esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Rapid changes in pH to acidic or alkaline level that are still in the optimal boundary level of wastewater have different effects on ASP:  
1. Rapid changes of pH from optimal to acidic had a greater impact on the ASP than alkaline pH.
2. Acidic pH affected ammonia conversion to nitrites indicating inhibition of both AOB and NOB; however, this phenomenon may also be attributed to the limitation of inorganic carbon source (due to pH change itself).
3. Organic carbon biodegradation also decreased, and increased sludge foaming and sludge washout were noted in acidic pH reactor.
4. The slight changes in sludge microfauna population were observed in both reactors. In the acidic reactor, there was a noticeable increase in _symmanobea_, _Zoogloea_ spp., and _crawling ciliate Chilodonella_ sp. In the alkaline pH reactor, the microRNA population decreased and filamentous sludge bulking occurred due to the increase in filamentous flocs.  
5. SVI\({}_{30}\) index is not the main indicator of the formation of high FA concentration in this case. Although, SVI\({}_{5}\) changed more than SVI\({}_{30}\) and can be specified as the first sign of the inhibition.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12115754/s1](https://www.mdpi.com/article/10.3390/app12115754/s1)]([https://www.mdpi.com/article/10.3390/app12115754/s1](https://www.mdpi.com/article/10.3390/app12115754/s1)), Figure S1: Changes in pH in acidic and alkaline pH reactors (n = 3); Figure S2: Changes in COD concentration in acidic and alkaline pH reactors (n = 3); Table S1: T-test one tailed results for acidic and alkaline reactor comparison; Table S2: two-sample unequal variance for acidic and alkaline reactor comparison; Figure S3: Sludge foaming in acidic pH lab-scale reactor; Figure S4: Changes in SVI5 in acidic and alkaline pH reactors (n = 3); Figure S5: Changes in SVI30 in acidic and alkaline pH reactors (n = 3); Figure S6: The schematic diagram of experimental set-up [14].  
Conceptualization, K.K. and R.N.; methodology, K.K.; validation, [PERSON] ([PERSON]), K.G. ([PERSON]), and R.N.; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]) and K.K.; resources, R.N.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, L.M.; visualization, K.G. ([PERSON]); supervision, T.J.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ERDF Project \"Zero-to-low-waste technology for simultaneous production of liquid biofuel and biogas from biomass\", No. 1.1.1.1/18/A075.  
Not applicable.  
We thank Riga Water Ltd. for providing test materials and supported microbial testing. We thank [PERSON] from RESETILOVS Ltd. for additional technological consultations.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Proposed Method
### Local Geometric Co-Occurrence Model Learning  
The following steps are required to perform the local geometric co-occurrence model learning:  
1. Consider the training set LR and HR face residue.
2. Get the local geometric feature representation for each patch.
3. Connect the HR and the corresponding LR geometric feature vectors for jointly learning the HR and LR visual vocabularies.
4. Apply the affinity propagation clustering algorithm.
5. All data points are considered simultaneously as candidate cluster centers.
6. Messages are transmitted between the data points until an appropriate set of cluster centers and corresponding clusters emerge.
7. The number of clusters also depends on a prior specification of how preferable each data point is as a cluster center.
8. Consider all data points that have equal potential to be the cluster centers, so the preferences of all data points are set to the same value that can be controlled to produce different numbers of clusters.  
In the testing phase, there were three steps to be followed. In the first step, we chose the LR image. Then, the initialization and the optimization process were performed. The LR image was given as input and it was initialized by interpolation. That image was compared to the global face image and processed to the M-step. Hence, the initialized image was compared to the local geometric co-occurrence model image, where that image was a process in the E-Step. Finally, the M-step and the E-step image was optimized. The geometrical feature that was defined in this section represented the high-frequency structures that are due to various intensity changes.
## 7 The Influence of the Constraint Range on the Optimization Results
### Influence of a Changing Range of Horizontal Displacements of the Pylon on the Optimization Results  
The impact of varying the constraint on the pylon's horizontal displacement from \(-100\) mm \(\leq\delta_{\text{pi}}\leq 0\) mm to \(-20\) mm \(\leq\delta_{\text{pi}}\leq 0\) mm while holding the other constraints constant was investigated to assess its effect on the optimization results. The variation in the structural states resulting from changes in the vertical range of constraints on the  
Figure 17: Variation in values of selected structural responses with decreasing constraint range of main girder’s vertical displacement. (**a**) Structural bending energy; (**b**) structural displacement; (**c**) structural bending moment; (**d**) structural normal stress; (**e**) cable force; (**f**) vertical support reaction.  
pylon's horizontal displacement is presented in Figure 18. Variations in the horizontal pylon displacement constraint range significantly affect the maximum sag of the main girder, the maximum positive and negative bending moments in the main girder, the maximum tensile stress in the top and bottom chords of the main girder, the horizontal displacement of the top of the pylon, the maximum negative bending moment in the pylon, the maximum cable force and cable force distribution parameters, and the vertical reactions of the transition and auxiliary piers. The influence on the other structural states is less than 5%. As the range of the constraints on the pylon's horizontal displacement narrows, the maximum positive bending moment in the main girder, the maximum tensile stresses in the top and bottom chords of the main girder, and the vertical reaction of the transition pier show an increasing trend. In contrast, the maximum downward deflection of the main girder, the horizontal displacement of the top of the pylon, the maximum cable force, and the cable force distribution parameter decrease. The maximum negative bending moment in the main girder, the maximum negative bending moment in the pylon, and the vertical reaction of the auxiliary pier display non-monotonic behavior, decreasing initially before increasing, with a narrower constraint range. A comparative analysis indicates that a constraint on the pylon's horizontal displacement of \(-30\) mm \(\leq\delta_{\text{pi}}\leq 0\) mm results in smoother structural alignment, lower maximum positive and negative bending moments in the main girder and the pylon, and a reduced maximum cable force distribution parameter. Therefore, this constraint range is considered appropriate.  
Figure 18: Variation in values of selected structural responses with decreasing constraint range for horizontal pylon displacement. (**a**) Structural bending energy; (**b**) structural displacement; (**c**) structural bending moment; (**d**) structural normal stress; (**e**) cable force; (**f**) vertical support reaction.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 6 Feature Selection
### Filter Approach  
* Principal component analysis. PCA uses orthogonal transformations to set correlated variables into a set of linearly uncorrelated variables called principal components. This dimensional reduction [41] projects high-dimensional data along a direction for which the variance of the data is maximized in a linear subspace spanned by a small number of latent components.
* Discriminant filter bank. It overlaps frequency bands and uses a Fisher ratio to analyze each sub-brand's band power to discriminant sub-bands. This analysis is performed on a single channel of EEG data [14].
* Sparse filter bank. It uses multiple frequency bands to optimize sparse patterns. To achieve this, the feature selection is completed using a supervised machine learning technique, such as Bayesians ones or super vector machines (SVMs) [14].
* MRMR (minimum redundancy maximum relevance). It is an algorithm that reduces redundant data within the database and maximizes the relevant information to classify [50].
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Calculating Load  
1. Self-weight The self-weight coefficient of the calculation model is g = -9.81 m/s2.
2. Superstructure load  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Bulk Density \(\gamma\) (kN/m\({}^{3}\))** & **Elastic Modulus \(E\) (MPa)** & **Poisson Ratio (\(v\))** & **Constitutive Relation** \\ \hline Existing bridge & 24.5 & \(3.45\times 10^{4}\) & 0.20 & Elastic \\ Underpinning pile cap & 24.5 & \(3.0\times 10^{4}\) & 0.20 & Elastic \\ Shield segments & 78.0 & \(3.45\times 10^{5}\) & 0.30 & Elastic \\ Shield shells & 78.0 & \(3.45\times 10^{5}\) & 0.30 & Elastic \\ Shield grouting layers & 24.0 & 30.0 & 0.20 & Elastic \\ Steel Strand & 78.0 & \(1.95\times 10^{5}\) & 0.30 & Elastic \\ \hline \hline \end{tabular} (Note: The mechanical parameters of rock and soil mass are defined with reference to Table 1).  
\end{table}
Table 2: Physical and mechanical properties of materials.  
The superstructure load is the main factor that causes the settlement of the structure in construction period. In this project, the superstructure load of the bridge is converted into a surface load and applied to the top of the box girder. \(f_{\text{s}}\) = 60.8 kN/m.  
Steel strands prestress  
The prestressed steel strands that places in the bottom of the underpinning bearing platform are 1860 PC steel wires. After the underpinning bearing platform concrete strength reaches the designed status, the steel wires are tensioned. The steel wires are tensioned by post-tensioning method and the design tension control value was \(f_{d}\)= 1334.0 MPa.  
During the piles foundation underpinning construction, the pre-jacking force control is the key point of the foundation replacement process. The pre-jacking force is loaded as a typical \"step-by-step\" way and the calculated pre-jacking force \(f_{p}\) = 1102 kN.  
The shield tunneling loads could be the primary factor that caused the bridge foundation structure deformed. Thus, it is essential to simulate the shield tunnel construction correctly. As shown in Figure 6, the simulation model took three kind of loads into consideration such as the tunnel face pressure, tunnel grouting pressure and the tunnel segments reaction force. Shield machine that uses in this project is earth pressure balance (EPB) shield, the outer diameter of the shield machine is 8.30 m, the inner diameter is 7.5 m, the width of the single segment is 1.6 m.
# A Finite Element Analysis of Tunnel Lining Demolition by Blasting for Subway Tunnel Expansion
## 4 Blasting Mechanical Characteristics
### Results Analysis
#### 4.2.3 Stress Analysis of Surrounding Rock  
The blasting stress evolution laws are analyzed with time after the dynamite blast, and the time history curves of the absolute value of the maximum principal stress of the surrounding rock at different times in the blasting process are shown in Figure 9.  
From the initiation of the explosive to the time of 0.1 \(\times\) 10\({}^{-3}\) s, the maximum principal stress S\({}_{1}\) and the minimum principal stress S\({}_{3}\) gradually increase, which is caused by the explosion. The S\({}_{1}\) stress induced by blasting is 1030 MPa and reaches the maximum value at the time of 0.1 \(\times\) 10\({}^{-3}\) s, and the S\({}_{3}\) stress is 886 MPa. The surrounding rock is sandy mudstone with a compressive strength of 24.7 MPa, and it is damaged by the action of the blasting stress based on the maximum tension theory. The S\({}_{1}\) stress and the S\({}_{3}\) stress gradually decrease from the time of 0.1 \(\times\) 10\({}^{-3}\) s to 0.4 \(\times\) 10\({}^{-3}\) s. The S\({}_{1}\) stress tends to be stable, but the S\({}_{3}\) stress increases and the value is greater than that of the S\({}_{1}\) stress until the time of 0.6 \(\times\) 10\({}^{-3}\) s. Lastly, the S\({}_{3}\) stress tends to be stable at the time of 1.0 \(\times\) 10\({}^{-3}\) s. The above blasting stress evolution law shows that the explosion pressure is released rapidly after the dynamite blast, and the S\({}_{1}\) stress decreases to a stable level after reaching the maximum value. However, the S\({}_{3}\) stress decreases after reaching the maximum value and then increases to a stable value by the superposition of the blasting stress.
# A Comparative Study for Control of Quadrotor UAVs
## 2 Materials and Methods
### System Control
#### 2.2.1 Inner Loop--PID Control  
This follows the linearized attitude dynamical system expressed by equations from (23) to (25) in state space form, with \(\mathbf{x}=(\phi,\theta,\psi,\Phi^{\prime},\ \theta^{\prime},\ \psi^{\prime})\) and \(\mathbf{u}=(\mathrm{U}_{2},\mathrm{U}_{3},\mathrm{U}_{4})\). Three independent Proportional-Integral-Derivative controllers are designed with the aim of stabilizing the linearized system in hovering.  
\[\begin{cases}\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{u}\\ \mathbf{y}=\mathrm{C}\mathbf{x}+\mathbf{D}\mathbf{u}\end{cases} \tag{26}\]  
\[\mathrm{A}=\begin{pmatrix}0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{pmatrix},\ \mathrm{B}=\begin{pmatrix}0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0&0\\ \mathrm{L}\mathrm{L}^{-1}_{\mathrm{x}}&0&0\\ 0&\mathrm{L}\mathrm{L}^{-1}_{\mathrm{y}}&0\\ 0&0&0&\mathrm{L}^{-1}_{\mathrm{z}}\end{pmatrix},\ \mathrm{C}=\begin{pmatrix}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{pmatrix},\ \mathrm{D}=\begin{pmatrix}0&0&0\\ 0&0&0\\ 0&0&0\\ 0&0&0&0\\ 0&0&0&0&0\end{pmatrix}\]  
A classical formulation of the PID control law applied to the quadrotor stabilization problem in the time domain is presented in Equation (27). The three control parameters (\(\mathrm{K}^{1}_{\mathrm{p}},\mathrm{K}^{1}_{\mathrm{r}},\mathrm{K}^{1}_{ \mathrm{d}}\)) of each control command are set in order to stabilize the model in (26) for the hovering equilibrium point. Refer to [25] for the theoretical foundations of PID control and further details.  
Figure 3: Overview of the proposed control system’s architecture.  
\[\mathrm{u}^{\mathrm{P}}(t)=\mathrm{u}_{\mathrm{eq}}^{\mathrm{P}}-\mathrm{K}_{ \mathrm{P}}^{\mathrm{P}}\Big{(}\mathrm{y}^{\mathrm{P}}(t)-\mathrm{y}_{\mathrm{eq} }^{\mathrm{P}}\Big{)}-\mathrm{K}_{\mathrm{i}}^{\mathrm{P}}\int_{0}^{\mathrm{ T}}\Big{(}\mathrm{y}^{\mathrm{P}}(t)-\mathrm{y}_{\mathrm{eq}}^{\mathrm{P}} \Big{)}\mathrm{dt}-\mathrm{K}_{\mathrm{d}}^{\mathrm{P}}\Big{(}\,\dot{\mathrm{y} }^{\mathrm{P}}(t)-\,\dot{\mathrm{y}}_{\mathrm{eq}}^{\mathrm{P}}\Big{)}\,\mathrm{u}_{\mathrm{eq}}=\ y_{ \mathrm{eq}}=(0,0,0)\,\ \mathrm{p}=1,2,3 \tag{27}\]
# Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
For example, a major problem of the proposed formula for PSO is that the speed is constantly increasing, while the particle should slow down as it gets closer to the global optimum so that it does not go past the global optimum. There are several solutions to this problem, but they are effective only for specific cases [37; 38]. The genetic algorithm has the inherent disadvantages of producing unpredictable and unripe results [39; 40]. Moreover, another major drawback of these algorithms is the need to set many parameters for each problem to achieve high performance [41]. Therefore, finding high-efficiency algorithms has been one of the most important tasks for optimization in engineering and AI applications [42; 43]. In the present paper, a new novel algorithm for global optimization, called the thermodynamic equilibrium algorithm (TEA), is proposed. TEA is inspired by the equilibrium process of thermodynamic systems that move toward equilibrium according to the second law of thermodynamics, while satisfying the first law of thermodynamics. During the process, the equilibrium state is checked by using the first and second law of thermodynamics. This allows the systems to converge to the global minimum of the cost function. This paper describes how the thermodynamic equilibrium procedure can be modeled with physically relevant systems and implemented for optimization purposes. Moreover, this work tackles the HIFU treatment planning by utilizing an optimization-based method based on TEA. The inputs to the optimization are the anticipated target volume and the organs that have to be ablated, and the TEA produces a workable treatment strategy. This scheme has the potential to improve targeting accuracy of HIFU and shorten treatment durations, ultimately improving clinical outcomes for HIFU patients.
# A Study on DNN-Based Practical Model for Predicting Spot Color
###### Abstract  
The color of product packaging plays an important role in brand awareness and consistency. Given the importance of consistent color reproduction, the use of standardized spot colors is essential. However, the reproduction of specific spot colors in offset packaging printing involves additional processes and costs. This study presents an efficient approach to predict the color result of spot color inks in the packaging printing industry, using only the amount of ink involved in the mixing process as an input. Using a neural network-based model, our approach uses the CIEDE2000 color difference formula as a loss function to accurately estimate the final color. This method provides a simplified alternative to traditional color mixing techniques, which often involve subjective judgment and can be resource-intensive. Particularly beneficial for smaller companies, our approach reduces the complexity and cost associated with achieving accurate spot colors. The significance of this work lies in its practical application, providing a simpler, more objective and cost-effective solution for consistent color reproduction in packaging printing.  
packaging printing; spot color; color mixing process; deep learning Article
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 4 Design of the Space-Based Time System for Navigation Constellations
### Overview of the Space-Based Time System Establishment  
The space-based time system needs to support the dynamic access of space and Earth time and frequency resources. It should have strong flexibility and expansibility. The newly accessed atomic clock can instantly work, and the whole system can remain stable when any clock is offline. Additionally, this system should support the access of not only the atomic clock resources inside the navigation constellation but also the timekeeping clock of the space and the ground outside the navigation constellation. This means that the system can realize space-ground integrated timekeeping.  
The process of the proposed space-based time system is shown in Figure 1. The construction of this space-based time system follows several steps.  
1. Preprocess the inter-satellite two-way ranging data to obtain the time comparison data of the entire network. Taking the ranging between two satellites as an example, the data preprocessing steps include ionospheric delay correction, relativity correction, outlier elimination, and epoch correction. The pseudo-range observation equation for inter-satellite two-way ranging is established, and the whole network time comparison data are accordingly calculated.
2. Acquire the stability matrix of the entire constellation from the time comparison data. The comprehensive frequency stability of two clocks in each group is obtained using the distributed computing method. Then, the frequency stability of the atomic clock of each star is acquired by the \"triangular hat\" method.
3. Calculate the atomic clock weight factor. The average atomic time scale reflects the noise suppression of the integrated clock, and an atomic clock with good stability has a great weight. Combined with on-orbit data, the atomic clock weight factor is calculated under the comprehensive analysis of the number of clocks, calculation cycle, weight variance interval, and maximum weight settings.
4. Output the space-based time. Comparing the advantages and disadvantages of the weighted average algorithm (including the ALGOS algorithm and at1 algorithm),Kalman filter algorithm, wavelet decomposition algorithm, etc., a timekeeping algorithm suitable for establishing space-based time based on the ALGOS algorithm is proposed.
## 3 Methods
### Multi-Scale Channel Cross-Fusion Module
#### 3.5.2 Channel-Wise Cross-Attention (CCA)  
To reduce the semantic imbalance between the outputs of the CCT module and the decoder-side features, we adopt the CCA module to enhance the fusion of these two types of features. As show in Figure 5, CCA is a type of channel-wise attention mechanism that strengthens channels with more information and suppresses those with less information by weighting them.  
For each layer of CCT, the output \(O_{i}\) is passed to the corresponding decoder-side feature \(D_{i}\) through the CCA module. In CCA, we perform average pooling and flatten operations on both \(O_{i}\) and \(D_{i}\) to generate the vector \(G(O_{i})\in R^{\mathbb{C}\times 1\times 1}G(D_{i})\in R^{\mathbb{C}\times 1 \times 1}\)  
Figure 4: Multi-head cross-attention detail diagram.  
Figure 5: Channel-wise cross-attention detail diagram.  
where \(G()\) represents global average pooling. This is performed to compress spatial information while preserving channel-wise information, as in Equation (20). Then, we use two weight matrices \(L_{1}\in R^{C\times C}\), \(L_{2}\in R^{C\times C}\) to weight and sum the two vectors to highlight channels with important information. Finally, the vectors are reshaped to their original size for each layer.  
\[M_{i}=L_{1}\cdot G(O_{i})+L_{2}\cdot G(D_{i}) \tag{20}\]  
Compared to regular skip connections, our method reduces the loss of spatial and channel information during feature map transmission through channel-wise fusion, effectively improving the segmentation performance.