esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Paper-ash is used for remediation of heavily contaminated soils with metals, but remediation efficiency after longer periods has not been reported. To gain insights into the mechanisms of immobilization of cadmium (Cd), lead (Pb), and znic (Zn), a study was performed in the laboratory experiment in uncontaminated, artificially contaminated, and remediated soils, and these soils treated with sulfate, to mimic conditions in contaminated soil from zinc smelter site. Remediation was performed by mixing contaminated soil with paper-ash to immobilize Cd, Pb, and Zn in the geotechnical composite. Partitioning of Cd, Pb, and Zn was studied over one year in seven-time intervals applying the sequential extraction procedure and complementary X-ray diffraction analyses. This methodological approach enabled us to follow the redistribution of Cd, Pb, and Zn over time, thus, to studying immobilization mechanisms and assessing the remediation efficiency and stability of newly formed mineral phases. Cd, Pb, and Zn were effectively immobilized by precipitation of insoluble hydroxides after the addition of paper-ash and by the carbonization process in insoluble carbonate minerals. After remediation, Cd, Pb, and Zn concentrations in the water-soluble fraction were well below the limiting values for inertness: Cd by 100 times, Pb by 125 times, and Zn by 10 times. Sulfate treatment did not influence the remediation efficiency. Experimental data confirmed the high remediation efficiency and stability of insoluble Cd, Pb, and Zn mineral phases in geotechnical composites.  
cadmium; lead; zinc; contaminated soil; paper ash; immobilization mechanisms +
Footnote †: journal: Applied Sciences
## 3 Data Collection  
A total of six uncontrolled intersections in two Indian cities (Nagpur, Visakhapatnam) were studied; namely: Nandanvan square, Coffee house square, Bhim square, Ambedkar square, M.V.P. Colony square and Kailash Nagar square. The video cameras were mounted at an elevated position with a clear and suitable view.  
To analyze the traffic data, this study considered paired interactions involving two vehicles, and vehicle tracking was conducted by extracting the data, frame by frame, from the recorded videos. The videos were recorded at a frame rate of 30 fps, and the length and width of a reference grid from the study area were collected for calibration. Table 1 provides a summary of the observed intersections.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name of Site and Location ID** & **Image** & **City** & **Traffic Volume (Vehicles in Two Hour)** \\ \hline Nandanvan Square & & Nagpur & 5358 \\ \hline Coffee House Square & & Nagpur & 4567 \\ \hline Bhim Square & & Nagpur & 6129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of study sites.  
The following criteria were used to select the study sites:  
1. Uncontrolled four arm intersection;
2. No horizontal curvature or vertical gradient;
3. Substantial volume of crossing vehicle traffic from all directions at the intersection;
4. Presence of an elevated point to obtain a clear view of the intersection and approaching arms for videographic data;
5. No obstructions or electric wires in the vicinity of the intersection that could hinder the trajectory extraction process following data collection.  
After collecting the data, a frame-by-frame extraction technique was used to extract the data, with a focus on identifying vehicle-vehicle interactions. The trajectories of the vehicles involved in these interactions were extracted using a semi-automated tool (Kinovea 8.27). Figure 1 provides an overview of the study methodology.  
The methodology used to assess the correlation between the surrogate safety indicators and human judgment of severity drew inspiration from a recent publication [11]. The objective of using this methodology was not to replicate the results of the prior study, but rather to ascertain whether applying a similar strategy--but using a different logistic model, involving three categorical variables--would result in different results under heterogenous traffic conditions.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name of Site and Location ID** & **Image** & **City** & **Traffic Volume (Vehicles in Two Hour)** \\ \hline Ambedkar Square & & Nagpur & 4782 \\ \hline MVP Square & & Visakhapatnam & 4843 \\ \hline Kailash Nagar Square & & Visakhapatnam & 5631 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 4 Evaluation Method
### Direct Methods
#### 4.1.3 Foam Power (FP)  
The foam power test proposed by [PERSON] and [PERSON] [22] is a direct measure of the foaming potential and it was developed in accordance with the concepts used in food science [74; 75; 76]. This test is among those most used in the foaming phenomenon in WWTPs and in the scientific literature it has almost always been applied following the measurement procedures reported by [PERSON] and [PERSON] [22]. In their experiments, a 100 mL sample of activated sludge was placed vertically in a transparent acryl cylinder (30 mm diameter \(\times\) 1000 mm height). The tests were conducted with an airflow of 5 L min\({}^{-1}\) supplied from the bottom of the cylinder during 20 s or 30 s. Albumin solution and EPS extracted from the activated sludge were used as the samples for foaming examinations and the activated sludge was from small scale domestic WWTPs with an MBR process.  
The other researchers, in the experiments carried out in the following years, have applied the same procedure by varying only some operating conditions such as the volume of the sample or the airflow. The foaming power index (FP) is determined as the volume of sample consumed by the foams per liter supplied with air through Equation (1) [22]:  
\[FP\left[mL\ L_{air}^{-1}\right]=\frac{(H_{0}-H_{1})\ast\ S}{Q\ast\Delta T} \tag{1}\]  
where:  
\(H_{0}\): the level of the interface between air and the matrix before aeration (cm);  
\(H_{1}\): the level of the interface between the matrix and the foams after an aeration interval \(\Delta T\) (cm);  
\(S\): the cylinder area (cm\({}^{2}\));  
\(Q\): the aeration flow rate (L\({}_{\text{air}}\) min\({}^{-1}\));  
\(\Delta T\): the aeration period (min).  
For the determination of foaming scum index (FSI; which is described below), [PERSON] and [PERSON] [62] proposed an alternative approach to the quantification of foaming potential. Instead of the level of the interface between the matrix and the foams after aeration, the height reached by the foams in the column following aeration is measured. In this case the foam power, called FP2, can be quantified using Equation (2):  
\[FP2\left[mL\ L_{air}^{-1}\right]=\frac{(H_{2}-H_{0})\ast\ S}{Q\ast\Delta T} \tag{2}\]  
where:  
\(H_{2}\): the level of the interface reached by the foams at the end of the aeration (cm);  
\(H_{0}\): the level of the interface between air and the matrix before aeration (cm);  
\(S\): the cylinder area (cm\({}^{2}\));  
\(Q\): the aeration flow rate (\(L_{\text{air}}\) min\({}^{-1}\));  
\(\Delta T\): the aeration period (min).  
In the study of [PERSON] and [PERSON] [62], a sample of mixed liquor (150 mL) was aerated for a period of 1 min at a controlled flow rate of 0.5 L min\({}^{-1}\) during which time the foaming potential was determined as a percentage of the maximum expansion of foams achieved in relation to the starting height.
## 5 Results
### Topology 1: Results
#### 5.1.2 Scenario 2  
In Scenario 2, the average response times achieved with the routing methods are depicted in Figure 5a. The average response time with snnr was 9-20% better than snndp at lower bundle rates, but 13-27% worse at bundle rates of 9-10 bundles/s. snnr and snndp showed 2-44% and 2-27% worse performance than snnd. The static and snnp methods performed better at lower bundle rates, but snndp and snnr performed 50% better than static and 25% better than snnp at 10 bundles/s.  
The bundle loss percentage in shown in Figure 5b. The snnr method showed 25-43% and 13-48% better packet loss performance than snndp and snnd at bundle rates of 8-10 bundles/s. snnr and snndp showed 20-60% and 15-60% better performance than static. snnp showed 270-650% superior performance over snnr and snndp.  
Figure 5c shows that the throughput with snnr was up to 24% better than snndp and snnd at bundle rates of 8-10 bundles/s. snnr showed up to 63% better throughput than static, and snndp showed up to 50% better throughput than static. snnp showed up to 38% better throughput than snnr and snndp.  
Figure 6 depicts the link selection at the node h26 by the SNN algorithms according to their optimization goals in Scenario 2, and snnp selected h30 the most to avoid packet loss.  
Figure 4: Link selection at Node h26: Scenario 1.  
Figure 5: Routing performance: Scenario 2. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.
## 4 Methodology for the Performance Analysis of the Physical Layer Proposals
### Summary of Performed Simulations for G3-PLC and PRIME Technologies  
PLC performance is measured with a complete implementation of physical layer modems in the Virtual PLC Lab, whose technology is certified by G3-PLC and PRIME Alliances. As such, the implementation includes realistic synchronization, frequency offset correction, and automatic gain control mechanisms. The comparison of the proposed improvements is carried out for all CENELEC-A modes available for G3-PLC and PRIME technologies and evaluated under the same circumstances. Tests are performed with 22 physical layer options configured in the Virtual PLC Lab:  
* Eight options of the physical layer for G3-PLC: Four non-coherent and four coherent modes.
* Fourteen options of the physical layer for PRIME: Six modulation schemes for type A frames, which are common to PRIME 1.3.6 and 1.4 versions, and eight for type B, which is available only in PRIME 1.4 and introduces increased physical robustness.  
Each of these configurations is evaluated under the 38 disturbances selected in [10], considered as a set of representative channel disturbances to be found in the LV distribution grid. For the above-mentioned configurations and for each channel disturbance, FER is evaluated per each SNR and the corresponding link budget is calculated.  
For PRIME, the process is repeated twice: for the equalization improvement and Reed-Solomon improvement. Table 2 represents the different parameters used in [10] and the two additional simulation steps of this study.  
For G3-PLC, the process is executed three times: for tone map calculation, standard tone map testing and tone map with power reallocation evaluation. The tone map is calculated following the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Equalization Curve** & **Forward Error Correction** \\ \hline Original [10] & Polynomial & Convolutional Coding \\ Equalizer improvement & Spline & Convolutional Coding \\ Reed–Solomon improvement & Spline & Convolutional Coding with Reed–Solomon \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters and steps for PRIME.  
algorithm described in Section 3.3.1. The communication parameters are configured according to the communication limit defined by ETSI [15] (attenuation corresponding to FER 5% with tone map '111111'). The algorithm will then obtain the tone map value that improves the FER under these conditions. The different parameters used in [10] and the additional simulation steps of this study are summarized in Table 3.  
In summary, this study involves 52 physical configuration options in channel conditions impaired by 38 disturbances. In total, the Virtual PLC Lab has generated 1782 FER-SNR curves whose calculations involved the exchange of 848 million frames.
## 3 Energy-Efficient Architectures
### Spatial Architectures: Fpgas and Asics
#### Accelerators with Sparsity Exploitation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accelerator** & **Ref.** & **Contribution** & **Target** & **Year** \\ \hline Cnvlutin & [68] & CSR for activations & ASIC & 2016 \\ \hline Cambricon-x & [69] & CIS for the weights & ASIC & 2016 \\ \hline SCNN & [24] & CSC for weights and activations & ASIC & 2017 \\ \hline Sparten & [70] & Improvement of SCNN & ASIC & 2019 \\ \hline EIE & [71] & CSC for the weights, zero-skip for activations & ASIC & 2016 \\ \hline NullHop & [26] & CIS for the weights, zero-skip for activations & FPGA & 2018 \\ \hline ZeNA & [72] & Zero-skip of weights and activations & ASIC & 2017 \\ \hline SqueezeFlow & [25] & \
\begin{tabular}{c} RLC for the weights, concise convolution rules \\ \end{tabular} & ASIC & 2019 \\ \hline Eyeriss v2 & [73] &
\begin{tabular}{c} CSC for weights and activations, \\ data are kept compressed \\ during computation \\ \end{tabular} & ASIC & 2019 \\ \hline UCNN & [74] & Generalizes sparsity to non-null weights & ASIC & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accelerators that exploit sparsity. Figure 9: Comparison among different compression techniques: (**a**) Compressed Sparse Row (CSR), (**b**) Compressed Sparse Column (CSC), (**c**) Compressed Image Size (CIS), and (**d**) Run Length Coding (RLC).
# Shallow Water Waves and Conservation Laws with Dispersion Triplet
## 1 Introduction
### Governing Model  
The generalized form of the KdV equation and its type are all incorporated in the following structure:  
\[q_{t}+F(q)q_{x}+b_{1}q_{xxx}+b_{2}q_{xxt}+b_{3}q_{xtt}=0. \tag{1}\]  
Here, \(x\) and \(t\) are the independent variables that respectively depict spatial and temporal variables. Then, the dependent variable \(q(x,t)\) represents the wave profile. The first term is the temporal evolution of the wave. The second term is from the effect of nonlinearity, which contains a single effect for the KdV equation and dual nonlinear terms that give GE. The three dispersion terms carry the coefficients \(b_{j}\) for \(j=1,2,3\), from triple spatial dispersion, spatio-temporal dispersion, and dual-temporal-spatial dispersion effects, respectively. The KdV equation and GE has \(b_{2}=b_{3}=0\), which has exhausted the journals [1; 3; 5; 7; 16].  
In order to study the model expressed by Equation (1) with traveling wave hypothesis, one would choose the following hypothesis:  
\[q(x,t)=g(x-vt). \tag{2}\]  
Here, the function \(g\) denotes the solitary wave profile, with \(v\) being the velocity of the wave. This assumption would transform (1) into the following ordinary differential equation (ODE):  
\[vg^{\prime}-F(g)g^{\prime}-\left(b_{1}-b_{2}v+b_{3}v^{2}\right)g^{\prime \prime}=0, \tag{3}\]  
which integrates to  
\[vg-\int F(g)dg-\left(b_{1}-b_{2}v+b_{3}v^{2}\right)g^{\prime\prime}=0, \tag{4}\]  
upon choosing the integration constant to be zero, as relevant for solitary waves. Next, multiplying (3) by \(g^{\prime}\) and integrating leads to  
\[vg^{2}-2\int\int^{g}\ F(u)dudg-\left(b_{1}-b_{2}v+b_{3}v^{2}\right)\left(g^{ \prime}\right)^{2}=0. \tag{5}\]  
The final integration that would lead to the solitary wave solution would be possible once the exact form of nonlinearity is considered. This study would therefore be classified into the subsequent three subsections depending on the type of nonlinearity.  
The study will be split into two sections that detail the KdV equation and GE in sequence. The first section contains three subsections based on the structure of the nonlinear term while the second section has two subsections based on the structure of the two nonlinear terms. The details on traveling waves and the conservation laws are also extracted.
## 4 Experiments and Results
### Experiment Details  
The model was trained and tested on a high-performance workstation with Windows 10 operating system, which was configured with a 2.10 GHz Intel Xeon Silver 4110 CPU (16 GB memory) and NVIDIA GeForce RTX 2080 Ti GPU. The software environment is as follows: Windows 10 operating system based on 64-bit, Pytorch deep learning framework, CUDA11.0, OpenCV2 library and VS Code integrated development environment.  
During network training, the learning rate was set to 0.01, the epoch was set to 60, and the batch size was set to 16. The Swish function was selected as the activation function, the cross entropy function was selected as the loss function, and Adaptive Moment Estimation(Adam) was selected as the optimizer.  
Before the model training, the parameters of the pre-trained model were loaded to the classification model by the transfer learning method. The images in the training set were then uniformly scaled to the size of 600 \(\times\) 600 \(\times\) 3 and randomly packaged into the model to start the training. In the process of training, we randomly selected 4169 images for training, and each image would be used several times. The training set and verification set were evaluated once per iteration, and the process of training accuracy, verification accuracy, and cross-entropy loss changes in each generation were saved as a log file, and then uploaded to Tensorboard for review.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques
### Transformer [45]  
Transformer addresses the problem of gradient explosion/vanishing in different types of RNN. It accomplishes this by using an encoder-decoder architecture with an attention mechanism instead of RNN to encode each position. It differs from the encoder-decoder architecture in terms of the following elements: positional encoding and Transformer blocks, which consist of multi-head attention, a position-wise feed-forward network, add, and normalize. Multi-head attention allows the model to focus on different positions,  
Figure 7: Simplified CNN with two Conv layers.  
avoiding the dominant tokens and giving multiple subspaces of representation. Each head has a separate query, key, and value, and runs in parallel, with no sequential relation perceived. Previously, encoder-decoder architectures were capable of directing attention to focus on the most important parts by adding an attention layer, with key and value pairs from the encoder and a query from the decoder, and the output was passed to the decoder's recurrent layer. However, this is now included three times in both encoder and decoder as an essential part, as there is one multi-head attention on the encoder side and two on the decoder side, and each concatenates the multiple attentions beneath it. Add and norm makes the training easier by combining the input and output of the multi-head attention layer or position-wise feed-forward network layer in a residual structure to perform batch normalization, which is conducted horizontally. The position-wise feed-forward network is a convolution layer used to reduce dimensionality and decrease the number of feature maps. The goal of this layer is to inject batches to reduce capacity. Positional encoding (which should not to be mixed with the position-wise feed-forward network) is used to add positional information through sin and cosine functions with even and odd positions of the embeddings of the input. This is because the sequentiality is not retained in the multi-head attention layer or the position-wise feed-forward network, since they work independently and in parallel.
# A Survey on Deep Learning in 3D CAD Reconstruction
## 6 CAD Representation  
### B-Rep (Boundary Representation)  
In computer-aided design (CAD), parametric curves and surfaces constitute the core of modeling foundations [96]. B-rep (boundary representation) is a common CAD data structure used to encode geometry as parametric shapes and their topological relationships to achieve complex and accurate 3D modeling [97], as shown in Figure 4. It consists of faces, edges, and vertices, and supports surface expressions such as lines, arcs, cylinders, and non-uniform rational B-spline curves (NURBS). It can accurately describe complex models generated by CAD modeling operations [98]. CAD users can directly manipulate the geometric elements of B-rep for design and modification.  
B-rep (boundary representation) plays a bridging role between geometric modeling and solid modeling in CAD [99]. Geometric modeling focuses on using mathematical methods to describe the geometric features of objects, such as curves, surfaces, and their spatial distribution [100], while solid modeling further emphasizes the construction of three-dimensional models with closed volumes and topological integrity to meet the actual needs of manufacturing and engineering [101]. B-rep can accurately describe complex surfaces while ensuring the topological coherence and closure of the model. As a means of expression for geometric modeling and the basis for the implementation of solid modeling, it plays a key role in connecting the two in the CAD system.  
Although B-rep data is widely used in the CAD field, its direct use in deep neural networks still faces many challenges [28]. B-rep consists of a variety of geometric and topological entities. The mapping between shapes and surface types is not one-to-one; its topological structure is complex, and the connection methods of each geometric unit need  
Figure 4: Three-dimensional CAD model. (**a**) B-rep (boundary representation); (**b**) polygon mesh representation; (**c**) sequence representation; (**d**) CSG (Constructive Solid Geometry); (**e**) sketch.  
to be considered. In modern machine learning, algorithms such as BrepGen, BrepNet, and BRep-BERT are all based on B-rep [17; 84; 102]. However, due to the complex data structure, it remains a significant challenge. In order to apply the latest advances in deep neural networks to CAD software, [PERSON] et al. [16] proposed a unified representation of B-rep data which enables the network architecture UV-Net to effectively combine image and graph convolutional neural networks. However, B-rep still has the limitation of not being able to utilize other available information, such as edge convexity and curve type.