esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Length Range in Slope Evaluation  
Range classification in slope evaluation needs to be considered because GGSJs of longer lengths are managed as single management areas. Three criteria for dividing an evaluation range were considered in this study based on expert interviews and survey results. First, the evaluation unit (extension standard) used by the Facilities Corporation was determined, with the evaluation being conducted at intervals. Three out of twelve people opted for the method, and the average grade of the entire slope was lowered in the case of a slope spanning 500 m, such as the one shown in Figure 12. There is a problem in that subjectivity is involved in determining the section to be designated. If the average grade is lowered, there is room for exclusion from the designated management list despite the area being at high risk. Opposite cases may also occur despite low risk.  
Second, an evaluation target was evaluated by separating it relative to a valley. Four out of twelve people opted for this method, and it was the most appropriate method for distinguishing the range. This is also the method used by the MOIS. However, it is necessary to separate the start and end points for the valley and to evaluate the slope between valleys when evaluating such a site. The subjective judgment of the evaluator determines this. Structures and safety levels differ between valleys. Furthermore, it is easy to select the criteria of the evaluation index if one side is in the same state as that in Figure 12A. However, if the structure or safety grade is different, such as Figure 12B,C, the criteria for evaluation will be disrupted. The right part of the slope (B) has a high stability grade because the gabion and concrete wall improve safety. The left part has the same shape as an unstable slope because there is only a small concrete wall at the bottom. Thus, the safety of slope (B) cannot be ascertained. If the average is used, grade D can be upgraded to grade C. However, disasters are not managed this way. It is best to reduce the damage, regardless of cost. Disaster prevention is possible only when approached with a methodology for preventing maximum losses. Therefore, the most reasonable method to suggest for use as a standard is to select the evaluation criteria based on the most dangerous  
Figure 12: GGSJ management district with a length of 500 m.  
point of the slope. In addition, the method selected by eight out of twelve experts must be considered. In selecting the extension, condition, and length of a range, without subjective judgment, it is possible to make the selection according to the detailed description for a photo and a guide, thus preventing intentional damage. This can lead to consistent evaluation grade determination for GGSJs nationwide for the effective management of GGSJ DBs. It is necessary to evaluate a critical point to be managed, where maintenance projects can be achieved, so as to prevent disasters.
## 4 A Hairy Root Platform for the Production of Bioactive Abietane Diterpenes
### Metabolic Engineering to Enrich the Content of Abietane Diterpenes in S. sclarea Hairy Roots
#### 5.2.4. Co-Expression of _Ggpp_s and _Cpps_ Genes  
Despite the success obtained by overexpression single genes, frequently, the final amount of a targeted metabolite is limited by multiple enzymatic activities. Therefore, the concerted regulation of two or multiple genes would be more appropriate to significantly obtain higher content of a desired compound. Co-expression of rate-limiting biosynthetic genes has been successfully applied to boost the synthesis of a variety of high-value plant-derived compounds [69, 75]. Considering the significant increase in aethiopinone content obtained by overexpression of _GGPPS_ and _CPPS_ genes individually, these two biosynthetic genes were co-expressed in _S. sclarea_ HRs [40]. This \"push and pull\" strategy induced an increase in the aethiopinone content by 6 times, against the 8- and 10-fold increase obtained by overexpressing the two genes singularly (Table 2). This might be probably caused by an unbalanced simultaneous high level of _GGPPS_ and _CPPS_ transcripts, and an impaired ratio of the levels of the two encoding enzymes and/or relative substrates. To better understand these unexpected results, it would be useful to determine the relative level of GGPPS and CPPS proteins and their relative enzymatic activities in order to identify overexpressing HR lines with a balanced level of both enzymes, which could affect additively or synergistically the final yield of aethiopinone and other bioactive diterpenes in _S. sclarea_ HRs [40].
## 2 Materials and Methods
### Image Classification Methods  
#### Convolutional Neural Networks  
The CNN plays a pivotal role in many deep learning models in computer vision. A CNN typically consists of several key layers, as shown in Figure 4, including convolutional, pooling, and fully connected layers [28]. Convolutional layers use filters to create feature maps by detecting patterns like edges and textures. An activation function like ReLU (Rectified Linear Unit) [29] adds non-linearity. Pooling layers reduce the spatial dimensions of feature maps to improve efficiency and prevent overfitting, with common methods being max and average pooling.  
High-level reasoning is performed in fully connected layers, which flatten the input and produce the final output. Dropout layers can be added to prevent overfitting by ignoring random activations during training, and batch normalization helps the network learn faster and more stably.  
Techniques like data augmentation, transfer learning, and advanced architectures like ResNet, VGG, and Inception have further enhanced the CNN's capabilities and applications.  
Figure 3: Class distribution of tomato leaf diseases dataset from Kaggle, highlighting data imbalance. All the data classes are listed as (a) Bacterial spot; (b) Early blight; (c) Late blight; (d) Leaf Mold; (e) Septoria leaf spot; (f) Spider mites; (g) Target spot; (h) Tomato yellow leaf; (i) Tomato mosaic virus; (j) Healthy, and (k) Powdery mildew.
# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 2 Related Work
### Deep Learning for Handwriting Digit Recognition  
For handwriting digit recognition, In 2010, [PERSON] et al. [10] represented handwritten Arabic numerals using an algorithm consisting of 88 features. They used a Multilayer Perceptron (MLP)-based classifier to recognize handwritten Arabic digits using the CMATERDB 3.3.1 dataset. An experiment involving 3000 samples showed that the technique yielded an average recognition rate of 94.93%. In 2017, [PERSON] et al. [11] presented a novel algorithm for deep learning neural networks that used an activation function based on ReLU activation and a regularization process that used dropout. Moreover, the authors modified the method outlined in [10] by implementing dropout regularization in the MLP model to reduce the overfitting between the fully connected layers. Overall, there were 10 neurons in the output layer with softmax activation. The CMATERDB 3.3.1 handwritten Arabic digits dataset was used to test all the models. The proposed model in the study had 93.8% accuracy. In 2017, [PERSON] et al. [12] proposed an algorithm combining RBMs and CNNs to recognize handwritten Arabic digits. In particular, they presented a two-phase approach to Arabic digit recognition. First, features were extracted from the raw data using an RBM. Then, a deep supervised learning architecture was used to train and test the extracted features. Similar to the previous study, the CMATERDB 3.3.1 handwritten Arabic digit dataset was used to train and test the proposed algorithm, which achieved an accuracy of 98.59%. In 2017, [PERSON] et al. [13] proposed an alphanumeric VGG net design with 13 convolutional layers, two max-pooling layers, and three fully connected layers for alphanumeric character recognition. To avoid overfitting, regularization dataset augmentation and dropout were used. Their VGGnet was based on the VGGNet standard model. Furthermore, their methodology employed the ADBase for digits and the HACDB for characters. For the ADBase database, the model was 99.57% accurate, while for the HACDB database, it was 97.32% accurate. In 2018, [PERSON] et al. [14] proposed a CNN model for recognizing handwritten digits in different languages: Eastern Arabic, Persian, Devanagari, Urdu, and Western Arabic. The proposed deep learning model was tested on various datasets. For the multilanguage database, the proposed algorithm achieved an accuracy of 99.26% and a precision of 99.29% on average, while an average accuracy of 99.32% was achieved for each language individually. In 2019, [PERSON] et al. [15] modified the CNN model for recognizing handwritten Arabic numerals proposed in [11] by adding data augmentation to the CMATERDB 3.3.1 dataset as well as changing the activation function from a ReLU to an exponential linear unit (ELU). Compared to previous work on this dataset, the model achieved a high accuracy of 99.4%. Table 1 summarizes these deep learning-based handwritten digit recognition studies.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 4 Multi-Carrier Transmission Schemes with Dimming Control
### Aco-Ofdm
#### 4.2.2 Adaptive SPAD Receivers
Hence, the transmission data rate for the DCO-OFDM system can be written as  
\[R_{\rm max}^{\rm DCO}=\frac{\log_{2}(M)(K/2-1)}{KT_{\rm s}}. \tag{55}\]  
As only the odd subcarriers are being transmitted by ACO-OFDM, its throughput will be approximately half of that of DCO-OFDM, i.e., \(R_{\rm max}^{\rm ACO}=\frac{R_{\rm max}^{\rm DCO}}{2}\).
# Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete
## 3 Numerical Modeling and Characterization
### Probabilistic Characterization Method and CT Measurement  
#### 3.2.1 Lineal-Path Function  
The main objective of this study is to investigate the correlation between the size characteristics and the properties of the LWC specimens with different gradings. Therefore, to characterize the LWA size distribution, a proper description method is needed. The lineal-path function, a type of low-order probabilistic function, can be used to describe the continuous connectivity of a specific component, such as aggregate and pore [43; 44; 45]; this function can be utilized to characterize the aggregate size distribution. The lineal-path function, \(L_{lnu}(r)\), is the probability that a randomly distributed line segment with length \(r\) is located in the LWA phase, which is denoted as \(lwa\). \(L_{lnu}(r)\) approaches the volume ratio of the lightweight aggregates as the line length becomes zero, while the probability of the whole line segment is in the lightweight aggregate approaches zero with the line length going to infinity as:  
\[\lim_{r\to 0}L_{lnu}(r)=f_{lnu},\qquad\lim_{r\to\infty}L_{lnu}(r)=0, \tag{4}\]  
where \(f_{lnu}\) is the LWA volume ratio in the specimen. Here, this function is adopted to describe the relative LWA sizes in the specimens.
# Complex Human-Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach
## 3 Proposed System  
In this paper, we propose a system for recognizing complex human-object interaction based on usage information for the objects involved. For this purpose, we apply a deep convolutional neural network (DCNN) over the spatio-temporal features extracted from information on human joints and the objects. We also apply a multi-class support vector machine (multi-class SVM) over the object usage probability (OUP) features in order to apply probability information for humans interacting with each object. The architecture of the proposed system is shown in Figure 1. The proposed system's architecture consists of: (i) input data acquisition, (ii) temporal segmentation, (iii) creating the input data for DCNN, (iv) training and recognizing interactions using DCNN, (v) extracting the object usage probability (OUP), (vi) training and recognizing interactions based on OUP by using a multi-class SVM, and finally (vii) a decision based on fusing the results of DCNN and multi-class SVM to produce the final result for recognizing human-object interactions. The main contributions and significant differences between this proposed system and our previous works [14; 15] are as follows:  
* Recognition of human-object interactions in which humans interact with different objects in order to complete desired tasks, such as making cereal or microwaving food.
* Creation of input data for a DCNN, which can accurately represent interactions between humans and objects.
* Calculation of object usage probability (OUP) and training OUP using a multi-class SVM to improve the performance of recognizing the human-object interactions.
* Fusing the result of DCNN and multi-class SVM (decision fusion) for generating better and more accurate results.
## 4 Statistical Methodologies and Analysis Results
### Analysis Strategy for Assessing the Model  
This research used SEM to carry out the construct analysis. SEM is a powerful second-generation multivariate analysis technique that includes a measurement model and structural model. The measurement model is used to discover the latent variables based on the current variables. The structural model is used to examine the strength and direction of the relationships of the constructs [49]. SEM has been widely applied by IS researchers [50; 51]. Additionally, this research also used AMOS software to examine the quantity value and factor structures.  
Table 6 shows that the variables extracted by the constructs are greater than any squared correlation, which indicates that the constructs are empiricists and exist individually. This research is thus satisfied with the research models, including the convergent and discriminant validity. Consequently, all the measures used in this research show that the research model provides a good fit to the data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Perceived** & **Perceived** & **Perceived** & **Satisfaction** & **Intention** \\  & **Playfulness** & **Ease of Use** & **Usefulness** & **Satisfaction** & **Intention** \\ \hline Perceived Playfulness & 1 & 0.514 (**) & 0.399 (**) & 0.485 (**) & 0.334 (*) \\ Perceived Ease of Use & 0.514 (**) & 1 & 0.185 & 0.533 (**) & 0.234 \\ Perceived Usefulness & 0.399 (**) & 0.185 & 1 & 0.397 (**) & 0.269 \\ Satisfaction & 0.485 (**) & 0.533 (**) & 0.397 (**) & 1 & 0.310 (*) \\ Intention & 0.334 (*) & 0.234 & 0.269 & 0.310 (*) & 1 \\ \hline \end{tabular} \({}^{*}p<0.05,**p<0.01\).  
\end{table}
Table 5: The correlation analysis.  
Figure 9: Results of the correlation analysis.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 6 Experiment
### Baselines  
The following is a list of comparative experiments with this paper to verify the validity of our proposed model. For those methods based on homogeneous graphs, we processed the data and ignored the heterogeneity of nodes and edges. We use OpenNMS framework and DGL framework, which contain many graph embedding methods, to run most of the contrast models. In addition, a variant MSGCN-mean of this model is also compared. The brief introduction of these contrast models are as follows:  
* DeepWalk [3]: A random-walk-based embedding method designed for the same composition, which performs truncated random-walk over the network and is represented by skip-Gram model learning nodes.
* Graph Factorization (GF) [37]: A method of obtaining embedding by decomposing the adjacency matrix of a graph.
* SDNE [23]: Uses an automatic encoder to embed graph nodes and capture highly nonlinear dependencies.
* HOPE [22]: A method that uses generalized singular value decomposition (SVD) to get embeddings efficiently.
* GCN [7]: A classic semi-supervised graph neural network designed for the same composition, aggregating neighborhood information.
* GAT [8]: A neural network approach that applies the mechanism of attention to the same composition.
* TAGCN [45]: TAGCN is one of the variants of GCN. It uses a K graph convolution kernel to extract local features of different sizes.
* HAN [12]: A semi-supervised graph neural network embedding method for heterogeneous graphs that uses node-level attention and semantic attention to aggregate neighborhood information.
* MuSDAC [43]: A method on heterogeneous information networks.
* GraphInception [46]: A deep GCN for collective classification on heterogeneous information networks.
* MSGCN-mean: A variant of MSGCN that simply averages the representation of each node without considering the effect of edges on each node.  
In this paper, as we conduct experiments on the multi-class classification task, we choose accuracy as the evaluation metric to measure the performance of each model.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Proposed Scheme 1: ACE
#### 3.5.3 Data Transmission Phase  
In this phase, data is transmitted from source to sink through the path which is established in path establishment phase. Source node broadcasts its data to master and cooperative nodes. Data on its way from source to destination suffers fading due to multi-path propagation and noise in the water. These factors introduce high BER in the signal. In ACE, data received at the master node in direct transmission is compared with the original data sent by the source node and BER is calculated. If BER is less than or equal to maximum allowable BER, \(E\), then data packet is accepted. Upon acceptance, the master node sends acknowledgement (ACK) to retransmission nodes as shown in Figure 6a. Soon after, retransmission nodes discard the data.  
Figure 6: Data transmission from source to master node: (**a**) packet is accepted by master node and acknowledgement (ACK) is sent to retransmission nodes indicating that no retransmission is required; (**b**) packet is rejected by master node and asking for first retransmission from R1; (**c**) packet is again rejected and asking for second retransmission from R2; and (**d**) \(m\)th relay node performing data retransmission.  
If BER is greater than \(E\), Negative ACK (NACK), is sent by master node to retransmission nodes. Figure 6b shows that, for the first retransmission, NACK1 is selectively sent to first retransmission node, R1. In response to NACK1, data is amplified and forwarded to master node by R1. This amplified data may also suffer fading and noise. Therefore, to achieve acceptable BER at master node, direct signal from source to master node and relayed signal from source to relay to master node are combined using MRC. At the master node, BER is calculated and compared with the predefined threshold, \(E\). If it is less than or equal to \(E\), data packet is accepted and ACK signal is sent to retransmission nodes. Retransmission nodes discard the data on reception of ACK signal. However, if BER is greater than \(E\), NACK2 is sent to another retransmission node which amplifies and forwards the data to master node as shown in Figure 6c. Selection of different retransmission nodes leads to balanced energy consumption. A given node quickly depletes its energy if it is engaged in multiple retransmissions. At the master node, the direct, and two relayed signals are combined using MRC. If the calculated BER is less than or equal to \(E\), data packet is accepted else dropped. Increase of ACK from the master node, the retransmission nodes discard their data. It is assumed that signaling information; ACK/NACK is not lost during transmission.
# A New Method for Detecting Weld Stability Based on Color Digital Holography
## 3 Experiments and Results  
### Digital Holograms  
The aluminum alloy plates were placed in the OBJECT position in the color digital holographic optical path. The load was applied in the y-direction of three samples. The object light and the reference light interfere with the CCD, forming a hologram carrying the deformation information of the aluminum alloy plates. Figure 4a,b shows the holograms of three RGB channels of lap-jointed and butt-jointed aluminum alloy plates, where the load of R1, B1, and G1 is 293.67 N (Initial load), and the load of R2, B2, and G2 is 297.59 N. Figure 4c shows that of the defective butt-jointed plate, where the load of R1, B1, and G1 is 293.87 N (Initial load), and the load of R2, B2, and G2 is 294.26 N. By gradually increasing the load according to the experimental program, 18 holograms of any aluminum alloy plate can be obtained, with a total of 45 holograms for three samples.  
The hologram is reconstructed by using the FIMG4 FFT algorithm, and the phase difference of aluminum alloy plates under load changes is obtained. The phase difference after noise masking obtained from the reconstruction of the hologram in Figure 4 is shown in Figure 5. It can be observed that the phase differences are all disturbed by speckle noise and the boundaries of those phase differences are blurred and discontinuous. This makes phase unwrapping of those phase differences very challenging. To obtain accurate  
Figure 3: The network structure of 4f-CycleGAN.  
information about the 3D deformation field, the speckle noise needs to be reduced using a denoising algorithm.
# Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods
## 4 Fire-Induced Changes in Soil and Choice of Soil Remediation Technologies
### Biological Remediation Methods
#### Phytoremediation
In chemically enhanced phytoextraction, a chelate (usually EDTA) is applied to the contaminated soil to solubilize the contaminant which then forms a metal-chelate complex facilitating its uptake by the plant, mostly through a passive apoplastic pathway [151]. The added chelate not only increases the concentration of metal in soil solution, but it also changes the primary route of plant metal-uptake from the symplastic to the apoplastic pathway improving the amount of contaminant assimilated by the plant. Phytoremediation requires that the plant involved (phytovolatilizers, phytostabilizers, phytoextractors) be able to grow in the contaminated environment. Fire-affected contaminated soils may not have the appropriate conditions for plant growth as a result of fire-induced changes in their properties [60]. Lack of organic matter, low nutrient content, and alkaline pH that may predominate such soils could hinder the establishment of plants to be used for phytoremediation. Phytoremediation as a method of cleaning fire-affected soil may therefore pose challenges related to the establishment of the plants, except the soils are amended to meet the necessary conditions for plant growth. The concentrations of plants nutrient like Ca, K, and Mg in fire-affected soils may be high because they would have been liberated from organic matter during the fire. Considering their high temperature thresholds, they are not likely to be affected by fire, and would be available to plants in fire-affected soil. Addition of organic amendments to these soils would not only improve the soil organic matter content but may also influence the soil-plant-water relationship, soil tilth and the concentrations of other major plant nutrients. These changes could enhance plant growth conditions, improving the efficiency of phytoremediation.