esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Deep Learning Methods
All of these elements together form an effective and accurate system to predict human plans and trajectories and ensure their effective coordination with the robot. The work was validated using collected data on a real warehouse. The innovations of this paper include a robust plan recognition algorithm that combines neural networks with Bayesian inference, ensuring efficient and safe HRC. This framework not only predicts human trajectories but also recognizes high-level plans, enhancing the adaptability of robots to human actions. However, potential limitations arise from its reliance on motion labels via neural networks, which might pose accuracy challenges. Despite its primary focus on desktop assembly, the framework's broader applicability remains an area of interest. Future endeavours should encompass comprehensive comparative studies and integration with diverse human-robot collaboration frameworks to further validate and expand upon the model's advantages. Table 3 categorizes the methods based on their evaluation approach, whether through simulation, benchmark datasets, synthetic datasets, or real-world case studies. From the presented papers, it is evident that benchmark datasets, particularly ETH/UCY and SDD, are the preferred choice for validation. Utilizing these datasets facilitates easy comparison of the methods in focus with existing state-of-the-art techniques. While real-world case studies are less common, they are crucial in ensuring that the proposed methods are not only theoretically robust but also practically applicable. The employment of simulation and synthetic datasets underscores the researchers' intention to rigorously test their models in controlled settings before applying them to real-world contexts. Such approaches grant a deeper insight into the model's behaviour under specific conditions. Table 4 showcases a comparative analysis of models evaluated on the ETH/UCY and SDD datasets. The table delineates the performance of each model using two metrics: Average Displacement Error (ADE) and Final Displacement Error (FDE). For the ETH dataset, ref. [67] stands out with the lowest ADE and FDE values. On the combined ETH/UCY average, ref. [69] exhibits the best performance in both ADE and FDE. In the SDD dataset, under different parameter settings, ref. [69] is notable for its performance in the k = 20 setting. The parameter 'k' refers to the number of samples used for evaluating multi-modal predictions.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 3 Adaptive Bat Algorithm Optimizes Observation Matrix
### Compress Sensing  
In this subsection, the process of compressed sensing is introduced. Firstly, the sparse signals are calculated as follows:  
\[X=\Psi\Theta \tag{1}\]  
where \(\Theta\) (\(\Theta\in R^{N\times 1}\)) represents a sparse representation of the signal \(X\) on the transform base \(\Psi\), and the length of signal \(X\) is \(N\).  
Then, observation signal (where, the dimension of observation signal is \(M\)) can be compressed with Equation (2).  
\[Y=\Phi\Psi\Theta=A_{CS}\Theta \tag{2}\]  
where \(Y\) represents the observation signals, \(\Phi\in R^{M\times N}\) is observation matrix, \(A_{CS}=\Phi\Psi\) is sensing matrix.  
Finally, the reconstruction process model is described as shown in Equation (3).  
\[\left\{\begin{array}{l}\min_{\Theta}\lVert\Theta\rVert_{0}\\ s.t.Y=A_{CS}\Theta\end{array}\right. \tag{3}\]  
where \(\lVert\Theta\rVert_{0}\) represents the \(l_{0}-norm\).
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 3 Proposed Data Augmentation Method
### Comparing the Ratio of Two Simulated RTFs to the Ratio of Two Rayleigh Random Variables  
We assumed that the two RTFs are i.i.d. Rayleigh random variables and then performed a pilot test with simulations to confirm this assumption. We employed an RIR generator that uses image methods [25] to validate that the value generated by the formula in [24] works as designed. The RTF generator used the algorithm presented in [21]. This simulation is an algorithm that generates RTFs based on the size of the room, the location of the sound source, and the reflection coefficient. The receiver positions were randomly generated for the experiment. Position x was between 0 and 7 m, y was between 0 and 4 m, and z was between 1 and 2.3 m. Table 1 displays the parameters used in the experiments.  
After converting these simulated data to the frequency domain through STFT, the two sets of data were divided element-wise. To make the generated data, we used the CDF in (11) and generated random values such that \(\sigma\) was 1 and \(\lambda\) was 1. Figure 7 displays these values as a PDF and histogram of the data.  
Figure 6: Augmented features(right, \(\mathbf{\tilde{Y}}_{aug}\)) are obtained by multiplying the mel features(left, \(\mathbf{Y}_{orig}\)) element-wise (denoted by \(\circ\)) with the \(\mathbf{A}\) (middle), as defined by (12).  
Figure 7 compares the statistical properties of the data generated using the two methods, namely ratio distribution based on the RIR model and the random numbers generated using Equation (11). The solid and dashed lines in Figure 7a represent the PDFs, while Figure 7b shows their corresponding histograms. These figures show that despite the differences in their mean values, both methods produce data with similar statistical patterns that are characterized by values clustered near the beginning and having a long tail. This suggests that the proposed method can effectively replicate the statistical behavior of the RIR model, thereby offering a comparable alternative for RIR simulation.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
###### Abstract  
This paper proposes a reversible data hiding technique based on two cover images. The proposed method enhances performance by utilizing Hamming coding (HC), arithmetic coding (AC), and an improved Exploiting Modification Direction (EMD) technique. Since AC provides lossless compression for binary data, it is widely used in image compression and helps maximize the efficiency of data transmission and storage. The EMD technique is recognized as an efficient data hiding method. However, it has a significant limitation: it does not allow for the restoration of the original cover image after data extraction. Additionally, EMD has a data hiding capacity limit of approximately 1.2 bpp. To address these limitations, an improved reversible data hiding technique is proposed. In this study, HC and AC are integrated with an improved EMD technique to enhance data hiding performance, achieving higher embedding capacity while ensuring the complete restoration of the original cover image. In the proposed method, Hamming coding is applied for data encoding and arithmetic coding is used for compression to increase efficiency. The compressed data are then embedded using the improved EMD technique, enabling the receiver to fully restore the original cover image. Experimental results demonstrate that the proposed method achieves an average PSNR of 66 dB and a data embedding capacity of 1.5 bpp, proving to be a promising approach for secure and efficient data hiding applications.  
Keywords:data hiding (DH); reversible DH (RDH); dual RDH; Hamming coding; arithmetic coding; exploiting modification direction (EMD) +
Footnote †: journal: Applied Sciences
# A New Method for Detecting Weld Stability Based on Color Digital Holography
## 2 Methods
### Weld Samples  
The samples in the experiment are lap-jointed and butt-jointed plates made of 6061 aluminum alloy and a defective butt-jointed plate made of 7075 aluminum alloy. These aluminum alloy plates were randomly selected from the laboratory and used common welding techniques in engineering. Their structure and dimensions are shown in Figure 2.  
Figure 1: The optical path of color digital holography.  
During the experiment, the load was applied in the y-direction of three samples. The load applied to the lap-jointed and butt-jointed aluminum alloy plates varies from 293.67 N to 301.50 N in steps of 3.92 N, while that applied to defective butt-jointed aluminum alloy plate varies from 293.87 N to 294.45 N in steps of 0.39 N. This is because a defective butt-jointed aluminum alloy plate is more prone to deformation, and the phase difference fringes obtained within the same load interval are very dense, exceeding the detection range of the system.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 4 Localization Algorithms
### Min-Max  
Min-Max is a popular localization algorithm whose success is mainly due to its extreme simplicity of implementation. A number N of fixed Anchor nodes is positioned at known coordinates (\(x_{i}\),\(y_{i}\)), where \(I=1\), 2,..., N. A target T is situated at (\(x_{i}\),\(y_{i}\)), which needs to be determined. Once the target collects RSSI measurements and convert them into distances \(d_{i}\), squares are constructed for each Anchor by drawing two horizontal lines and two vertical lines around each reference node so that the side of this square is equal to the double of distance \(di\), as showed in Figure 5. The intersection of these squares determines another quadrilateral whose coordinates can be calculated as follows:  
\[\begin{array}{l}V_{1}=[max(x_{i}-d_{i}),max(y_{i}-d_{i})]\\ V_{2}=[max(x_{i}-d_{i}),min(y_{i}+d_{i})]\\ V_{3}=[min(x_{i}+d_{i}),min(y_{i}+d_{i})]\\ V_{4}=[min(x_{i}+d_{i}),max(y_{i}-d_{i})]\end{array} \tag{5}\]  
Figure 5: Min–Max operating principle.  
Finally, the position estimated is calculated at the center of the intersecting quadrilateral as follows:  
\[x_{est} =\frac{[min(x_{i}+d_{i}),max(x_{i}-d_{i})]}{2} \tag{6}\] \[y_{est} =\frac{[min(y_{i}+d_{i}),max(y_{i}-d_{i})]}{2}\]  
Hence, the smaller the area of the intersection rectangle, the higher the localization accuracy.
###### Abstract  
The GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) navigation system has been widely discussed in recent years. Because of the unique INS-aided loop structure, the deeply coupled system performs very well in highly dynamic environments. In practice, vehicle maneuvering has a big influence on the performance of IMUs (Inertial Measurement Unit), and determining whether the selected IMUs and receiver parameters satisfy the loop dynamic requirement is still a critical problem for deeply coupled systems. Aiming at this, a new parameter self-calibration method based on the norm principle is proposed which explains the relationship between IMU precision and the velocity error of the system; the method will also provide a detailed solution to calculate the loop steady-state tracking error, so it will eventually make a judgment about the stability of the tracking loop under present system parameter settings. Lastly, a full digital simulation platform is set up, and the results of simulations show good agreement with the proposed method.  
deeply coupled; highly dynamic environments; norm; parameter self-calibration 18 June 2018; Accepted: 16 July 2018; Published: 18 July 2018
# Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures
## 2 Materials and Methods
### Mix Proportions  
Following UNE 83502:2004 [26] and UNE 83504:2004 [27], twelve concrete samples were manufactured for each LWC in the laboratory. The mix proportions of expanded clay aggregates, siliceous aggregate, cement, and water are shown in Table 1. LWC-1 was prepared using 4.95 L of ECF, 6.3 L of ECC, 14.71 kg of siliceous aggregate, 11.7 kg of cement, and 5.66 L of water. LWC-2 was prepared using 5.25 L of ECF, 4.5 L of ECC, 17.11 kg of siliceous aggregate, 11.7 kg of cement, and 5.66 L of water. LWC-3 was prepared using 3.6 L of ECF, 3.6 L of ECC, 20.02 kg of siliceous aggregate, 12.6 kg of cement, and 6.09 L of water.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Mixing** \\ **Compositions** \\ \end{tabular} } & \multicolumn{5}{c}{**Constituents**} \\ \cline{2-6}  & **ECF** & **ECC** & **Siliceous Aggregate** & **Cement** & \\  & **(\%)** & **(\%)** & **0/2 (\%)** & **42.5R (\%)** & **Water (\%)** \\ \hline LWC-1 & 5.85 & 6.05 & 40.42 & 32.14 & 15.54 \\ LWC-2 & 5.89 & 4.11 & 44.68 & 30.55 & 14.77 \\ LWC-3 & 3.91 & 4.50 & 44.44 & 31.79 & 15.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixing proportions of lightweight concrete in mass fractions.  
Figure 1: Types of aggregates: (**a**) expanded clay-fine; (**b**) expanded clay-coarse; (**c**) polyolefin fibers.  
LWCs have pores within their structure. In order to know their porosity, true density (\(\rho_{true}\)) and bulk density (\(\rho_{bulk}\)) were measured using dry samples [28]. The porosity (\(n\)) of the dry LWC samples was estimated using Equation (1). The true density of each LWC was measured using a helium pycnometer model AccuPyc 1340, which provided high-speed, high-precision volume measurements and density calculations. To measure the bulk density, volume, and mass, standardized cylindrical samples of \(\Phi\)15 \(\times\) 30 cm were used. The results are shown in Table 2.  
\[n=\left(\frac{\rho_{true}-\rho_{bulk}}{\rho_{true}}\right)\times 100\% \tag{1}\]
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets
### Benchmark Datasets
#### 3.2.4. Occluded Human Dataset (OCHuman)  
The OCHuman dataset [51] is a large-scale image benchmark specifically designed to address the challenges of human pose estimation under severe occlusion. Released by MEGVII Research in 2018, the dataset was constructed by filtering human instances from the COCO dataset with an occlusion ratio exceeding 60%. OCHuman comprises approximately 6700 images and over 11,000 human instances, with significant occlusion. The dataset is divided into training, validation, and test sets, with 1000 images each in the validation and test sets. Each instance is annotated with bounding boxes, human keypoints, and segmentation masks, offering comprehensive supervision for a variety of vision tasks. Notably, each keypoint is labeled with a visibility flag indicating whether it is visible or occluded. Owing to its emphasis on severe occlusion and fine-grained annotations, OCHuman is regarded as one of the most challenging and complex benchmarks in human-centric vision research. Figure 8 displays representative examples from the OCHuman dataset.  
Figure 8: Example images from the OCHuman Dataset.  
Figure 7: Example images from the LSP Dataset.
## 3 Results  
### Analysis of Volatile Organic Compounds in Two Cultivars of Z. bungeanum in Different Periods  
The main volatile organic compounds in _Z. bungeanum_ were terpenoids, alcohols and esters. A total of 39 substances were detected across the five, which included 26 types of terpenoids, 9 types of alcohols and 4 types of esters (Table 2). \(\alpha\)-Pinene had the highest content of the volatile components in the two types of _Z. bungeanum_, which reached the highest values of 24.74% and 20.78%, respectively. Hanchengdahongpao has more volatile compounds than Fuguhuajiao as 35 types of volatile components were detected in Hanchengdahongpao, including 24 types of terpenoids, 7 types of alcohols and 4 types of esters. A total of 21 types of volatile components were detected in Fuguhuajiao, including 16 types of terpenoids, 4 types of alcohols and 1 ester. These volatile components are unique to Hanchengdahongpao: Bicyclo[2.2.1]heptane, D-Limonene, Cyclohexene, Eucalyptpol, \(\alpha\)-Myrcene, 3-Cyclohexen-1-one, 2-Cyclohexen-1-one, 6-Octenal, GermacreneD, Geraniol, Cyclohexanol, 5-Isopropyl-2-methyl, bicyclo[3.1.0]hexan-2-ol, 3-Cyclohexen-1-ol, 6-Octen-1-ol, Linalyl acetate, Myrtenyl acetate and \(\alpha\)-Ternyl acetate. These volatiles are unique to Fuguhuajiao: (S,1Z,6Z)-8-Isopropyl-1-methyl-5-methyl- Encyclodeca-1,6-diene, (1S,4 aR,8 aS)-1,4a-Isopropyl-7-methyl-4-methylene-1,2,3,4,5,6,8a-octahydronaphthalene, (3E,5E)-2,6-Dimethylocta-3,5,7-trien-2-ol and (2E,4S,7E)-4-Isopropyl-1,7-dimethylcyclodeca-2,7-dienol. In the case of terpenoids, the monoterrepenoids were the dominant type of terpenoids (Table 2). A total of 18 types of monoterpene and 6 types of sesquiterpene were released by Hanchengdahongpao, with (1S,2E,6E,10R)-3,7,11,11-Tetramethylbicyclo[8.1.0]undeca-2,6-diene having the highest content among all sesquiterpenes in the two cultivars of _Z. bungeanum_. The heatmap for the content of terpenoids in five different periods is shown in Figure 2a. In general, as the fruit grows, the content of volatile terpen in _Z. bungeanum_ first increases before decreasing.
## 4 Investigation of Influence of Model Size and Joint Dip Angle on Rock Brittleness  
### Experimental Scheme and Design  
Previous research has emphasized the significance of model sizes and joint dip angles in determining the strength and deformation properties of rocks. Nonetheless, there are very few studies on how the brittleness of rock masses is influenced by model size and joint dip angle. To address this gap, we conducted uniaxial compression tests on rocks that considered both size effects and joint dip angle in our previous work [33]. Specifically, we examined the impact of these two factors on the brittle characteristics of rock masses using the proposed brittleness index (Equation (15).  
Figure 4 displays the test specimens. Note that L, H and W symbolize the length, height and width of the specimens, respectively, and \(\alpha\) denotes the dip angle of the joints. We prepared the specimens using comparable materials (silicate cement, pure water and fine sand in a ratio of 2:1:2). Given the practical constraints of laboratory tests, we neglected the boundary effects of the joints. To simulate the intermittent joints typically encountered in real-world conditions, we combined the smallest specimens of the test design in equal ratios to create larger specimens for assessing the influence of size on rock properties.  
Figure 4: Schematic diagram of jointed specimens.  
Figure 3: Brittleness indexes under different temperatures.  
For the test, we examined a total of 20 groups, with three specimens in each group, using four joint dip angles (\(\alpha\) = 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\)) and five model sizes (\(L\) = 40, 80, 120, 160 and 200 mm). Table 3 illustrates the testing protocol. We employed a rock mechanics rigid servo testing machine (model: RMT-150C) manufactured by the Chinese Academy of Sciences (Beijing, China), to conduct the laboratory experiments. The tests were performed using a constant displacement-controlled loading of 0.005 mm/s. The test equipment and loading diagram are shown in Figure 5.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 2 Degradation Modeling and Prognostic Hybrid
### Decision-Level Fusion Based on Model Averaging  
The system's ability to withstand the damage is different for the system in different aging states. In order to integrate the long-term cumulative degradation and the latest stage degradation, the fusion model is used in this paper. The fusion model includes two kinds of methods, one is based on a bagging, boosting or stacking method, which consumes a lot of training time. Therefore, the averaging method is used to make a final decision in this paper. The long-term cumulative prediction \(\hat{y}_{c}\) is obtained as in Section 2.2. The latest stage prediction \(\hat{y}_{l}\) is obtained as in Section 2.2.2. Finally, the final result \(\hat{y}\) is calculated as in Equation (17) using the averaging method.  
\[\hat{y}=\gamma\hat{y}_{c}+(1-\gamma)\hat{y}_{l}. \tag{17}\]  
where coefficient \(\gamma\) is set as in [36].
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 4 Experimental Setup
### Gaussian Distribution Model  
The Gaussian distribution is a form of probability distribution that is frequently utilized in probability theory and statistics. It also goes by the name of the normal distribution. The central limit theorem is closely related to the Gaussian distribution, which is used to represent a variety of natural events and datasets. Mean and standard deviation are the two fundamental characteristics of the Gaussian distribution. The formula is given below [32,46];  
\[f(x)=\frac{1}{\sigma\sqrt{2\Pi}}exp(-\frac{1}{2}\frac{(x-\mu)^{2}}{\sigma^{2 }}) \tag{8}\]  
In the Gaussian distribution equation, we see the following: \(x\) is the value of the random variable, \(\mu\) is the mean of the distribution, \(\sigma\) is the standard deviation of the distribution, and exp is [PERSON]'s number (approximately equal to 2.71) in the equation.  
A bell curve with a symmetrical shape represents the Gaussian distribution. The mean establishes the distribution's center, and the standard deviation establishes how dispersed or tight the distribution is. Numerous applications of the Gaussian distribution exist, particularly in the domains of statistics, engineering, economics, natural sciences, and data science. Making predictions, testing hypotheses, and studying data distribution all benefit from using this method.  
As shown in Figure 5, these distributions can be translated into a reward mechanism that is exactly fit for purpose. Vehicles that arrive at the destination on time will be rewarded as they approach the average arrival time. Late and early arrivals are penalized by deducting the arrival time penalties and distributing a reward. The reward model is as follows:
## 4 Synthesis of Amidoalkyl Naphthols via Multicomponent Mannich Condensation
### Homogeneous Catalysis  
#### 4.1.1 Bronsted Acids as Catalysts  
Bronsted acid catalysts play an important role in modern organic synthesis due to their ability to activate a wide range of functional groups. These catalysts are relatively easy to store and generally stable toward oxygen and water. Their metal-free nature also makes them an alternative to the metal catalysts used in the pharmaceutical industry, as traces of toxic metal impurities are often very hard to remove from the desired products [59]. In several instances this class of catalysts has been reported to promote the synthesis of amidoalkyl naphthols via multicomponent Mannich reaction (Table 1).  
[PERSON] et al., developed an efficient green synthesis of 1-amidoalkyl-2-naphthol derivatives via three-component one-pot condensation of 2-naphthol with a wide range of functionalized aromatic aldehydes and acetamide/acrylamide at 120 \({}^{\circ}\)C under solvent-free conditions in the presence of 15 mol% phenylboronic acid as a catalyst. The synthesis of 19 amidoalkyl naphthols was achieved in 60-92% yields in the range of 1-7 h [23]. [PERSON] et al., made use of 10 mol% adipic acid as a catalyst in a similar three-component condensation of 2-naphthol under solvent-free conditions and 120 \({}^{\circ}\)C. Yields of up to 96% for 21 amidoalkyl naphthols have been achieved in shorter reaction times that lay in the range of 9 to 76 min [60]. Another efficient solvent-free method was reported by [PERSON] and [PERSON]. The presence of only 8.5 mol% of the readily available natural ascorbic acid rendered a range of 13 amidoalkyl naphthols in good to high yields (75-96%) in very short reaction times of only 4-12 min. Notably, the easy work-up was stated to be one of the advantages of this method [61]. [PERSON] and [PERSON] achieved the synthesis of 1-amidoalkyl-2-naphthol derivatives in the presence of 10 mol% 2,6-pyridinedicarboxylic acid (dipicolinic acid) as a reusable catalyst. High yields of up to 96% in reaction timesof a few hours were reported [62]. However, this method requires the use of refluxing toluene as a reaction medium. [PERSON] et al., used \(p\)-toluenesulfonic acid (10 mol%) for the preparation of these derivatives. The highest yields (65-91%) were obtained when polyethylene glycol 400 (PEG-400) was utilized as an environmentally friendly solvent. The reaction times varied from 20 min to 4 h [63].