in the form of a directed graphical model [70]. In a BN model, a set of random variables is represented by the network's nodes, as well as the causal relationships between variables denoted by directed arcs. Each conditional probability distribution is associated with a node that quantifies the influences the parents of the node have on it. It worth noting that a BN model denotes the casual relationships between nodes; however, the arcs in the network do not necessarily denote direct cause and effect. The Naive Bayes is the simplest form of the BN model. The Naive Bayes has two basic assumptions as follows: (1) all other input variables are conditionally independent of each other given the target variable and (2) all other input variables are directly dependent on the target variable. However, these assumptions are unrealistic [71]. To relax the first assumption and achieve better performance over the Naive Bayes, the Tree Augmented Naive Bayes (TAN) approach was introduced by [PERSON], et al. [72]. TAN employs a tree structure, in which each input variable only depends on the target variable and one other input variable. In order to implement the classification, the TAN uses a maximum weighted spanning tree that maximizes the probability of the training data [73] (Figure 1(a)). According to [PERSON] [71], creation of a complete BN model for classification may be computationally ineffective because the full structure may not be related to classification. To identify all input variables in the network that are required to predict the target variable and remedy the abovementioned issue, Markov Blanket Networks were proposed by [PERSON] [74]. Indeed, the Markov Blanket chooses the set of nodes in the dataset that include the target variable's parents, its children, and its children's parents (Figure 1(b)).
## 3. Sacrificial Anode CP
### Zinc-Based Alloy Anode Material  
Zinc was the first material used as a sacrificial anode. Zinc-based sacrificial anode materials are mainly pure zinc anodes and two types of zinc alloy anodes. The presence of cathodic impurity elements (Fe, Cu, Pb, etc.) in the anode will make the pure zinc anode easily polarized, causing it to lose the CP and also make the potential of zinc positive. Self-corrosion produces Zn(OH)\({}_{2}\), which is difficult to remove from the surface of the pure zinc anode, causing passivation [14]. Therefore, the mass fraction of impurity elements should be controlled at Fe \(\leq\) 0.0014%, Cu \(\leq\) 0.002%, and Pb \(\leq\) 0.003%. Analysis of failed pure zinc anode samples showed that the elemental contents of Fe, Cu, and Pb were higher than the above values, and the chemical interaction between zinc and Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\) ions in the marine environment led to the generation of Zn\({}_{5}\)(OH)\({}_{8}\)Cl\({}_{2}\cdot\)H\({}_{2}\)O, Zn\({}_{12}\)(SO\({}_{4}\))\({}_{5}\)Cl\({}_{3}\)(OH)\({}_{5}\cdot\)5H\({}_{2}\)O, and Zn\({}_{4}\)SO\({}_{4}\)(OH)\({}_{6}\cdot\)5H\({}_{2}\)O [15], which are also difficult to shed. Therefore, pure zinc anodes require a zinc purity of up to 99.995% and strictly controlled high-temperature conditions (>60 \({}^{\circ}\)C). This is because zinc-based alloy anodes in high-temperature conditions may experience violent intergranular corrosion [16]. The corrosion products are difficult to remove and can potentially accelerate the corrosion of the protected metal [17].  
Therefore, the electrochemical performance of zinc-based sacrificial anode material can be improved mainly by the following: first, by using high-purity zinc and strictly controlling the impurity content; second, by using low-alloyed zinc-based alloys, created by adding a small amount of highly activated alloying element, such as Al, Cd, Si, Hg, Sn, Mn, etc., while controlling the impurity content. It was found that, through reasonable optimization of the sacrificial anode alloy composition, it was possible to completely prevent the zinc-based anode potential reversal behavior seen at high temperatures [18]. Pure zinc anodes were used in the early sacrificial anode CP systems, but improving the electrochemical performance of zinc anodes through alloying has led to greater advantages in terms of technology, economy, and environmental protection; therefore, zinc alloy anodes have gradually replaced pure zinc anodes and are now widely used, such as in the Tianyuan cross-sea bridge bearing platform protection project in Xiamen [19]. The new high-temperature zinc alloy anode material can be used in high-temperature seawater, marine mud, or high-temperature seawater environments containing oil, with a capacity of 785 A.h/kg at 60 \({}^{\circ}\)C and a potential negative to \(-\)1.0 V (v.s. SCE). The anode surface dissolves evenly, and corrosion products fall off easily. The product has been used on a large scale in submarine pipelines [20].
# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 2 Related Work
### Deep Learning Method  
Deep learning methods consider both local and global features of the input image by using kernel filters for human body detection. [PERSON] et al. [43] presented a human body recognition approach using deep learning. They found that convolutional neural networks (CNNs) can successfully extract and detect human body parts. They also constructed a suitable pose estimation method by using a similar mapping between dimensions. However, this system requires optimizing a low-dimensional space for the human pose search. [PERSON] and [PERSON] [44] proposed a pedestrian detection method using improved CNNs with multi-feature fusion selection. Local parts of the human body were considered individually for the extraction of local features. Then, they merged these local features for full-body human detection. The ability to detect people achieved through multi-feature fusion was superior to what could be achieved based on the individual original features. However, the human anatomical proportions used to divide the parts of the human body were designed without referring to anthropometric data drawn from real human body measurements. Furthermore, the complexity of the multi-feature fusion process remained higher than that of using only one feature. Considering the human parts, [PERSON] et al. [45] proposed the OpenPose method which is applied the CNNs model to map between the Part Affinity Fields (PAFs) and body joints. The results showed the high accuracy of body part detection of the human for multi-person detection. [PERSON] et al. [46] also deployed the OpenPose method to detect human movement through detecting the keypoints of human joint changes. They applied the series recurrent neural network, long- and short-term memory (LSTM), and gated recurrent unit (GRU) models to detect a human fall. Nonetheless, the length of keypoints might be related to the human anatomical proportion from anthropometric data of the real human body.  
While previous approaches can achieve adequate pedestrian detection performance, they may be limited to full-body detection based on human images. Consequently, in some applications, such as lower-body activities, these previous algorithms cannot perform well due to insufficient information. Our work investigates an indirect detection method for lower-body detection using anthropometric ratios applied to human body images.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Machine Learning and Deep Learning Techniques  
Machine learning and deep learning techniques play a promising role in different applications related to UAVs such as battery scheduling, trajectory planning, tracking, obstacle avoidance and resource allocation. The development of new ML tools and enhancement of onboard computational power will help to develop novel UAV models that are smarter, lightweight and smaller to perform any operation without the risk of collision. Using these tools, UAVs can autonomously modify their motion, direction and location to serve ground users. Moreover, the availability of accurate data can support UAVs in smart control, trajectory planning and vision tasks. By installing different cameras on UAV, several types of images can be captured for further image processing. UAV planning, including trajectory, navigation and manipulation can be performed to find an intended path. Similarly, the movement of ground users and load sharing can be obtained for UAV trajectory planning. Although conventional machine learning methods have been implemented in UAVs, but deep learning methods have not been explored due to limited power resources and processing capabilities. Thus, research fraternity should investigate efficient and low-power deep learning methods in UAVs, especially for SAR operations [119].
## 1 Introduction  
Dental erosion is defined as the irreversible loss of mineralized tissue that is caused by acidic agents not involving bacteria [1]. Tooth wear that is induced by erosion has multifactorial etiological factors whose prevalence has increased in recent years and affects almost one-third of the general population [2], resulting in more cases of dental erosion and cervical dentine hypersensitivity [3]. The progression of dental erosion can lead to complications, such as fractures, tooth decay, aesthetics issues (yellow and stained teeth), and eventually tooth loss. Current treatment modalities are based on multifactorial approaches and may include nutritional education, psychological care, fluoride application, laser irradiation, remineralizing solutions, and even tooth restoration [4; 5]. Despite the options that are available to treat dental erosion, some studies have focused on preventive strategies in order to avoid demineralization and caries, such as the development of wireless biosensors that are able to monitor analytical parameters that are involved in the pathogenesis of tooth erosion (e.g., intra-oral pH) [2; 6].  
Localized heating that is induced by high intensity infrared laser irradiation promotes changes in the microstructure of dental tissues, therefore preventing the appearance and progression of dental erosion [7; 8]. The use of Nd:YAG laser (1064 nm) for caries prevention as well as for treating dentine hypersensitivity has been well demonstrated [9; 10; 11; 12]. However, few studies have focused on evaluating the effects of Nd:YAG laser to control the progression of dentine erosion [13; 14; 15]. Besides, contradictory results have been reported due to the different laser parameters, erosive protocols, as well as the association of laser irradiation to different therapeutic agents. Thus, the present study evaluated the protective effects of topical fluoride and Nd:YAG laser irradiation as methods to control dentine erosion based on the Optical Attenuation Coefficient (OAC) that was calculated from Optical Coherence Tomography (OCT) images.  
OCT is a high-resolution, non-invasive, and non-destructive technique that allows the evaluation of both surface characteristics and the quantitative loss of tooth structures [1; 16]. The method generates sub-surface images that are based on the magnitude and echo time delay of backscattered light from different depths of tissue-sample [17; 18]. The potential applications of OCT as a tool to quantitatively and qualitatively monitor the changes in the optical properties of oral tissues has been well demonstrated through many proof-of-concept studies [16; 17; 19; 20]. Besides OCT, quantitative light fluorescence (QLF) is also an option in evaluating dental erosion, however it is more expensive. Other techniques that are used to monitor dentine erosion in vivo may include fluorescence, fiber optic transillumination (FOTI), and X-rays, but without longitudinal information [1].
## 4 Galileo Augmenting GPS SFSE Precise Positioning Experiment
### Experiment Analysis
#### 4.2.2 Experiment 2
The GNSS data is processed in the WGS84 coordinate system, in which the rover station position is described in the form of (X, Y, Z); and, the baseline components (E, N, U) are formed from each monitoring station to the reference station SHM1. These directions are inconsistent with the direction of the vibration component of the bridge. Therefore, a local Bridge Coordinate System (BCS) (XB, YB, ZB) should be defined for further analysis. As shown in Figure 2, the XB axis is formed by shm2 and shm4 and it points to the longitudinal direction of the bridge. Subsequently, YB and ZB correspond to the lateral and height directions. The coordinates in BCS can be obtained by transforming from WGS84 with two-dimensional (2D) similarity transformation. With a baseline solution of shm2 and shm4, the azimuth of the bridge is equal to \(0^{\circ}0^{\prime}36.36^{\prime\prime}\), which is so small that it has little effect on the results before and after the conversion. Afterwards, a Fast Fourier Transformation (FFT) algorithm is adopted precisely for detecting the local dominant frequencies of the XB, YB, and ZB direction of station shm4 and shm2 from 15:00 to 16:00, which is resolved by the SFSE GPS/Galileo baseline length constraint model. From Figures 9 and 10, the local dominant frequencies can be identified, which are listed in Table 5 and are consistent with the result in literature [6]. This indicates that the GPS/Galileo with baseline length constraint is an effective approach to monitor the displacement or deflection behavior of bridges. Figure 9: FFT results of shm4.
# Admission-Based Reinforcement-Learning Algorithm in Sequential Social Dilemmas
## 1 Introduction
However, simultaneously pursuing overall interests and fairness can create the problem of multi-objective conflicts in learning. To address this issue, a novel admission-based multi-agent reinforcement-learning algorithm, the Admission algorithm, is proposed, which enables agents to learn to be cooperative and fair. First, we generalize the give-or-take-some dilemma (GOTS) [29] model to Markov games and introduce a fair Admission reward that allows each agent to optimize its own strategy. The individuals in the GOTS model take atomic actions, which makes it difficult for the agent to learn effectively in long-term tasks. To overcome this problem, we design an Admission Hierarchical Network (AHN) consisting of a high-level policy and several low-level policies. The high-level policy interacts directly with the environment and maximizes the Admission reward by choosing the low-level policy. Two low-level policies are specified to maximize the environmental reward and maximize contribution and the other strategies explore various behaviors guided by information-theoretic rewards. In addition, employing cheap talk [30], the algorithm coordinates agents' strategies in fully decentralized multi-agent learning. In this method, the agents are trained independently and execute independently. Therefore, the Admission algorithm avoids unrealistic assumptions and effectively solves the social dilemma problem. We conducted some experiments in two classic SSD environments to evaluate the proposed algorithm. In the Admission algorithm, each agent directs behavior and enables simple communication through its own AHN. The experimental results show that the algorithm achieved cooperation among multiple agents and is superior to the existing algorithms in fairness. Due to cheap talk, the Admission algorithm can learn and execute in a completely decentralized manner, making it more realistic. The rest of this paper is organized as follows: In Section 2, we provide an overview of the partial observation Markov decision process, hierarchical reinforcement learning, and sequential social dilemmas. In Section 3, we describe the proposed Admission algorithm in detail, including its reward design, network structure, and algorithm flow. Section 4 provides the experimental environment and results, while Section 5 offers a discussion of these findings. Finally, in Section 6, we summarize the paper.
## 2 Materials and Methods
### The Methodology
#### 2.1.2 Use of Theoretical Models to Extract the Elastic Curves  
From a theoretical point of view, if we have the detailed geometric and physical data, we could obtain analytical models that represent the physical bending of a structure and, in particular, of a beam. For a beam, the governing differential equation for the elastic curve, is given by:  
\[\frac{\partial^{2}z}{\partial x^{2}}=\frac{M(x)}{EI} \tag{1}\]  
where:  
\(z=\) deflection  
\(x=\) abscissa (along the longitudinal axis)  
\(M=\) bending moment;  
\(E=\) Modulus of Elasticity;  
\(I=\) Area moment of Inertia cross-section.  
The displacement values are a function of the structural scheme and of the position in which the loads are applied [29]. In many cases, the displacement model can be assimilated to polynomials. With reference to Figure 1, e.g., taking into account a simply supported beam with uniform section, a structural scheme currently used in many bridges, a punctual load produces a displacement given by:  
\[\delta_{x}=\frac{Fax\left(\
icefrac{{I-a^{2}-x^{2}}}{{6 EI}}\right)}{6 EI} \tag{2}\]  
for \(0<x<b\), and by:  
\[\delta_{x}=\frac{Fb(l-x)\left[\
icefrac{{I-b^{2}-(l-x)^{2}}}{{6 EI}}\right]}{6 EI} \tag{3}\]  
for \(b<x<l\)  
where:  
\(F=\) Force acting on the beam;  
\(\delta x=\) displacement at a distance x from the support 1;  
\(a=\) distance from the load to the support 2;  
\(b=\) distance from the load to the support 1;  
\(l=\) distance between supports.  
The generalized form of the cubic polynomial is given in Equations (4) and (5) [30].  
\[\delta_{x}=a_{30}x^{3}+a_{10}x+a_{00}+a_{01}y \tag{4}\]  
for \(0<x<b\), and by:  
\[\delta_{x}=\ b_{30}x^{3}+b_{20}x^{2}+b_{10}x+b_{00}+a_{01}y \tag{5}\]  
for \(b<x<l\), where:  
\(y=\) horizontal distance from the longitudinal axis.  
The best fitting line of the 2D point cloud provided by TLS in line scanner mode will be obtained by finding the coefficients \(a_{i0}\) and \(b_{i0}\) of the previous formulas with a least-squares procedure. The coefficient \(a_{01}\) has been introduced in [30] to take into account rotation about x axis.