esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proactive Balance Testing Program  
A guiding program was developed using Labview10.0 (National Instruments Corp., Austin, TX, USA). The forces from four load cells were used to calculate weight and COP in real time by the following equations. The weight is the sum of four forces (\(F_{i},\ i=1,\ 2,\ 3,\ 4\)) measured by four load cells in Wii balance board. These load cells are installed beneath the plate; they are numbered as shown in Figure 1. According to their positions, the position (\(x,y\)) of COP, which is the COM, can be calculated by the following equations, where \(L_{x}\) is the horizontal distance between \(F_{1}\) and \(F_{3}\) and \(L_{y}\) the vertical distance between \(F_{1}\) and \(F_{2}\).  
\[Weight=\sum_{i=1}^{4}F_{i} \tag{1}\]  
\[COP_{x}=\frac{(F_{3}+F_{4})L_{x}}{Weight} \tag{2}\]  
\[COP_{y}=\frac{(F_{2}+F_{4})L_{y}}{Weight} \tag{3}\]  
Here, the position of the first load cell is assumed to be at the origin of x-y coordinates.  
The initial COP can be reset to zero for those who have asymmetric balance and cannot move their COP to the origin (geometric center). The target distance is adjustable by changing the diameter of a circle. The direction of movements and number of trials are also adjustable.  
In order to test the validity of using the Wii balance board to detect APAs in proactive balancing task, a pilot test was performed. In this test, the Wii balance board was placed above a force plate (Kistler, 9260 AA). A subject stood on the Wii balance board and initiated weight shifting guided by the proactive balance test program. The COP calculated from the Wii balance board and the force plate were recorded. The correlations between the two devices were evaluated by Pearson correlation coefficient. High correlations between these two devices were shown on COP trajectories in both X (r > 0.99) and Y (r > 0.99) directions (Figure 2).  
Figure 1: The testing platform setup. The Wii balance board is wirelessly connected to the laptop via Bluetooth. The LabVIEW graphical user interface includes multiple control panels for program adjustment and data management. The screen displays the circular target and trajectory of the subject’s center of pressure (COP).
## 2 Materials and Methods
### Data Augmentation
#### 2.1.3 Augmentation of Faulty Samples Using the Superposition of 1D Observation Sequences  
To artificially generate data representing system responses to various excitations, including faults, a superposition of one-dimensional observation sequences can be applied. This procedure involves defining the system response to different types of faults and generating the system response to the given excitations.  
Figure 2: TimeGAN training flowchart.  
In this case, the system responses to local damages, \(y_{f}(t-\tau)\), where \(\tau\) indicates the moments of damage occurrence, were simulated, knowing the recorded system responses in the undamaged state, \(y_{0}(t)\), and in the damaged state, \(y(t)\).  
To determine the system response in the damaged state, given the system response in the undamaged state and the system response to local damage, the signal was divided into time segments and segments corresponding to typical tape damage patterns were identified. Then, fragments characterized by defects were inserted in randomly selected locations. Additionally, the amplitude of the damage signal was scaled to match the actual conditions of defect occurrence  
\[y(t)=k_{0}y_{0}(t)+\sum_{i}k_{i}y_{f}(t-\tau_{i}) \tag{1}\]  
The first component of the equation is the system response in the undamaged state, and the second represents the impact of locally scaled damages (shifted by \(\tau_{i}\)) on the system response.  
The method of generating synthetic responses recorded by the strain gauge is shown in illustrative Figures 3 and 4.  
Figure 3: Simulation of conveyor belt damages: (**a**) scaled system response to local damage at one location, (**b**) signal recorded by the strain gauge.
## 3 Experiment
### Detailed Analysis and Comparison
The results of the preceding comparison experiments clearly demonstrate that the MOMTA-HN algorithm exhibits a markedly superior success rate in information transmission compared to the multipath algorithms employing only the first type of simplification (MOMTA-I) or the second type of simplification (MOMTA-II). The MOMTA-HN algorithm showcases enhanced flexibility and reliability in addressing network attacks. Its advanced multi-objective optimization strategy allows it to adapt dynamically to diverse network environments, ensuring the successful completion of transmission tasks. In contrast, while the SPFA and Dijkstra algorithms demonstrate effective path selection capabilities under specific conditions, they lack the flexibility and robustness of the MOMTA-HN algorithm when confronted with network attacks and complex network environments. The MOMTA-HN algorithm's ability to utilize multiple types of simplifications allows it to maintain high transmission success rates even under adverse conditions. This capability is particularly crucial in heterogeneous network environments where attacks can occur unpredictably and affect various network segments differently. By effectively managing path redundancy and ensuring that redundant paths do not share critical links with the original paths, the MOMTA-HN algorithm significantly enhances the overall network reliability and security. In conclusion, the experimental results validate the effectiveness of the MOMTA-HN algorithm in providing robust and reliable data transmission in heterogeneous networks. Its superior performance, especially in maintaining transmission success rates under attack conditions, underscores its potential as a highly adaptable and secure solution for complex network environments. This study demonstrates that incorporating multi-objective optimization and advanced simplification techniques can significantly improve network resilience and data transmission reliability, offering valuable insights for future research and development in network security and performance optimization.
## Nomenclature  
\begin{tabular}{l l l} Parameters & & \\ \(P_{n}\) & Nominal power & kW \\ \(P_{m}\) & Measured power & kW \\ \(V\) & Velocity & m/s \\ \(\hat{m}_{\text{air}}\) & Mass flow rate of the air & kg/s \\ \(\hat{m}_{\text{total}}\) & Mass flow rate of the air in the nominal condition & kg/s \\ \(T\) & Temperature & \({}^{\circ}\)C \\ \(T_{\text{DB}}\) & Dry bulb temperature & \({}^{\circ}\)C \\ \(T_{\text{WB}}\) & Wet bulb temperature & \({}^{\circ}\)C \\ \(T_{\text{C}}\) & Condensation temperature & \({}^{\circ}\)C \\ \(P\) & Pressure & bar \\ \(\mathit{UIR}\) & Relative humidity & bar \\ \(P_{\text{overall}}\) & Overall electrical power & kW \\ \(q_{\text{rej}}\) & Heat transfer rate rejection in the evaporative condenser & kW \\ \(P_{\text{motor}}\) & Motor electrical power & kW \\ \(P_{\text{min}}\) & Minimum electrical power & kW \\ \(P_{\text{max}}\) & Maximum electrical power & kW \\
**Abbreviations** & & \\ COP & Coefficient of performance & \\ CP & Compressor & \\ EC & Evaporative condenser & \\ FHPCC & Floating head pressure condensation control & \\ HVAC & Heating, ventilation, and air conditioning & \\ OCP & Operation condensation pressure & \\ PLC & Programmable logic controller & \\ VSD & Variable speed drive & \\ Diff & Difference & \\ \end{tabular}
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 7 Validation and Discussion
### Compared Algorithms and Implementation Details  
Our analysis centers on a comparison of widely used iterative algorithms, as outlined in Table 1. Since the MOROR algorithm is fundamentally iterative in nature, and non-iterative algorithms often fall short in achieving image reconstruction quality on par with iterative approaches, our evaluation is limited to iterative methods, excluding non-iterative algorithms from the comparison.  
In Table 1, the PRPCG algorithm is a well-known unconstrained optimization algorithm and has a faster convergence rate than gradient optimization algorithms. The PRPCG algorithm is used on the premise that the objective function must be differentiable. The TL1R, L\({}_{1}\)\({}_{2}\)R, L1R, FL1R, and L\({}_{1/2}\)R algorithms are renowned for their emphasis on leveraging the sparsity prior of imaging objects. The L1 TV, L1 SOTV, ELNR, FOENR, and L1 LR algorithms are compound regularization methods. The LRR algorithm, renowned for its utilization of the nuclear norm to integrate the low-rank prior of imaging targets, represents a widely recognized approach in low-rank regularization methodologies. The algorithms selected for comparison are highly prevalent and have been extensively applied in the field of electrical capacitance tomography. Their extensive adoption establishes them as both credible and representative benchmarks for assessing performance. All the algorithms have been developed using the MATLAB R2021a platform.  
In the NSGA-II algorithm, the number of populations is set to 20, and the maximum evolution generation is 150.  
The MNN consists of an input layer, an output layer, and two hidden layers. The input layer takes capacitance signals and initially reconstructed images as input, with 1090 neurons. The output layer represents the true distribution of dielectric constants and has 1024 neurons. Each of the two hidden layers contains 2180 neurons, and their activation functions are sigmoid functions. To improve training stability, we have introduced two batch normalization layers. We use the Adam algorithm for training with an initial learning rate of 0.0001.  
The imaging domain is discretized into 32 \(\times\) 32-pixel elements, establishing a parameter space of a dimensionality of 1024. Within this framework, the dimensionality of the sensitivity matrix governing the physical model is 66 \(\times\) 1024, while the acquired capacitance measurements constitute a vector with a dimensionality of 66 \(\times\) 1.
# Surface Quality Evolution Model and Consistency Control Method of Large Shaft Multi-Pass Grinding
## 6 Conclusions  
This research studies the surface quality evolution and consistency control of large shaft multi-pass grinding. Based on the analysis of the grinding experimental data, it is found that the grinding surface quality is affected by the grinding process parameters and the surface quality of the previous grinding pass, which shows the evolutionary characteristics. Then, the surface quality evolution model is established based on Elman neural network. Furthermore, the consistency control method of surface quality is proposed by adjusting the actual grinding depth at the rough grinding stage. Experiments show the effectiveness of the proposed methods. The conclusions drawn from this research can be summarized as follows:  
(1) The surface quality indexes of multi-pass grinding of shaft parts, i.e., surface roughness and glossiness, have evolutionary characteristics and are affected by grinding parameters and surface quality of the previous grinding pass. The uneven distribution of the actual grinding depth along the length direction of the shaft is the reason for the inconsistent surface quality.  
(2) The surface quality evolution model based on Elman neural network accurately describes the evolution characteristics of surface quality, and the prediction errors of surface roughness and glossiness are less than 6%.  
(3) The consistency control method of surface quality guarantees the consistency of surface quality, reduces the grinding passes, and increases the grinding efficiency.  
Figure 16: The standard deviations of surface roughness and glossiness of measuring points.  
**Author Contributions:** Conceptualization, L.W. and S.F.; methodology, L.W. and S.F.; validation, S.F.; formal analysis, L.W. and S.F.; investigation, S.F.; data curation, L.W.; writing--original draft preparation, L.W. and S.F.; writing--review and editing, D.W. and X.L.; supervision, L.W.; funding acquisition, L.W., D.W. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by National Natural Science Foundation of China under Grant No.52105520, No.52275440 and No.51975319.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available upon request by contact with the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### The Societal Level  
#### 4.3.1 Reduced Public Health Costs and the Exemplary Effect  
The interviewees were not able to describe the effects at the societal level as clearly as the effects at the organizational level, let alone the individual level. However, a few potential direct and indirect societal impacts emerged from the interviews. The first related to societal resources, that is, how the promotion of physical activity at the organizational level has societal effects, for example, in terms of decreased social and health care costs: \"_After all, it is pretty clear if you look at the data, musculoskeletal disorders and diabetes and others, it is going to be a huge public health cost... I think employers and leaders should be even more concerned about this and really do even more than is currently being done_\" (#2).  
The second impact is connected to the exemplary effect described above but on a societal level. Most of the interviewees had mentioned their physical activity in various magazines and newspaper interviews, as well as on social media, for example. However, the interviewees were cautious about how significant they rated the impact to be. The views varied from a statement that \"_I am a small drop in the ocean_\" (#18) to the following comment: \"_The best feedback I have ever received, both inside and outside the organization, are the hundreds if not thousands of messages I have received. 'Hi, I looked at your way of life and decided that if you have time, so will I, and here I stand 30 kilograms lighter_\" (#7). Additionally, some of the interviewees served on the boards of sports organizations and exerted a positive influence through that particular role.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 2 Problem Formulation
### Objective Function  
The purpose of this study is to determine the commitment and dispatch of power from both diesel generators and batteries that would minimize the operating cost. Therefore, the paper calculated two types of operating costs: diesel generator and startup cost. The objective function was formulated as follows, where \(F\) is the total operating cost, \(T\) is the total scheduling period, \(C\) is the linearized fuel cost of the diesel generator, and \(C_{st}\) is the startup cost:  
\[\text{Min }F=\sum_{t=1}^{T}C(t)+C_{st}(t) \tag{1}\]  
Figure 1: The architecture of the research.
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 3 Results and Discussion
### Compressive Strength  
Insufficient strength of pervious concrete has always been the main factor restricting its wide application. Due to its low strength, pervious concrete is generally applied to non-loaded pavements. Improving the strength of pervious concrete has always been the focus and hotspot of research. The compressive strength of the silica fume-modified pervious concrete of this experiment is shown in Figure 9. It can be seen that the addition of silica fumes improved the compressive strength of the pervious concrete. The compressive strengths of all groups met the standard requirement of 20 MPa for pervious concrete in the Chinese national standard [34]. With the increasing amount of silica fumes, the compressive strength gradually increased, and the compressive strength reached maximum when the addition of silica fumes was 9% and 12%. Compared to the control group (0% silica fume addition), the maximum improvement of the compressive strength of silica fume-modified pervious concrete was 14%. This was because the aggregate of pervious concrete mainly bonded together by the cement paste layer, and the interface characteristics between the bonding layer and aggregate have an important influence on the strength of the pervious concrete. Silica fume is a pozzolanic material that can cause secondary reaction (pozzolanic reaction) with Ca(OH)\({}_{2}\) produced by cement hydration to form a volcanic ash C-S-H gel, which improves the property of the traditional cement C-S-H gel and interface characteristics between the hardened cement paste and aggregate. In addition, silica fume has smaller particle size and larger specific surface area than the cement, and the micro-aggregate effect is remarkable. It can fill the harmful pores of the cement paste, make the pore distribution more uniform, and improve the pore structure of the hardened cement paste. Thus the strength of pervious concrete was improved.  
Figure 8: Permeability coefficient of silica fume-modified pervious concrete.
###### Abstract  
To address the issue of soil erosion and limited economically valuable vegetation resources, perennial sweet peas were introduced to Hebei Province, China, and showed favorable biological attributes. Nevertheless, its specific efficacy within soil and water conservation endeavors requires further examination. This study selected four trial sites within Hebei Province to assess four-year-old perennial sweet peas' soil and water conservation functionality. The findings underscored that cultivating perennial sweet pea plots on 9\({}^{\circ}\) disturbed slopes notably outperformed bare ground (CK) in their rainwater absorption capacity. Specifically, in the 0-20 cm soil layer, moisture increased from 10.51% to 17.39%, in the 20-40 cm layer from 10.63% to 17.25%, and in the 40-60 cm layer from 10.09% to 16.04%. The dense canopy formed by perennial sweet peas effectively intercepted 25-32% of precipitation. Fallen organic matter also demonstrated commendable water absorption features. During severe rain, the perennial sweet pea showcased a 90.4% runoff reduction and a notable sediment interception. Their deep and well-developed root system enhanced soil structure and infiltration. The outcomes of this study highlight the perennial sweet peas' potential in soil erosion mitigation, rainwater retention, and soil improvement, which carries substantial implications for sustainable land management and ecosystem restoration initiatives. Furthermore, the successful introduction of perennial sweet peas could serve as a model for analogous ecological interventions in regions confronting similar challenges, offering holistic solutions to soil and water conservation in environmentally sensitive areas.  
perennial sweet pea; soil erosion; soil and water conservation; surface runoff reduction; soil infiltration capacity
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Emulator
#### 3.1.1 TTHNet  
Conducting an experimental transmission test in an underwater environment is costly, but there is no accurate analytic model as a reference for underwater high-speed VLC. In order to reduce the cost of testing underwater VLC systems, a machine learning method is needed to model the underwater channel. The two- tributaries heterogeneous neural network (TTHnet) uses a convolutional neural network (CNN) for modeling the linearity  
Figure 5: Generalizability of (**a**) Volterra and (**b**) MLP for different cases; * represents the same model.  
of the underwater VLC channel and a two-layer MLP with a hollow layer for modeling the nonlinearity of the underwater VLC channel [44]. The two-branch heterogeneous structure makes full use of the CNN's shared parameters, thus reducing the system complexity. At the same time, it utilizes the MLP's extremely strong nonlinear fitting capability to fit the nonlinearity in the channel. Experiments show that the channel modeled by TTHnet is extremely close to the real channel, and the average spectrum mismatch is only 36.2% of the MLP-based channel emulator and 44.3% of the CNN-based channel emulator.
# Enhancing Short-Term Sales Prediction with Microblogs: A Case Study of the Movie Box Office
## 3 Framework of the Microblog-Based Short-Term Sales Prediction  
### Architecture  
Unlike the previous prediction work focusing on microblogs' quantitative characteristics and text characteristics, this paper proposes a new prediction framework based on the topic characteristics of microblogs, the sentiment polarity, and the influence of microblogs (as shown in Figure 1). The original microblog was crawled from Sina Weibo based on keywords, including the original microblogs, comments, and the forwarding of these microblogs. The fields extracted from the microblog data included microblog content, the number of likes, comments, forwards, and user information [26]. After preprocessing the microblog content, the sorted word segmentation results were used to obtain the topic distribution. According to the topic distribution and sentiment dictionary of microblogs, the sentiment polarity of a microblog was calculated. Through some measurement methods of microblog influence, we can obtain the influence of these original microblogs. Then, using the sentiment polarity and microblog influence as the input, we trained a prediction model for a short-term product sales prediction. Below, we detail the critical modules of the architecture, shown in Figure 1.
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 2 Methods
### Three-Dimensional Reconstruction Method
#### 2.3.2 Recovering the 3D Coordinates  
The projection matrix \(\mathbf{M}\) can be obtained by combining the intrinsic and extrinsic parameters as follows:  
\[\mathrm{s}\left(\begin{array}{c}u\\ v\\ 1\end{array}\right)=\left[\begin{array}{cccc}m_{11}&m_{12}&m_{13}&m_{14}\\ m_{21}&m_{22}&m_{23}&m_{24}\\ m_{31}&m_{32}&m_{33}&m_{34}\end{array}\right]\left(\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right) \tag{8}\]  
Equation (8) can be written in the following form:  
\[\begin{array}{c}m_{11}X_{w}+m_{12}Y_{w}-su=-m_{14}-m_{13}Z_{w}\\ m_{21}X_{w}+m_{22}Y_{w}-sv=-m_{24}-m_{23}Z_{w}\\ m_{31}X_{w}+m_{32}Y_{w}-s=-m_{34}-m_{33}Z_{w}\end{array} \tag{9}\]  
which leads to:  
\[\left[\begin{array}{ccc}m_{11}&m_{12}&-u\\ m_{21}&m_{22}&-v\\ m_{31}&m_{32}&-1\end{array}\right]\left(\begin{array}{c}X_{w}\\ Y_{w}\\ s\end{array}\right)=-\left(\begin{array}{c}m_{14}\\ m_{24}\\ m_{34}\end{array}\right)-Z_{w}\left(\begin{array}{c}m_{13}\\ m_{23}\\ m_{33}\end{array}\right) \tag{10}\]  
Then, the 3D world coordinates of the target points on the bridge can be calculated by:  
\[\left(\begin{array}{c}X_{w}\\ Y_{w}\\ s\end{array}\right)=\left[\begin{array}{ccc}m_{11}&m_{12}&-u\\ m_{21}&m_{22}&-v\\ m_{31}&m_{32}&-1\end{array}\right]^{-1}\beta \tag{11}\]  
where \(\beta=-\left(\begin{array}{c}m_{14}\\ m_{24}\\ m_{34}\end{array}\right)-Z_{w}\left(\begin{array}{c}m_{13}\\ m_{23}\\ m_{33}\end{array}\right)\), \(Z_{w}\) is the vertical distance between the fixed object plane and the bridge model plane. Finally, by substituting the pixel coordinates \(u\) and \(v\) of the target points into Equation (11), the 3D world coordinates of the points can be recovered. The real displacement of the target points can be calculated from the subtraction between \(Y_{wi}-Y_{w1}\) (where \(i\) is the frame number, \(Y_{w1}\) is the value of the original coordinate).  
Ultimately, the approach mentioned above is compared with the homography transformation approach.