esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Growth Parameters
Under Cd-stress, the improvement in root fresh weight due to inoculation with rhizobacterial strains ranged from 29.8% to 50% with maximum improvement due to inoculation with FN13. Under unstressed conditions, the maximum improvement (33.2%) in root fresh weight was also observed due to inoculation with FN13 followed by 25.5% and 24.4% improvement in root fresh weight by the strain FN16 and FN14, respectively. Data showed that Cd-stress significantly decreased the root dry weight of _Brassica juncea_ (L) plants with a reduction of 29.8% over the unstressed control plants (Figure 2B). Inoculation with rhizobacterial strains had a significant effect on root dry weight of _Brassica juncea_ (L.) grown under unstressed conditions and Cd-stress. The impact, however, was more prominent under Cd-stress where an increase of 32.8% to 54.3% was observe than the control. Under unstressed conditions, the inoculation gave up to 32.2-37.6% increase in root dry weight when compared with uninoculated control. Maximum improvement (37.6%) in root dry weight under unstressed conditions was observed due to inoculation with FN13. Under Cd-stress, maximum improvement (54.3%) in root dry weight was noted due to inoculation with FN13 followed by 48.5% in the treatment with inoculation of FN14. Data showed that total dry biomass was decreased due to Cd-stress however this decrease was non-significant when compared with unstressed control plants (Figure 2C). Inoculation with Cd-tolerant, IAA-producing rhizobacterial strains lessen the negative effect of Cd-stress thus resulted in improvement of total dry biomass. Inoculation with these strains was, however, more effective in enhancing the total dry biomass under unstressed conditions than Cd-stressed conditions. Under Cd-stress, the maximum response was given by the strain FN13 that was non-significantly related to other strains and control plants. Under unstressed conditions, the maximum improvement (41.8%) in total dry biomass was observed due to inoculation with rhizobacterial strain FN13 that showed significantly better results than the respective control. The strain FN14 also showed significantly better results in improving the total dry biomass of _Brassica juncea_ (L.) seedlings than control
## 1 Introduction
Figure 1: (a) The Model\(\Leftrightarrow\)Concept duo extended to (b) Model\(\rightarrow\)View\(\rightarrow\)Concept trio extended as a (c) Concept\(\rightarrow\)Model\(\rightarrow\)Graph\(\rightarrow\)View Cycle (CMGVC). The categorical equivalence of programs, concepts, and theories has inspired us to assert that modeling languages are categories, and that mappings within and between modeling categories are powerful means of robust model transformations for various applications. This assertion substantiates a formal, holistic approach for perceiving and implementing concept transformations, particularly in complex systems. Such an approach is instrumental for reasoning about complex concepts using the reasoning mechanisms of people's choice, rather than the representation mechanisms originally used for capturing those concepts. In complex systems, this idea of concept representation interchangeability is critical for a paradigm shift, cohesion of modeling and reasoning practices, and enabling digital systems engineering [10]. This paper advances the state of the art in several ways: (a) asserting and proving that modeling languages are categories; (b) augmenting the emerging model-based systems engineering paradigm with robust foundations using Category Theory, rather than supplanting such system representation and analysis approaches with categorical or otherwise-algebraic representations; (c) offering a holistic systems perspective over the entire life cycle, metamorphosis, and evolution of the concept, from its inception, through modeling, generic representation, and informative rendering; (d) providing mathematical evidence that this approach is superior to both direct and language-bound mappings, (e) synergistically integrating and fusing category theory, graph theory, modeling theory, systems theory, and informatics to facilitate a multi-faceted pipeline of representations, transformations, and visualizations, which is a critical building-block for the digital engineering paradigm; and (f) providing a concrete example for a concept transformation cycle that renders conceptual models built with Object-process Methodology (OPM) as graph data structures, and graph data structures as stakeholder-informing matrices, in a significantly more structured and robust manner, which allows for both additional visualization of OPM models and matrix visualization of graphs of other MLs. The rest of this paper is organized as follows: we review related work, relevant MLs, Graph Theory, and Category Theory in Section 2. We describe the methods to conduct the CMGVC in Section 3. We briefly describe the software implementation of the framework in Section 4. We assess the framework in Section 5. We demonstrate our method in Section 6, and discuss the results in Section 7. Conclusions, and potential, ongoing, and future research appear in Section 8.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 4 Ceiling Analysis  
Ceiling analysis is a method of identifying the weakest component in the system, and then optimizing that component to improve the performance of the total system. In the present work, the ceiling analysis is applied to an ALPR which was previously reported in [3] to identify potential improvements in various components of the system, detection, segmentation, and recognition. This is typically achieved by manually replacing the inputs  
Figure 1: Examples of Jordanian license plates for private vehicles. (**a**) American standard (34 \(\times\) 22 cm). (**b**) European standard (52 \(\times\) 11.4 cm).  
Figure 2: Samples of the collected dataset elements, taken at/for: (**a**) a sunny day, (**b**) different vehicles’ categories, (**c**) different lighting conditions, (**d**) vehicles in movement, and (**e**) different tilt angles.  
of each component with artificial inputs to provide 100% accurate predictions with that component. The overall improvement of the system performance can then be observed, component by component.  
In this work, the ALPR system under study was initially tested with real inputs using the study dataset. This test revealed that the overall accuracy of the system was approximately 52%. Next, the ceiling analysis was carried out for each component as follows:  
* Detection--for this stage, a total of 500 vehicles' images were used as test input and the correctly detected license plate images were considered as an output. The system detected 410 correct license plates that represented about 82%, compared to the ground truth (100%).
* Segmentation--the license plate images were manually extracted from the vehicles' images and used as a test input for segmentation. In this test, the system correctly segmented 475 images that represented 95% of the input images, compared to the ground truth.
* Recognition--a total of 3365 character-images were manually extracted from 500 license plate images and used as test input at this stage. The system correctly recognized 3254 characters which represented 96.7% of the input character-images, compared to the ground truth.  
Table 1 summarizes performance comparisons for both actual and artificial inputs for the ALPR system under study. As expected, the artificial inputs used in this analysis resulted in a significant potential for performance improvement as compared to that of the original system [3]. More importantly, the possible improvement margins for individual system components were extremely diverse, with detection (40.2%), segmentation (13%), and recognition (1.7%). These findings suggest that the detection component has the most potential for development, followed by segmentation and finally the recognition component.
## 2 System Model
### UAVs and User Distribution Model  
Without loss of generality, we assume all UAVs always hover at a constant height \(H\). During the interested time slot, the positions of users are considered to be quasistatic, while all the horizontal positions of UAVs can be changed over time.  
We denote the \(i\)th UAV as UAV\({}_{i}\), \(i\in\mathcal{M}\) and the \(j\)th user as UE\({}_{j}\), \(j\in\mathcal{N}\). The position vectors of UAV\({}_{i}\) and UE\({}_{j}\) are denoted by \(\mathbf{v}_{i}=(x_{i}^{\mathbf{v}},y_{i}^{\mathbf{v}},H)\) and \(\mathbf{u}_{j}=(x_{i}^{\mathbf{v}},y_{i}^{\mathbf{v}},0)\), respectively. For convenience of the subsequent formula expressions, we denote the position vector of the n\({}_{i}\)th antenna of UAV\({}_{i}\) as \(\mathbf{v}_{in_{t}}=(x_{in_{t}}^{\mathbf{v}},y_{in_{t}}^{\mathbf{v}},H)\), \(n_{t}=1,2,\ldots,N_{T}\), and if UE\({}_{j}\) is required for the \(k\)th content, denote the position of UE\({}_{j}\) as \(\mathbf{u}_{jk}=(x_{jk}^{\mathbf{v}},y_{jk}^{\mathbf{v}},0)\), where \(k\in\mathcal{K}\).  
Figure 1: Illustration of the cache-enable UAV-assisted wireless system.  
We assume that each UAV serves several nearest users and each user could be served by several UAVs. For simplicity, the user association is considered to be fixed, which means the connection between UAVs and users would be unchanged during the interested time. The connection relationship between UAV\({}_{i}\) and UE\({}_{j}\) is denoted by \(q_{ij}\). \(q_{ij}=1\) represents that a connection exists, and \(q_{ij}=0\) represents that a connection does not exist. That is,  
\[q_{ij}=\left\{\begin{array}{rl}&1,\text{ connection exists}\\ &0,\text{ connection does not exist}.\end{array}\right. \tag{1}\]  
The set of users served by UAV\({}_{i}\) is denoted as \(A_{i}\), \(i\in\mathcal{M}\), and \(A_{i}\subseteq\mathcal{N}\).
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 4 Conclusions  
In this paper, a model-free NRF identification approach was proposed basing on the principle of EKF. The NRFs provided by the nonlinear components such as the MR damper and SMA damper were treated as 'unknown fictitious input', and thus, no prior assumptions or approximations for the NRF model were required. A modified version of observation equation was obtained on the basis of the usage of a projection matrix. The recursive solution of the proposed approach was analytically derived. The structural parameters, including the stiffness and damping coefficients, were identified at each time step. The NRFs were also identified by means of LSE at the same time. The effectiveness and robustness of the proposed approach was numerically validated via a six-story building structure equipped with MR damper and/or SMA damper. A further comparison of the proposed approach with the PSPM method was made to demonstrate the reliability of the proposed approach for the identification of NRFs around the unsmooth points. The results show that the proposed approach is  
Figure 8: Comparison of NRFs: (**a**) hysteretic loop of SMA damper; (**b**) time history of NRF by SMA damper; (**c**) hysteretic loop of MR damper; (**d**) time history of NRF by MR damper.  
Figure 7: The identified stiffness (building with SMA damper and MR damper): (**a**) first floor; (**b**) fourth floor.  
capable of satisfactorily identifying the structural parameters and unknown NRFs provided by the nonlinear components.  
Although no prior knowledge of NRF model is required, the proposed approach should be performed under the condition that the influence matrix \(\eta^{\text{u}}\) associated with NRFs is available. Nonlinearities may often be located easily, at least for simple structures [3]. However, for complex structural systems, it is difficult to precisely determine the spatial localization of nonlinearities. Therefore, further research can be conducted towards the model-free NRF identification without the assumption of influence matrix being available.  
[PERSON] presented the framework of the proposed approach and wrote the paper; [PERSON] and [PERSON] gave the numerical examples for the validation of the proposed approach and participated in paper revision; [PERSON] showed the suggestions for the development of the algorithm and polished the manuscript.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China through grant No. 751201239. The support from the Natural Science Foundation of Hunan Province (No. 2018 JJ3054) and the Fundamental Development Funds for Young Researchers of Hunan University (No. 531107050912) are also greatly appreciated.  
The authors declare no conflict of interest.
# EmbeddedPigDet--Fast and Accurate Pig Detection for Embedded Board Implementations
## 4 Experimental Results
### Evaluation of Detection Performance  
The main steps of the proposed method are to create a composite image from infrared input images using the image preprocessing (IP) module and then run EmbeddedPigYOLO with the composite image. Figure 6 shows the results of detecting pigs through EmbeddedPigDet (i.e., IP + EmbeddedPigYOLO). To evaluate the effect of the IP module of EmbeddedPigDet qualitatively, the results of TinyYOLO with the infrared images were also shown in Figure 6. The 2 AM and 8 PM images were night-time images and there were many pigs lying on the floor. Therefore, in these images, pixel values of pigs and background had similar values. On the other hand, the 8 AM and 2 PM images were daytime images. There was sunlight in the room and pigs moved relatively a lot in the daytime images. As we confirm in Figure 6, EmbeddedPigDet performed well in difficult detection situations, such as similar pixel values with background and sunlight.  
In fact, this detection accuracy of EmbeddedPigDet was largely due to the IP module. In order to evaluate the effect of the IP module quantitatively, we compare the quality of the infrared input images and the three-channel composite images by using the following metrics:  
\[mean=\frac{1}{MN}\sum_{i=0}^{N-1}\sum_{j=0}^{M-1}I_{ij} \tag{1}\]  
Figure 6: Detection results of TinyYOLOv2 with infrared images and EmbeddedPigDet(v2) with composite images.  
where \(I_{ij}\) is the image value at \((i,j)\) of \(N\times M\) image.  
\[contrast=\sqrt{\frac{1}{MN}\sum_{i=0}^{N-1}\sum_{j=0}^{M-1}{(I_{ij}-mean)^{2}}} \tag{2}\]  
\[entropy=-\sum_{k=0}^{255}P_{k}log_{2}P_{k} \tag{3}\]  
where \(P_{k}\) is the proportion of the pixel value \(k\).  
Table 4 compares the values of mean, contrast and entropy of the input image with those of the composite image. At 8 AM, note that, there was strong sunlight in the input image through a window in the pig room and thus the values of mean, contrast and entropy of the input image at that time period were relatively larger than those at other time periods. Based on the difference between mean values of the input and composite images, we could confirm that, generally, the darker input image became brighter in the composite image after the IP module (see Figure 6). Also, larger values of contrast and entropy generally indicate better image quality. Because the values of contrast and entropy were increased after the IP module, better image quality of the composite image could help to improve the detection accuracy.
# Edible Films Based on Ovine Second Cheese Whey with Oregon Essential Oil
## 3 Results and Discussion
### Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c} \hline
**Film** & **Tensile Strength at Maximum (MPa)** & **Elongation at Break (\%)** & **Puncture Strength (MPa)** & **Puncture Deformation (\%)** & **Elastic Modulus (MPa)** \\ \hline WPI & 1.57 \(\pm\) 0.73 ef & 52.17 \(\pm\) 17.32 ef & 2.40 \(\pm\) 0.66 e & 18.92 \(\pm\) 5.74 h & 7.41 \(\pm\) 2.08 a \\ WS 2:1 & 1.17 \(\pm\) 0.18 de & 57.26 \(\pm\) 14.77 ef & 1.74 \(\pm\) 0.14 d & 15.71 \(\pm\) 3.89 gh & 15.00 \(\pm\) 8.45 bc \\ WS 1:1 & 1.19 \(\pm\) 0.22 de & 45.20 \(\pm\) 9.83 de & 1.87 \(\pm\) 0.36 de & 9.57 \(\pm\) 2.40 def & 11.24 \(\pm\) 2.60 abc \\ WS 1:2 & 0.46 \(\pm\) 0.14 ab & 19.56 \(\pm\) 7.79 abc & 0.51 \(\pm\) 0.16 abc & 3.55 \(\pm\) 1.33 abc & 16.75 \(\pm\) 4.22 c \\ SCW & 0.17 \(\pm\) 0.05 a & 3.57 \(\pm\) 1.18 a & 0.20 \(\pm\) 0.03 a & 0.93 \(\pm\) 0.28 a & 9.25 \(\pm\) 4.13 ab \\ WS 2:1O1 & 1.02\(\pm\) 0.25 cd & 30.32 \(\pm\) 9.27 bcd & 2.07 \(\pm\) 0.59 de & 12.34 \(\pm\) 2.42 fg & 12.29 \(\pm\) 3.04 abc \\ WS 1:101 & 0.61 \(\pm\) 0.12 abc & 34.32 \(\pm\) 10.99 cd & 0.94 \(\pm\) 0.11 bc & 7.62 \(\pm\) 1.11 cd & 8.07 \(\pm\) 2.99 a \\ WS 1:2O1 & 0.41 \(\pm\) 0.08 a & 15.01 \(\pm\) 4.65 ab & 0.47 \(\pm\) 0.11 ab & 2.56 \(\pm\) 0.95 ab & 7.09 \(\pm\) 1.54 a \\ WS 2:1O2 & 1.90 \(\pm\) 0.36 f & 68.22 \(\pm\) 14.30 f & 1.77 \(\pm\) 0.48 d & 11.14 \(\pm\) 3.47 ef & 14.66 \(\pm\) 3.27 bc \\ WS 1:1O2 & 0.95 \(\pm\) 0.16 bcd & 27.59 \(\pm\) 8.70 bc & 1.05 \(\pm\) 0.17 c & 5.52 \(\pm\) 0.94 bcd & 9.91 \(\pm\) 2.08 ab \\ WS 1:2O2 & 0.55 \(\pm\) 0.09 abc & 17.98 \(\pm\) 3.34 abc & 0.66 \(\pm\) 0.13 abc & 3.57 \(\pm\) 1.00 abc & 7.54 \(\pm\) 2.37 a \\ \hline \end{tabular}
\end{table} 
Table 5: Mechanical properties (mean \(\pm\) SD) of films described in material and methods section. The addition of EO also significantly influenced the mechanical properties of the films. It was observed that films with 1% of EO showed lower values of tensile strength and elongation at break than films without EO; however, the films with 2% EO showed higher values of tensile strength than the corresponding films with 1% EO. [PERSON] et al. [16] observed the different evolution of these parameters, depending on the amount of oregano essential oil. They detected increases in tensile strength and elongation percentage with the addition of up to 2% of EO in WPI films, but that the incorporation of 3% decreased both values [16]. It has been observed that the addition of other essential oils increases the tensile strength and the elongation of the films [32; 33; 35; 36] and has been explained by the way these compounds can become intercalated between the whey proteins, acting as plasticizers [32]. The incorporation of EO significantly decreased the values of puncture deformation of the films.
# A New Chaotic-Based Approach for Multi-Objective Optimization
## 3 Scalarization-Based Chaotic Search  
### Tchebychev Scalarization Approaches  
The aggregation (or weighted) method is one of the most popular scalarization method for the generation of Pareto optimal solutions. It consists in using an aggregation function to transform a \(MOP\) into a single objective problem \((MOP_{\lambda})\) by combining the various objective functions \(f_{i}\) into a single objective function \(f\) generally in a linear way.  
The first proposed scalarization approach (Multi-Objective Fractal Decomposition Algorithm Scalarization) uses the Tchebybycheff function [10; 25]. It introduces the concept of ideal point or reference point \(z_{i}^{*}\) as follows:  
\[\begin{array}{ll}\text{{Minimize}}&\max\limits_{i=1,...,k}&[\omega_{i}(f_{i}( x)-z_{i}^{*})]\\ \text{Subject to}&x\in X\end{array} \tag{30}\]  
where \(z^{*}=(z_{1}^{*},...,z_{k}^{*})\) is the reference point, and \(\omega=(\omega_{1},...,\omega_{k})\) is the weight vector.  
There have been numerous studies of decomposition approaches to use different types of reference points for providing evolutionary search directions. According to the position of reference point relative to the true PF in the objective space, we consider here three Tchebychev approaches:  
* The Standard Tchebychev approach (TS) that consider the utopian point as the reference point.
* The modified Tchebychev variant (TM) that consider multiple utopian reference points instead of just one reference point [26] (see Figure 12).
* The augmented Tchebychev variant (AT) which is defined by the augmented Tchebychev function defined as [27]: \[\begin{array}{ll}\text{{Minimize}}&\max\limits_{i=1,...,k}&[\omega_{i}(f_{ i}(x)-z_{i}^{*})]+\rho\sum\limits_{i=1}^{m}\omega_{i}|z_{i}^{*}-f_{i}(x)|\\ \text{Subject to}&x\in X\end{array}\] (31)
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 3 Data and Methodology  
Depending on the type of data being studied and the company or financial scenario, several forecasting models may be employed. For instance, one may utilize statistical models, machine learning techniques, or professional judgment. As substitutes for traditional methods, Recurrent Neural Networks (RNNs) and their variations, such as Long Short-Term Memory Networks (LSTMs), have been suggested for modeling intricate sequential data such as natural language, audio waves, and video frames. Studies conducted recently have demonstrated the ability of LSTMs to capture complex temporal patterns in dynamic time series and apply them to forecasting tasks [27; 28]. By using an LSTM encoder-decoder model to map the past of a time series to its future, sequence-to-sequence learning can naturally formulate multi-step forecasting.  
Multi-horizon forecasting is a common practice that involves making forecasts at various time horizons, from short-term predictions for the coming days or weeks to long-term predictions for several years. One of its many benefits is that, over a certain time frame, it offers directions for scheduling resources and making decisions [29]. The objective of multi-horizon forecasting is to give businesses and investors a thorough understanding of potential trends and changes, so that they can make wise judgments. In volatile or fast-changing contexts, the neural network architecture called Temporal Fusion Transformers (TFT), developed by [30], was applied in the present study. The novelty of this neural network structure is its ability to integrate other network architectures such as LSTM layers and attention heads. Three major blocks underlie the architecture of this neural network, namely:  
* A multiheaded temporal attention block whose purpose is to identify long-range patterns present in the time series;
* Sequence-by-sequence LSTM encoder-decoders to account for the short-range patterns present in the time series;
* Residual network blocks (GRN) whose function is to eliminate unimportant and unused inputs to the neural network.  
Another important feature of TFT is the possibility of determining the weight or contribution of explanatory variables to network performance, similar to the determination of statistical significance in econometric regression models, but with the difference that no distributional assumption is required.
## 4 Experiments
### Ablation Studies

Table 3: Ablation study on the noise robustness of WDARFNet. The DIOR-R dataset was utilized in this experiment. Figure 8: Visualization analysis of heatmaps illustrating feature activation for ResNet50 and WDARFNet, derived from clean (**top**) and noisy (**bottom**) images. This comparison highlights the improved noise suppression and object focus of WDARFNet.