esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Construction
### Reward Function
\begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **Specific Power Interval (kw/t)** & **CO\({}_{2}\) Emission Rate (g/s)** & **CO\({}_{2}\) Emissions per Second** \\ \hline
1 & \(<\)\(-\)2 & 1.5437 & 1.5437 \\
2 & \(-\)2\(-\)0 & 1.6044 & 1.6044 \\
3 & 0\(-\)1 & 1.1308 & 1.1308 \\
4 & 1\(-\)4 & 2.3863 & 2.3863 \\
5 & 4\(-\)7 & 3.2102 & 3.2102 \\
6 & 7\(-\)10 & 3.9577 & 3.9577 \\
7 & 10\(-\)13 & 4.7520 & 4.7520 \\
8 & 13\(-\)16 & 5.3742 & 5.3742 \\
9 & 16\(-\)19 & 5.9400 & 5.9400 \\
10 & 19\(-\)23 & 6.4275 & 6.4275 \\
11 & 23\(-\)28 & 7.0660 & 7.0660 \\
12 & 28\(-\)33 & 7.6177 & 7.6177 \\
13 & 33\(-\)39 & 8.3224 & 8.3224 \\
14 & \(\geq\)39 & 8.4750 & 8.4750 \\ \hline \end{tabular}
\end{table} 
Table 2: Vehicle CO\({}_{2}\) emission rates for different \(VSP\) intervals. Combining both carbon emissions and traffic efficiency, the comprehensive reward function (Reward-CO2 Reduction) is as follows:  
\[R_{CO_{2}}=k_{1}(b_{1}E_{I-1}-E_{t})+k_{2}(b_{2}D_{I-1}-D_{t}) \tag{11}\]  
where \(k_{1}\) and \(k_{2}\) are weighted factors are determined by the mean queue length of vehicles at the intersection. The formula for calculating these weights is as follows:  
\[k_{1}=\left\{ \begin{array}{l}e^{-1.2(\frac{Queue_{avg}}{Queue_{std}})}-0.2, \;Queue_{avg}\leq Queue_{std}\\ e^{-1.2}-0.2,\;Queue_{avg}>Queue_{std}\end{array} \right. \tag{12}\]  
\[k_{2}=\left\{ \begin{array}{l}1.2-e^{-1.2(\frac{Queue_{avg}}{Queue_{std}})},\;Queue_{avg}\leq Queue_{std}\\ 1.2-e^{-1.2},\;Queue_{avg}>Queue_{std}\end{array} \right. \tag{13}\]  
where \(Queue_{avg}\) represents the average number of vehicles in the queue for each lane, and \(Queue_{std}\) represents the average queue threshold. This threshold can be adjusted to modify the weights. In this study, considering that, when fuel-powered vehicles and electric vehicles mix, the carbon emissions of electric vehicles in an idling state are lower than those of traditional fuel-powered vehicles, the optimization effect of the carbon emission index is easily influenced by the current vehicle composition at the intersection. Therefore, it is necessary to dynamically adjust the weight coefficients of carbon emissions and waiting time, allowing the agent to find the optimal solution. After multiple experiments, the threshold is set to 3.
## 3 Parametric Modeling of GSPCW Walls
### Concrete Compressive Strength  
The compressive strength of concrete was changed from 30 MPa to 60 MPa, and the parametric modeling results are shown in Figure 11. The results show that with increasing concrete strength, the lateral load capacity and stiffness of the test walls increased to a certain degree, but the rate of increase rate was small. Compared with other design variables, the compressive strength of concrete has less influence on the seismic behavior of GSPCW walls.
###### Abstract  
This paper proposes a method for 3D reconstruction from Freehand Design Sketching (FDS) in architecture and industrial design. The implementation begins by extracting features from the FDS using the self-supervised learning model DINO, followed by the continuous Signed Distance Function (SDF) regression as an implicit representation through a Multi-Layer Perceptron network. Taking eyeglass frames as an example, the 2D contour and freehand sketch optimize the alignment by their geometrical similarity while exploiting symmetry to improve reconstruction accuracy. Experiments demonstrate that this method can effectively reconstruct high-quality 3D models of eyeglass frames from 2D freehand sketches, outperforming existing deep learning-based 3D reconstruction methods. This research offers practical information for understanding 3D modeling methodology for FDS, triggering multiple modes of design creativity and efficient scheme adjustments in industrial or architectural conceptual design. In conclusion, this novel approach integrates self-supervised learning and geometric optimization to achieve unprecedented fidelity in 3D reconstruction from FDS, setting a new benchmark for AI-driven design processes in industrial and architectural applications.  
freehand design sketching; 3D shape reconstruction; deep learning; geometric reconstruction algorithms; image processing techniques +
Footnote †: journal: Applied sciences
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Sick Leave Prediction
#### 5.2.3 Long-Term Predictive Performance (Multi-Target Prediction)  
To evaluate how well we can predict sick leave one month in advance based on our data representation, we compared the performance of different models, including the baseline, regression trees, and various ensemble methods, using four evaluation metrics. The results, presented in Figure 11, provide insight into the effectiveness of these approaches (detailed results are available in Table S8 of the Supplementary Materials).  
Figure 9: Results for one-week-ahead sick leave prediction for all four evaluation metrics: MAE, MSE, RMSE and CC.  
Among the methods evaluated, Bagging stands out as the best performing method, achieving the lowest average MAE, MSE, and RMSE, while also having a high correlation coefficient (CC). Random Forests, while slightly behind Bagging, also demonstrate comparable performance.  
Additionally, it is evident from the figure that predictions for the first week are more accurate than for subsequent weeks, as reflected in the lower RMSE for W1 compared to later weeks. This decline in predictive accuracy as the forecast period extends illustrates the challenges of long-term forecasting.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 3 Chromatography
### Liquid Chromatography and Mass Spectrometry
#### 3.3.1 LC-MS as Confirmatory Tool  
Early efforts to use LC-MS for aflatoxin anaysis focused on thermospray MS coupled with LC [242; 243]. As a soft ionization technique, thermospray MS could identify aflat based on their molecular ions. Combined with retention times on the LC, the technique was believed to be a useful tool for confirmation. [PERSON] et al. [244] carefully characterized a commercial thermospray MS for the detection of aflatoxin B\({}_{1}\), B\({}_{2}\), G\({}_{1}\) and G\({}_{2}\) in peanuts. LC separation was achieved within 12 min on a C18 (4.6 mm ID \(\times\) 25 cm, 5 \(\upmu\)m particle size) column with 0.1 M ammonium acetate-methanol-acetonitrile (56:22:22, _v/v/v_) as the mobile phase and a flow rate of 1.0 mL/min. The identification and sensitivity of aflat in sample extracts were evaluated using both full scan and selected ion monitoring (SIM) modes. Using the SIM mode, aflat could be detected below 1 ng (on column mass). Although it was less sensitive than LC-FLD, the study demonstrated that LC-MS could, at least, be used as a confirmatory tool. In the meantime, using thermospray MS, [PERSON] et al. [119] obtained the structural information and molecular weights of derivatized aflat, aflatoxin B\({}_{1a}\) and B\({}_{2a}\), confirming the mechanism of the post-column chemical derivatization using iodine, which has been widely used to enhance fluorescence of aflatoxin B\({}_{1}\) and G\({}_{1}\) for the LC-FLD analysis. Another notable attempt was the hyphenation of a particle beam interface with electron impact (EI) ionization MS by [PERSON] et al. [245]. Aflatoxins were introduced into the ion source via a reversed phase capillary column. The resulting full scan mass spectrum could be used for identification and improved sensitivity was achieved using the SIM mode for quantitation with estimated method detection limits around 10 ppb for individual aflat.  
Although these early LC-MS methods could not provide sufficient sensitivity for quantitative analysis of aflat in foods, they were appreciated as a tool for confirmation with a high degree of specificity [246; 247]. Back in the 1960s, researchers already realized the importance of confirming the identity of \"presumptive\" aflat in positive samples. Extra steps of using another method to confirm the identity of detected aflat were cumbersome but necessary when results are required to support sample compliance. Initially, aflatoxin confirmation was performed by bioassays [248], which was an indirect and time-consuming approach and soon replaced by techniques based on physicochemical properties of aflat (e.g., 2D-TLC, chemical derivatization and FLD) [211; 249; 250]. When MS became available, negative chemical ionization MS- [182], gas chromatography (GC)-MS- [251] and eventually, LC-MS-based confirmation was adopted [136; 252] by private and government laboratories.
## 4 Data Acquisition and Analysis
### Time Series Analysis  
We recorded data for approximately 17 to 24 h at each station. However, not all of the measured data could be used due to persistent noise, particularly during the daytime when urban activities are at their peak. After reviewing the time series segment, we chose to focus only on the data collected at midnight, as these segments have comparatively lower levels of noise. Figure 2 compares the orthogonal EM fields recorded during daytime (07:00-07:59) and nighttime (02:00-02:59). The north-south \(E\) component (_Ex_) measured during the daytime fluctuated around \(\pm\)1000 mV/km, whereas at night, the magnitude was reduced by approximately half, to \(\pm\)500 mV/km. In the _Hy_ component, the \(H\) perpendicular to _Ex_, showed similar values, typically around \(\pm\)2.5 nT. However, we observed significant disturbances in the daytime data, particularly spikes in the time series, along with strong power extending across a broad frequency range in the spectrogram. These are commonly attributed to moving vehicles [44], with the magnitude and frequency range of the disturbance varying depending on factors such as vehicle size and speed. In contrast, such disturbances were minimal during the night, except for one brief anomaly at 660 s. The 60 Hz powerline signal was observable in both daytime and nighttime data, which can be removed using a notch filter. To reduce the impact of the random noise, we applied remote reference processing, incorporating data from a synchronized remote station located away from the noise source.  
Figure 2: Comparison between two orthogonal components (_Ex_ & _Hy_) during daytime (**left**) where noise is on a higher level than the nighttime (**right**).
## 3 The Research Results and Discussion
### Economic and Technical Indicators  
The indicated power increases with increases in the boost pressure for all cases of the \(D_{b}\)/\(T\) ratio, while the indicated specific fuel consumption (ISFC) decreases. The possible reason is that when the boost pressure increases, the maximum pressure of the cycle also goes up if the same quantity of fuel is injected, and, therefore, the work carried out in the cycle during the expansion process is greater under the condition that the displacement and the volume are unchanged (Figures 16 and 17).  
Figure 16: Dependence of ISFC on the change in \(p_{in}\) and \(D_{b}\)/\(T\) ratio.  
The ignition delay decreases (due to a rise in the ratio of the volumetric mixture formation process), while the value of the heat transfer into the wall of the combustion chamber grows with an increase in the \(D_{b}\)/\(T\) ratio. Additionally, the combustion process is faster and produces more effective work by the expansion process. These two factors determine the best values of the indicated power and the ISFC, which depends mainly on the \(D_{b}\)/\(T\) ratio (the presence of the optimal value of the \(D_{b}\)/\(T\) ratio).  
When the boost pressure increases, the heat release process begins earlier (Figure 12), and more fuel gets burned in the combustion chamber volume. As a result, the optimal value of the \(D_{b}\)/\(T\) ratio increases, and the range of variation of the indicated power and ISFC becomes larger (Figures 16 and 17). At the same time, the nature of the changes in the indicated power and the ISFC for the boost pressures of 0.35 and 0.45 MPa is different from those values obtained at the boost pressures of 0.15 and 0.25 MPa.
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 2 Background and Related Work
### Trust Management in MANETs  
In the dynamic environment of a MANET, trust management plays an essential role [8]. Therefore, several studies have developed various approaches, such as reputation-based frameworks, trust establishment frameworks, and policy-based trust management. In reputation-based trust management [17], trust values are calculated by aggregating and distributing reputation among residents. In this scheme, a destination node rates the reputation values of its upstream nodes by  
Figure 4: Sample Network with an NIA.  
checking the packets delivered and acknowledges the source node. Based on the occurrence of transactions, each node that participated in the message transmission process maintains the trust values of other participants in their distributed routing table. However, trust values are not verified, meaning if the rating node is malicious, all nodes that participate in message transmission can be manipulated in this scheme. Additionally, the rating process occurs after packet transmission, meaning sender nodes cannot be sure of the trust values of their relay nodes.  
Some researchers have argued regarding the limitations of reputation-based systems, which include vagueness, complexity, and inaccurate characterization [18]. Some have proposed policy-based trust management, where logical rules are used to distinguish trusted entities. In [18], the authors combined policy-based and reputation-based trust systems to create an integrated system that enhances the security and flexibility of other solutions. Trust is necessary for both service-requesting nodes and service-providing nodes. A requested service is evaluated based on control policies, while the provider must also verify that its reputation is sufficiently high to satisfy the request. Although this method meets the needs of trust management in MANETs by combining two mechanisms, it heavily depends on prior information, rather than dynamic trust management.
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### Tukey Test--Reality Scenario  
Using the Tukey test, we calculated HSD = 0.032. After analyzing the impact between the message delivery probability results of the routing algorithms, we concluded that there were significant differences in the performance averages between all routing algorithms in the battery range \(E\) = [25; 100].  
In the battery range \(E\) = [0, 100], there was no difference between the performance of the proposed algorithm concerning the GPDMI algorithm. However, there was a slight difference between the \(e\)GPDMI algorithm with 32% and the PRoPHET algorithm with 48% delivery rates.  
The relationship between the PRoPHET algorithm with 48% concerning GPDMI and 32% shows that the last two comparisons maintained an average of similar deliveries.  
The calculation of the absolute difference of the averages (module of the averages) supports the delivery rates in Figure 6, where \(E\) = [25; 100], the GPDMI algorithm had 20%, PRoPHET 30%, and \(e\)GPDMI 32% of the message delivery probability. In the limits of \(E\) = [50, 100] and \(E\) = [75; 100], no significant differences were found between the comparison of the PRoPHET and GPDMI algorithms.  
Some differences were found in other algorithms when comparing both limits. \(e\)GPDMI maintained the best results compared with GPDMI and PRoPHET. The proposal (\(e\)GPDMI) had about 32% and 30% of the delivery performance over 10% against less than 5% of the other algorithms. In summary, \(e\)GPDMI obtained a statistically different performance in almost all battery ranges, except compared to GPDMI in the \(E\) = [0, 100] range. The exact values for the Tukey test are described in Table 11.
## 3 Results and Discussion
### Results of Biodegradation
#### 3.4.1 Degradation of DOC
Figure 3: Degradation of DOC for the different fractions presented as c/c\({}_{0}\) versus time (**A**) and exponential degradation phase of DOC presented as logarithm of c/c\({}_{0}\) versus time (**B**). The results of the present investigations indicate that the smaller fractions (125 \(\upmu\)m to 200 \(\upmu\)m and 200 \(\upmu\)m to 630 \(\upmu\)m) had a higher moisture content and therefore WFPS of 86% and 77% due to higher proportion of fine pores. This soil moisture content should be able to allow high microbial activity, but the growth was limited by the reduced diffusion of air into the soil. The minimum pore space filled with air should be higher than 10% by volume to create conditions for aerobic biodegradation, as reported by [24; 26; 72]. Moreover, the lower proportion of coarse pores in the smaller fractions slows down and reduces the supply of nutrients in the accessible areas. In contrast, the supply with oxygen and nutrients was more efficient in the largest fraction (1000 \(\upmu\)m to 2000 \(\upmu\)m) with the lowest WFPS (44%) and the highest air capacity. Nevertheless, the lower moisture content caused by the lower water storage capacity of the coarse pores is the limiting factor here for bacterial growth and the corresponding metabolization of DOC.
## 1 Introduction
Third, the SSD coupling method used in this paper correlates seepage-stress with damage to reflect the impact of damage on the seepage of concrete linings. The relationship between the seepage coefficient and damage was used as a bridge connecting the seepage field, stress field and damage field. Some scholars have also explored such methods. For example, [PERSON] [25] derived the permeability coefficient conversion equation taking into account the damage to the tunnel rock and depicted the SSD multi-field coupling model of the surrounding rock. He analyzed the stability of the surrounding rock excavated in the tunnel construction based on the fluid-solid coupling theory. [PERSON] et al. [26] established an SSD coupling algorithm based on the permeable lining theory and applied it to high-pressure hydraulic tunnels. Their results are consistent with the general engineering laws and provide a reference for solving practical engineering problems. [PERSON] et al. [27] believe that the influence of groundwater on slope stability cannot be replaced by pore water, and the synergy of the seepage field and the stress field must be considered in foundation pit slope engineering. [PERSON] et al. [28] established the equation of relation between rock failure and coefficient of permeability based on damage variables and seepage-stress coupling. They described the evolutions of the rock-failure-based permeability and groundwater seepage field. They studied the evolutionary relationship between rock mass stress and strain, permeability and strain, strain and failure, as well as permeability and failure. [PERSON] et al. [29] coupled failure and fluid flow to the Mohr-Coulomb failure criterion, based on the dynamic evolution of damage, porosity and permeability, and proposed SSD models under the effect of hydraulic fracturing and natural fracturing based on the dynamic evolution of damage, porosity, and permeability. The results are very close to the engineering practices. The above research findings and the SSD coupling method used in this paper make the predictions more realistic. Aiming at the above problems, this study used the SSD coupling method to predict the SNWDP's service life and performance evolution after long-term operation. Taking into account the change in the permeability coefficient induced by soil consolidation over time and the evolution of the infiltration field and its performance after long-term operation, it is necessary to discover the internal mechanism of the seepage failure and further explore the long-term changes in the performance of typical deep excavated sections of the SNWDP. The ultimate goal of this study is to predict the performance evolution of the deep excavated canal in the SNWDP Xichuan Section after long-term operation, providing a theoretical basis for the actual operation of the project.
# SOH Estimation of Lithium-Ion Battery Pack Based on Integrated State Information from Cells
## 5 Conclusions  
To solve the problem of insufficient background information from the interaction degradation of cells in SOH prediction, this paper proposed a modified data-driven method and regulation for the SOH prediction of a lithium-ion battery pack based on deep learning, with the fusion of multi-cell deterioration information. With experimental data analysis and validation, it was demonstrated that compared to SOH prediction using only an overall indicator of the packs, the proposed method performed better for future trend forecasting. The proposed method also describes the relationship between degradation process of cells and packs with neural networks. The proposed method mines the deep influence from cells on a battery pack, and improves the input quantity of the information. Because of this, the method improves the prediction accuracy and stability efficiently along with providing internal information on battery pack structures, and prevents the strong dependency on limited external factors.  
Take advantages of the development of machine learning, data-driven methods bring a qualitative improvement to SOH management and prediction in order to help improve the application quality of a power lithium-ion battery. However, in applications, there are still some problems need to solve. On the one hand, even if we proposed ideal model to predict the SOH of battery packs, the performances of the model still depend on the parameters choices facing to different training data, which is a common problem in machine learning method. On the other hand, the model adopted should be improved and optimized with the development of deep learning technology. More advantageous data-driven analysis methods are preferred to be selected in our future research. What is certain is that according to the ideas and method proposed in this paper, the performance will be gradually optimized with adaptive research carried out in the future.  
[PERSON] designed the research and experiments; [PERSON] wrote the paper and analyzed the data; [PERSON] performed the experiments and polished illustrations; [PERSON] supervised and administrated the projects; [PERSON] conceived the research and primary content. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant number 51805016), Strategic Leading Science & Technology Programme (Class A) of Chinese Academy of Sciences (grant number XDA14000000), Field Foundation of China (No. JZXY20190242012001) and the Fundamental Research Funds for the Central Universities (grant number. YWF-20-BJ-J-908).  
**Conflicts of Interest:** The authors declare no conflict of interest.