esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Secret Keys  
Secrete keys are vital for the proposed algorithm to implement embedding and extraction procedures. Three keys are utilized and these should be preserved well for watermark extraction and verification. These are:  
* The parameters for Torus automorphism permutation: The disarrangement parameters \(k\) and \(m\) obtained in Equation (1) are necessary when reconstructing the watermark pattern.
* The record number of the selected block has to be preserved.
* The embedding position of the watermark bits, i.e., the DCT coefficients and their positions have to be preserved.
* Co-prime numbers used in CRT.
# Challenge-Based Learning and Scrum as Enablers of 4.0 Technologies in Engineering Education
###### Abstract  
Incorporating Industry 4.0 technologies in higher education is crucial for preparing future engineers in a digitalized industrial environment. A challenge-based educational model and the agile Scrum methodology were implemented to move in this direction, combined with the use of Industry 4.0 technologies. New physical and virtual laboratories were enabled with Industry 4.0 hardware/software equipment. The engineering academic programs at the second-largest university in Chile were modified, and all the academic staff involved were trained. Based on conducting challenges that Industry 4.0 technologies can address, this study presents the results of implementing a new educational model and students' response to freely adopting new technology; such as artificial intelligence (AI), the Internet of Things (IoT), and collaborative robotics. The results showed a high rate of acceptance and use of 4.0 technologies by the students, so the proposed methodology could be extrapolated to other engineering programs worldwide.  
challenge-based learning; Scrum; Industry 4.0; engineering education +
Footnote †: journal: Applied  
developed for the use of technology. In addition, a new technological department called the National Unit of Industry 4.0 Laboratories (NUIL) was created, composed of engineers who are experts in various Industry 4.0 technologies. The mission of the NUIL was to coordinate and manage the technological and human resources necessary to successfully integrate these technologies in academic training. It links specialized academics and technology, unifying joint projects and ensuring that students have access to real and current industry problems.  
One of the most relevant aspects of implementing this initiative was training academics. To achieve successful implementation, the teaching staff were previously trained in 4.0 technologies, challenge-based learning, and agile project methodologies (Scrum).  
The central objective of this article is to provide evidence on the implementation of Industry 4.0 technologies in a higher education institution (taking as a case study the second largest university in Chile) and evaluate the adoption of advanced technologies by engineering students, especially in their ability to solve real industry problems. The implementation of 4.0 technologies involves adopting new tools and a profound change in the educational mentality.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Time Domain Results  
Table 2 presents the results of seven features in the time domain: Higuchi's fractal dimension (HFD); the Hjorth parameters Hjorth activity (HjAc), Hjorth complexity (HjComp), and Hjorth mobility (HjMo); Hurst (Hur); kurtosis (Kurt); and skewness (Skw). The accuracy and the number of EEG channels selected are two measurements to evaluate the seven features chosen for the motor/imaging EEG dataset. The evaluation metrics are classified into three categories: best, worst, and mean accuracy and no. of channels selected to evaluate the algorithms' stability. According to Table 2, the performance results for the metaheuristic algorithms PSO, GWO, FFA, CS, and BAT with several features achieved different and remarkable accuracy concerning best, worst, and mean accuracy and the number of channels selected. For the HFD feature, the BAT algorithm achieved better accuracies of 99, 97.5, and 92.8 for best, mean, and worst accuracy and channel selection, respectively. For the HjAc feature, the BAT algorithm achieved high accuracy, 97.7, for accuracy and no. of channels selected in the best case, while PSO achieved a valuable ratio of 94.4 and 85.3 in accuracy and the number of channels selected in the mean and worst cases, respectively. For the HjComp feature, the PSO algorithm achieved an accuracy of 88.6 and 84.7 concerning mean and worst cases, while the BAT algorithm achieved the highest accuracy of 93.8 in the best case. For the HjMo feature, BAT obtained 97.8, 95.4, and 88.6 for best, mean, and worst among other metaheuristic algorithms. For the Hur feature, PSO gained high accuracies of 72.8, 65.2, and 56.6 for best, mean, and worst, respectively. For the Kurt feature, the BATalgorithm obtained valuable accuracies of 73.2, 68.8, and 63.1 for best, mean, and worst accuracy and the number of channels selected, respectively. Moreover, for Skw, BAT achieved high accuracies of 79.2 and 72.5 for best and mean, respectively, compared with PSO, which obtained an accuracy of 58 in the worst case of accuracy. Figure 4 shows the results of the time domain features for the convergence rate and channel distribution, comparing the BAT and PSO algorithms, which achieved the highest accuracy over the other metaheuristic algorithms, i.e., GWO, FFA, and CS. In Figure 4a, the convergence rate shows the channels and the number of iterations for the BAT and PSO algorithms. In addition, Figure 4b presents the effective and emotive channels when channels are distributed in the BAT and PSO algorithms.
## 5 Results and Discussion
### The ResQhot 2.0 Assembly
#### 5.2.1 Differential-Drive Mobile Base  
ResQbot 2.0 is designed to use a differential-drive mobile base module for mobility. This module provides fast and flexible manoeuvrability in flat terrain typically found in urban and indoor environments. The mobile base platform is a customised version of a commercially available powered wheelchair--Quickie Salsa-M--manufactured by Sunrise Medical [62]. This mobile base has a versatile design and is stable since it was designed to transport a disabled person both indoors and outdoors. The differential-drive wheels are located at the centre to enable a compact turning circle. In order to ensure its stability and safety while manoeuvring, this platform is equipped with an all-wheel independent suspension and anti-pitch technology suitable for use on rough or uneven terrain. Moreover, this platform was designed to carry loads up to 140 kg, which makes it suitable for the ResQbot 2.0 application: To carry an average-size casualty weighing approximately 80-100 kg [62]. Other important reasons for choosing this mobile platform are its mission range and its maximum operational speed. This mobile base can cover up to 32 km with a 60 Ah battery [62], and it has a maximum speed of 10 kph [62]. Figure 11a shows the differential-drive mobile base module.  
Figure 11: ResQbot 2.0 main modules. (**a**) Differential-drive mobile base module. (**b**) Stretcher bed tilting module. (**c**) The bed’s sliding-rail mechanism. (**d**) The motor drive and the lead-screw mechanism that enable the stretcher bed to slide. (**e**) The motor drive and transmission system of the conveyor module. (**f**) The rotary encoder of the conveyor module, which allows feedback control to synchronise the conveyor and mobile base speed during the casualty loading process. (**g**) DC motor to drive the strap fastening and securing mechanism. (**h**) The stretcher strap module attached to the stretcher bed.  
Figure 10: The fully assembled ResQbot 2.0. (**a**) The compact configuration is used for general navigation and casualty transportation. (**b**) ResQbot 2.0 in the loading configuration, allowing the robot to adjust its configuration to the optimal loading angle to perform the casualty extraction procedure safely.
# Networked Roadside Perception Units for Autonomous Driving
## 5 Evaluation
### Experimental Evaluation  
As shown in Figure 6, we took into consideration a scenario wherein an RSPU stated at an intersection gets the \(CPM_{CAM8}\) broadcasted to the surrounding vehicle (at Hongo campus in the University of Tokyo, Japan). The same equipment (an OpenC2X router, an Autoware host, an antenna, and a LiDAR sensor) was involved in both the RSPU and the vehicle. With an Intel Wi-Fi 6 AX200 module, the routers were APU4C4-embedded routers wherein Ubuntu 18.04.3 LTS is deployed. In addition, the hosts included laptop PCs (CPU Core i7 8-cores) with 32 GB (vehicle host) RAM memory and Ubuntu 16.04.5 LTS and 16 GB (RSPU host). The vehicle and the RSPU were equipped with Velodyne VLP-16 LiDAR sensors. As per the Japanese regulations, the tests involved the use of the IEEE 802.11g ad-hoc mode.  
Figure 6 shows that the total delay \(T\) and PDR \(R_{pd}\) were viewed as the metrics of evaluation. \(R_{pd}\) denoted the PDR between the routers since there was no packet loss in the ITS station (i.e., Ethernet link) across all experiments. \(R_{pd}\) was measured by comparing the LDM entries of the sender and receiver after the experiments. The total delay \(T\) is expressed as  
\[T=T_{pc}+T_{hr}+T_{rr}+T_{rh}, \tag{3}\]  
Figure 6: Experimental setup.  
where \(T_{pc}\) signifies the delay in Autoware. On the other hand, \(T_{hr}\), \(T_{rr}\), and \(T_{rh}\) denote the transmission delay from the host to the router, between routers, and from the router to the host, respectively. \(T_{pc}\) is inclusive of the processing delay in Autoware relating to the clustering of detection, point cloud, and the objects' localization; the calculation of this value is made by taking into consideration the gap between the point cloud's published time and the time of the router's message. \(T_{hr}\), \(T_{rr}\), and \(T_{rh}\) are calculated by taking into consideration the round-trip time for each link. Upon the message's message, the proposed extensions for both Autoware and OpenC2X return the same message to the sender for measuring the round-trip time.
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
## 2 Results
### Transcriptomic Profiles
#### 2.2.2 Detailed Analysis of Selected Defense-Associated Pathways
Four of them were among the 25 most highly up-regulated genes for the Tu treatment, including #1; Solyc03g098760, a PI (Table S3). This latter gene is also the top-ranking induced gene in the Te samples (Table S4), although the induction is much stronger in Tu (26-fold induction versus 7-fold). In total, four antinutritive protein-encoding genes were significantly up-regulated in Te (two PIs, one Lap, one PPO). Despite the high total number of up-regulated DEGs in Al samples, only seven of them encode putative JA-responsive antinutritive proteins (five PIs, one arginase, one PPO) and none of them were among the 25 most highly up-regulated Al-DEGs (Table S5). In addition, when directly compared with the antinutritive protein-encoding genes induced in Tu, the induction was considerably lower (i.e., absolute Log\({}_{2}\)FC difference \(>\) 0.2) for nearly 60% of them in Tu+Al, and for about 20% of them in Tu+Te, indicative of suppression by _A. lycopersici_ and _T. evansi_, respectively. We verified this for several genes by means of qPCRs (Figure S1A-G). Our data indicates that suppressor mites do not suppress all antinutritive protein-encoding genes, i.e., at least at this single time point seven days after infestation. Furthermore, it is worth pointing out that in dual-infested leaves those genes suppressed by _A. lycopersici_ were usually not suppressed by _T. evansi_ and vice versa. For instance, the _T. urticae_-induced expression of Solyc03g098720, putatively coding for a Kunitz-type trypsin inhibitor (a PI), is suppressed by _A. lycopersici_ but not by _T. evansi_ in dual-infested plants (Figure S1C), while the opposite is observed for Solyc03g098740, which encodes another Kunitz-type trypsin inhibitor (Figure S1D). To get an approximate idea of the extent to which the JA pathway genes depicted in Figure 3 are indeed JA-responsive, we cross-referenced our DEGs with genes whose expressions was significantly induced in _def-1_, 24 h after exogenous application of JA [39]. As expected, each section of the JA pathway contained JA-responsive genes and, except for _TD-2_, all were up-regulated in response to mite feeding, in particular in the Tu samples (Figure 3). Next, we applied custom filters to our microarray data in order to identify _A. lycopersici_-suppressed genes in dual-infested leaves (Table 1). We found that 23 out of the resulting 30 candidates were JA-inducible according to the data of [PERSON] et al. [39]. To put this into perspective, applying the same filters to identify _T. evansi_-suppressed genes yielded a set of 31 candidates, four of which were JA-inducible (Table 2). Moreover, of the 25 most highly up-regulated DEGs, seven were JA-inducible for the Tu treatment (Table S3), 13 for the Te treatment (Table S4), yet zero for the Al treatment (Table S5). Our data, therefore, provides compelling evidence in support of the hypothesis that _A. lycopersici_ predominantly suppresses JA-regulated defense responses [23].
## 2 Materials and Methods
### Data Used
#### 2.2.3 Net Primary Productivity (NPP) Datasets  
The Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based NPP product (MOD17A3 HGF) was used for analyzing vegetation production affected due to mining operations. The MOD17A3 HGF (version 6.1) products provide gap-filled annual NPP at 500 m spatial resolution. The MOD17A3 HGF is derived from the sum of all 8-day net photosynthesis (PSN) products (MOD17A2H). The gap-filled MOD17A3 HGF is an upgraded version of MOD17 that has removed the low-quality inputs from the 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for each pixel. The details on MODIS NPP can be found in Running and Zhao [52].  
NPP is the initial tangible stage in transferring atmospheric carbon to the biosphere. NPP is vegetation's net carbon gain (stock), equivalent to the difference between photosynthetic gains and plant respiration (losses). Additionally, it is the key process in carbon cycling and the main direct product for human society from the vegetation ecosystem. As an essential indicator of ecosystem functioning, NPP change has been widely used to reflect the environmental problems caused by anthropogenic activities, such as mining, urbanization, deforestation, etc. [53; 54]. Thus, the present study used annual NPP datasets to approximate the loss of vegetation carbon stock (i.e., indirect CO\({}_{2}\) emission) due to mining activity over the Rajmahal Hills from 2001 to 2021.
## 4 Results and Discussion
### Probable Immobilization Mechanism of Cr  
The XRD results of the Cr(VI)-contaminated soil, FeSO\({}_{4}\)-stabilized soil, and CaS\({}_{5}\)-stabilized soil are shown in Figure 7. In Cr(VI)-contaminated soil, illite, quartz, albite, and calcite were identified as the major phases. In the FeSO\({}_{4}\)-stabilized soil, ferric hydroxide (Fe(OH)\({}_{3}\)) and chromium hydroxide (Cr(OH)\({}_{3}\)) were identified. For the CaS\({}_{5}\)-stabilized soil, sulfur (S) and ettringite (Ca\({}_{6}\)Al\({}_{2}\)(OH)\({}_{12}\)(SO\({}_{4}\))\({}_{3}\).26H\({}_{2}\)O) were identified. The differences in the leachability, bioaccessibility, and speciation distribution of Cr in the FeSO\({}_{4}\)- and CaS\({}_{5}\)-stabilized soils can be attributed to (1) the different hydration products in the FeSO\({}_{4}\)- and CaS\({}_{5}\)-stabilized soils, and (2) the different pH conditions and Eh in the FeSO\({}_{4}\)- and CaS\({}_{5}\)-stabilized soils. [PERSON] et al. [36] suggested that Cr(VI) was reduced to Cr(III) by FeSO\({}_{4}\) and formed Cr(III)-Fe(III) hydroxide precipitation (Cr(OH)\({}_{3}\) and Cr\({}_{x}\)Fe\({}_{1-x}\)(OH)\({}_{3}\)). [PERSON] et al. [37] found that Cr(OH)\({}_{3}\) and Cr\({}_{x}\)Fe\({}_{1-x}\)(OH)\({}_{3}\) were disintegrated and desorbed Cr(III) under strong acid conditions. Therefore, the Cr(OH)\({}_{3}\) and Cr\({}_{x}\)Fe\({}_{1-x}\)(OH)\({}_{3}\) were disintegrated and desorbed Cr(III) in the SBET test. For the CaS\({}_{5}\)-stabilized soil, the elemental sulfur precipitated and ettringite formed by the reaction of CaS\({}_{5}\) with Cr(VI) [10]. Sulfur and ettringite were the key compounds responsible for Cr(III) immobilization [38]. [PERSON] et al. [38] suggested that the ettringite increases Cr(III) uptake into the matrix, making it difficult for Cr(III) to be released from the soils during the leaching test. [PERSON] et al. [10] and [PERSON] et al. [39] found that Cr(III) was bound to sulfides or adsorbed. [PERSON] et al. [40] found that the leachability of Cr(III) was strongly dependent on solution pH, soil pH, and redox potential. The Cr solubility increased at low pH values, specifically at pH values below 6, because amorphous Cr(OH)\({}_{3}\) dissolves to form the soluble chromium hydroxide cations Cr(OH)\({}_{3}\), Cr(OH)\({}_{2}\)\({}^{+}\), and Cr(OH)\({}^{2+}\). The lowest leachability of Cr was observed between pH 6 and 11. Insoluble and amorphous Cr(OH)\({}_{3}\) forms were observed at a pH of approximately 8.0 and under reducing (-Eh) conditions. Compared with TCLP, the SBET method uses extreme conditions, a more aggressive extracting agent (pH 1.5), and a higher liquid-to-soil ratio (100:1) to simulate the gastrointestinal environment [41; 42].
## 2 Materials and Methods
### Catalytic Activity Measurements  
The redox properties of the samples and their catalytic activity in ammonia oxidation were studied by temperature-programmed reaction (TPR-NH\({}_{3}\)+O\({}_{2}\)) and temperature-programmed reduction (TPR-H\({}_{2}\)). The experiments were carried out in an automatic setup equipped with a plug flow quartz reactor with a FTIR spectrometer (I1801, MIDAC corp., USA) and a gas chromatograph (Crystal 2000 M, CHROMATEC). The reaction mixture containing 0.1 vol.% NH\({}_{3}\), 4.0 vol.% O\({}_{2}\) (balance He) was introduced at a rate of 500 cm\({}^{3}\)/min and GHSV-120 000 h\({}^{-1}\). Each sample (0.145 g) was heated twice in the NH\({}_{3}\)+O\({}_{2}\) mixture from room temperature to 400 \({}^{\circ}\)C. Analysis of H\({}_{2}\) consumption was performed using mass spectrometry (QMS200 high pressure sampling system, Stanford Research Systems, Sunnyvale, CA, USA). The setup description and experimental conditions are presented in more detail in [6].
# A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
## 3 Proposed Binary QUATRE Algorithm
### Mathematical Analysis  
Suppose that the position matrix at \(G\) generation \(X_{G}\) of BQUATRE is made up of 0 or 1. So the \(X_{r1,G}\), \(X_{r2,G}\), \(X_{r3,G}\), \(X_{r4,G}\), \(X_{r5,G}\) and \(X_{gbest,G}\) at \(Gth\) generation are also binary matrixes. The mutation matrix \(B\) is calculated according to the schemes shown in Table 1. However, the range of scale factor \(F\) is \([0,2]\). Therefore, the mutation matrix \(B\) will not be a binary matrix, and the position matrix \(X_{G+1}\) at \((G+1)th\) generation obtained by Equation (1) will also not be a binary matrix. In order to further analyse, we choose the first mutation strategy in Table 1 as an example, which is Equation (9).  
\[B=X_{gbest,G}+F*(X_{r1,G}-X_{r2,G}) \tag{9}\]  
There are eight combinations about the values of \(X_{gbest,G}\), \(X_{r1,G}\) and \(X_{r2,G}\), which are described as follows:  
(1) if \(X_{gbest,G}=0\), \(X_{r1,G}=0\) and \(X_{r2,G}=0\).  
(2) if \(X_{gbest,G}=0\), \(X_{r1,G}=0\) and \(X_{r2,G}=1\).  
(3) if \(X_{gbest,G}=0\), \(X_{r1,G}=1\) and \(X_{r2,G}=0\).  
(4) if \(X_{gbest,G}=0\), \(X_{r1,G}=1\) and \(X_{r2,G}=1\).  
(5) if \(X_{gbest,G}=1\), \(X_{r1,G}=0\) and \(X_{r2,G}=0\).  
(6) \(X_{gbest,G}=1\), \(X_{r1,G}=0\) and \(X_{r2,G}=1\).  
(8) \(B=1+F*(0-1)=1-F\), thereby \(B\in[-1,1]\).  
(7) if \(X_{gbest,G}=1\), \(X_{r1,G}=1\) and \(X_{r2,G}=0\).  
(8) \(B=1+F*(1-0)=1+F\), thereby \(B\in[1,3]\).  
(8) if \(X_{gbest,G}=1\), \(X_{r1,G}=1\) and \(X_{r2,G}=1\).  
\(B=1+F*(1-1)=1\).  
From the above analysis, it can be concluded that \(B\in[-2,3]\). The same conclusion can be drawn from mutation strategies QUATRE/target/1 and QUATRE/best/1. In the last three mutation strategies, QUATRE/target/2, QUATRE/rand/2 and QUATRE/best/2, it can be concluded that \(B\in[-4,5]\) by the same way.
## 4 Results and Discussion
### Structural Equation Modeling (SEM) and Hypothesis Testing
The results for complete data were (\(\beta\) = 0.423, t = 7.16, \(p\) < 0.001), for the United Kingdom were (\(\beta\) = 0.525, t = 6.047, \(p\) < 0.001), and for Turkey were (\(\beta\) = 0.239, t = 3.762, \(p\) < 0.001). Therefore, H3 was supported. The relationship between perceived sociability (PS) and behavioral intention (BI) to use the robot as a co-worker was positive and significant for complete data, and those from the United Kingdom and Turkey. The results for complete data were (\(\beta\) = 0.189, t = 4.033, \(p\) < 0.001) and for the United Kingdom were (\(\beta\) = 0.159, t = 2.527, \(p\) = 0.012). The results for Turkey were (\(\beta\) = 0.335, t = 5.819, \(p\) < 0.001). Therefore, H4 was supported. The structural model of behavioral intention to use the robot as a co-worker is shown in Figure 5. These findings validate the theoretical framework of UTAUT and extend its applicability to office environments by incorporating perceived sociability as a critical factor. The results suggest that robot acceptance is multifaceted and influenced not only by functional attributes like performance expectancy but also by social and cultural dimensions. These insights have implications for designing and implementing robots in workplace settings, emphasizing the need for user-centered designs and supportive organizational policies.