esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**FTIR**} \\ \hline \multicolumn{3}{c}{**Band Location (cm\({}^{-1}\))**} \\ \hline \multicolumn{3}{c}{**Borage Oil**} & \multicolumn{1}{c}{**Milk Thistle Oil**} \\ \hline
3005 & 3005 & \(\
u\)(=C-H\({}_{\rm m}\), _cis_-) \\
2951 & 2950 & \(\
u_{\rm as}\)(-CH\({}_{2}\)) and \(\
u_{\rm s}\)(-CH\({}_{2}\)) (aliphatic groups in triglycerides) \\
2918 & 2918 & \(\
u_{\rm as}\)(-CH\({}_{2}\)) and \(\
u_{\rm s}\)(-CH\({}_{2}\)) (aliphatic groups in triglycerides) \\
2852 & 2852 & triglycerides) \\
1741 & 1741 & \(\
u\)(-C=O\({}_{\rm vst}\)) in esters \\
1703 & 1704 & \(\
u\)(-C=O\({}_{\rm vw}\)) in acids \\
1646 & 1646 & \(\
u_{\rm vw}\)(-C=C\({}_{\rm c}\), _cis_-) \\
1458 & 1458 & \(\delta_{\rm vw}\)(-C-H) in CH\({}_{2}\) and CH\({}_{3}\) groups, deformation \\  & & (scissor) and \(\
u_{\rm vw}\)(-C-H, _cis_-) deformation (wagging) \\
1372 & 1372 & \(\
u_{\rm w,\,m,\,vw}\)(-C-H, -CH\({}_{3}\)) and deformation \\
1315 & 1315 & \(\delta_{\rm m}\)(-C-H, -CH\({}_{3}\)) \\
1273 & 1273 & \(\
u_{\rm m}\)(-C-O) or \(\delta_{\rm m}\)(-CH\({}_{2}\)-) \\
1230 & 1230 & \(\
u_{\rm m}\)(-C-O) or \(\delta_{\rm m}\)(-CH\({}_{2}\)-) \\
1155 & 1155 & \(\
u_{\rm sf}\)(-C-O) or \(\delta_{\rm sf}\)(-CH\({}_{2}\)-) \\
1137 & 1137 & \(\
u_{\rm m,vw}\)(-C-O) \\
1093 & 1093 & \(\
u_{\rm m,vw}\)(-C-O) \\
1050–900 & 1050–900 & \(\delta_{\rm w}\)(-HC=CH-, _trans_-) out-of-plane deformation or \\  & & (\(\delta\)(-(CH\({}_{2}\))\({}_{\rm n}\)- and -HC=CH- (_cis_-) deformation (scissor) \\ \hline \end{tabular} \(\
u\)—stretching vibrations, \(\delta\)—deformation vibrations, \(\delta\)—symmetric, as—asymmetric, st—strong, w—weak.  
\end{table} 
Table 9: Locations of FTIR absorption band maxima and assignment of particular vibrations to the respective samples, forage oil and milk thistle oil, registered within the spectral range of 800–3200 cm\({}^{-1}\)[24; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. In the case of the stretching vibrations of the C=O and the corresponding spectral band, a slight enhancement is present on the short-wavelength side at ~1703 cm\({}^{-1}\), which can be attributed to the formation of a hydrogen bond between C=O\(\cdots\) H-O- groups. The observed spectral differences correlate very well with the differences in the fatty acid profile presented in Table 2 and described in the first part of the paper.
## 6 Conclusions  
This study presents an integrated framework combining digital twin and knowledge graph technologies to enhance risk assessment and emergency response in urban tunnel construction. By continuously integrating real-time monitoring data with a semantic reasoning system, the proposed approach enables dynamic risk prediction, intelligent decision support, and interactive visualization. The bidirectional interaction between the digital twin and knowledge graph ensures that raw sensor data are transformed into structured knowledge, facilitating automated inference and proactive mitigation strategies for tunneling-induced ground disturbances.  
The method has been successfully applied in a practical urban tunnel construction in Shanghai, China. The results demonstrate that this approach improves situational awareness, construction safety, and response efficiency, offering a scalable solution for intelligent infrastructure management. However, limitations such as data availability, computational complexity, and adaptability to unforeseen conditions highlight the need for further advancements. Future research should focus on enhancing machine learning-based reasoning, optimizing system scalability, and expanding real-world validation across diverse geotechnical and urban environments.  
By bridging the gap between real-time monitoring and advanced risk analysis, this study contributes to the development of intelligent, sustainable, and resilient urban tunnel construction. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems.  
Conceptualization, C.C. and Y.L.; methodology, C.C. and B.W.; software, C.C. and B.W.; validation, Y.L. and L.L.; formal analysis, C.C.; investigation, L.L.; resources, L.L.; data curation, B.W.; writing--original draft preparation, C.C. and Y.L.; writing--review and editing, B.W.; visualization, B.W. and L.L.; supervision, C.C. and Y.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This work was substantially supported by the Key R&D Plan of Shandong Province (2021 CXGC011203) and the Shandong Province Housing and Urban Rural Construction Science and Technology Plan (2019-K7-12).  
Not applicable.  
The monitoring data used in this study were obtained from an ongoing urban tunnel construction project and are subject to confidentiality agreements with the project stakeholders; therefore, the raw datasets are not publicly available. Structured descriptions of the data schema, system requirements, and pseudocode illustrating the reasoning workflow are available upon reasonable request to the corresponding author.  
Author [PERSON] was employed by the company Jinan Transportation Development Investment Co., Ltd. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 4 Numerical Results  
In this section, we compare the codes and coding techniques described in Section 3. Only \(R=3/4\) codes with syndrome memory \(m=2\) are studied since they provide the smallest delay and lowest complexity requirements. First, in Section 4.1, we consider a memoryless channel (BEC) and study influence of the code and decoder parameters on the packet-recovering performance. In Section 4.2, we investigate performance of the Wyner-Ash and the RS-convolutional codes in the channel with memory (M-BEC) where packet losses are generated according to the Gilber-Elliott model obtained from the fading channel model as explained in [19; 20].
# Evaluation of Turkey's Road-Based Greenhouse Gas Inventory and Future Projections
## 3 Results
### Emission Estimates for Future Years
The polynomial regression model yielded an RMSE of 310,527 and an MAE of 260,123 for predicting PC registrations over the 2019-2021 period. These results indicate that the model deviates from actual values by approximately 260,000 vehicles on average, with  
Figure 6: Vehicle-based emission estimates for the future. (**A**) Estimated CO\({}_{2}\) emissions, (**B**) Estimated N\({}_{2}\)O emissions, (**C**) Estimated NH\({}_{3}\) emissions, (**D**) Estimated NO\({}_{2}\) emissions, (**E**) Estimated PM10 emissions, (**F**) Estimated SO\({}_{2}\) emissions, (**G**) Estimated CO emissions, (**H**) Estimated NMVOC emissions. RMSE reflecting the influence of higher-magnitude errors on overall model performance. The LCV prediction model yielded an RMSE of 211,415 and an MAE of 189,297, indicating an average deviation of approximately 189,000 vehicles from actual values. While the model captures historical trends with reasonable accuracy, year-to-year fluctuations may limit its precision at finer temporal scales. The HDV prediction model produced an RMSE of 57,221 and an MAE of 41,770, indicating an average deviation of approximately 42,000 vehicles. The relatively low error values suggest strong predictive accuracy and a close alignment with historical HDV registration trends. It should be noted that polynomial regression models assume trend continuity and may fail to capture disruptive changes in technology, policy, or behavior, which can substantially alter future emission trajectories. Turkey needs to immediately implement several measures to achieve its goal stated at the 27 th Conference of the Parties (COP 27) in addressing climate change. Although sophisticated technology and low emissions are essential, it is important to acknowledge that our nation's sluggish pace of vehicle replacement may delay the noticeable effects of new technology vehicles on overall emissions. To meet the desired objectives, it may be imperative to enforce financial measures and incentives to encourage the removal of vehicles manufactured with outdated technology from the current traffic fleet. This may promote a transition toward more efficient motor vehicles. Furthermore, increasing the number of absorption zones can reduce the country's emissions by capturing and storing them. Based on Ministry statistics, approximately 9% of the country's CO\({}_{2}\) equivalent emissions were effectively captured and decreased in wooded regions, spanning around 23 million hectares in 2020. The sequestration patterns in 2020 exhibited a 3% rise compared with the data from 1990 [57; 60]. It may be advantageous to further increase this growth rate.
## 4 A Method for Determining the Degree of Controllability of State Variables  
Assume, without loss of generality, that the control \(u\) is a scalar, and the control matrix \(B\) is an \(n\times 1\) matrix.  
\[\mathbf{x}_{k+1}=\mathbf{F}\mathbf{x}_{k}+\mathbf{B}u_{k} \tag{13}\]  
The cost function has the following form  
\[J=\mathbf{x}_{0}^{T}\mathbf{P}_{0}\mathbf{x}_{0}=\mathbf{x}_{N}^{T}\mathbf{P}_{N}\mathbf{x}_{N}+\sum_{ k=1}^{N-1}\mathbf{x}_{k}^{T}\mathbf{Q}\mathbf{x}_{k}+\sum_{k=0}^{N-1}u_{k}^{T}Ru_{k} \tag{14}\]  
Since the value of \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\) in the estimation process of the dual system and \(\mathbf{P}_{k}\), \(\mathbf{K}_{k}\) in the regulation process of the original system are the same, we can use the degree of observability (12) of the dual system to determine the degree of controllability of the original system.  
The dual system for estimation problem has the following form  
\[\left\{\begin{array}{l}\mathbf{x}_{k-1}=\mathbf{F}^{T}\mathbf{x}_{k}+\mathbf{w}_{k}\\ \mathbf{z}_{k-1}=\mathbf{B}^{T}\mathbf{x}_{k-1}+\mathbf{v}_{k-1}\end{array}\right. \tag{15}\]  
The controllability matrix of the dual system has the form \(\mathbf{O}=\begin{bmatrix}\mathbf{B}^{T}\\ \mathbf{B}^{T}\mathbf{F}^{T}\\ \ldots\\ \mathbf{B}^{T}\left(\mathbf{F}^{n-1}\right)^{T}\end{bmatrix}\), and the equivalent measurement noise is  
\[\mathbf{R}^{*i}=E\left[\left(\mathbf{\varsigma}^{*i}\right)^{2}\right]=\left[\alpha_{ i1}^{2}+\alpha_{i2}^{2}+\cdots+\alpha_{in}^{2}\right]R^{1} \tag{16}\]  
where \(\alpha_{i1},\alpha_{i2},\ldots,\alpha_{in}\) are elements in the \(i\)th row of matrix \(\mathbf{O}^{-1}\).  
Thus the degree of observability (12) of the dual system will be  
\[DoO^{i}(dual)=\frac{P_{N}^{i}R^{1}}{P_{N}^{1}R^{*i}} \tag{17}\]  
where \(P_{N}^{1}\), \(R^{1}\) are initial conditions of the estimation process in dual system.  
Since the initial conditions of the dual system \(\mathbf{P}_{N}\), \(\mathbf{Q}\), \(R\) are the same, then the degree of controllability of the original system will be  
\[DoC^{i}(original)=DoO^{i}(dual)=\frac{P_{N}^{i}R^{1}}{P_{N}^{1}R^{*i}} \tag{18}\]  
where \(R^{1}\) is initial control-cost weighting matrix for control \(u\); \(R^{*i}\) is the equivalent control-cost weighting matrix of the \(i\)th component of the state vector and the value of it is the same as (17). As in Equation (17), the criterion (18) for the degree of controllability can also be used for any terminal time \(t_{N}\).  
The physical meaning of expression (18) may be explained as follows. If the projection of the control costs \(R^{*i}\) on the \(i\)th coordinate is less, the energy consumption will be less to control the \(i\)th state component. In other words, the \(i\)th state variable is easier to control. This idea was first mentioned in [1] to evaluate the controllability of the whole system.  
In summary, we proposed a new method for determining the degree of controllability of specific state variables using the duality theorem. This criterion allows for comparing the degree of controllability of state components in different systems.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & \(\theta_{hm}\) (\({}^{\circ}\)) & \(\theta_{vm}\) (\({}^{\circ}\)) & \(\theta_{hp}\) (\({}^{\circ}\)) & \(\theta_{pp}\) (\({}^{\circ}\)) & \(\theta_{hc}\) (\({}^{\circ}\)) & \(\theta_{pc}\) (\({}^{\circ}\)) \\ \hline \(F_{s2}\) & 0.182 & 88.502 & 0.188 & 88.424 & 0.397 & 84.186 \\ \(F_{r}\) & 3.578 & None & 3.686 & None & 4.858 & None \\ \(F_{s3}\) & 1.552 & 88.105 & 1.534 & 87.845 & 2.434 & 82.428 \\ \(F_{s1}\) & 1.784 & 86.238 & 1.779 & 86.173 & 2.379 & 89.327 \\ \(F_{c}\) & 1.874 & 88.386 & 1.824 & 88.693 & 1.467 & 85.486 \\ \(F_{i}\) & 2.836 & 86.332 & 2.868 & 86.546 & 3.542 & 88.248 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measurement results of various methods of \(\theta_{h}\) and \(\theta_{v}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & \(\epsilon_{hpm}\) (\%) & \(\epsilon_{hpm}\) (\%) & \(\epsilon_{hpm}\) (\%) & \(\epsilon_{eppm}\) (\%) & \(\epsilon_{vzm}\) (\%) & \(\epsilon_{vdm}\) (\%) \\ \hline \(F_{s2}\) & 0.587 & 1.709 & 0.781 & 0.728 & 0.839 & 1.119 \\ \(F_{r}\) & 0.111 & 3.548 & 6.208 & None & None & None \\ \(F_{s3}\) & 0.391 & 98.434 & 25.734 & 1.433 & 4.936 & 4.140 \\ \(F_{s1}\) & 0.682 & 66.647 & 4.432 & 0.166 & 1.326 & 0.746 \\ \(F_{c}\) & 0.239 & 2.466 & 3.978 & 0.523 & 13.240 & 4.617 \\ \(F_{i}\) & 0.405 & 10.280 & 5.601 & 0.964 & 1.371 & 1.927 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative errors of the period length measurements for the multi-period pattern piece. The following observations were derived from Tables 6 and 7. The proposed method for measuring \(\theta_{h}\) and \(\theta_{v}\) achieved a smaller relative error compared to manual measurements. Compared with the corner detection-based method, the proposed method had higher accuracy and more stable performance in angle measurement.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### Static Parameters  
Static problem parameters provide extra problem context for the agent. These are frequent when the same agent must solve different problem instances, as it needs to be aware of changes in available resources, number and types of jobs, processing times, etc.  
It is very common to include entity [34, 41, 160] and resource [93, 160, 211] properties, such as resource maximum capacity [33, 69, 85], job processing time [49, 57, 69, 78, 192], arrival rates [65] or due dates [66, 213].
###### Abstract  
The 3D resistivity imaging surveying is adept at identifying structural details at various subsurface depths, and it can acquire valuable underground information even in complex geological environments. This technique has been widely applied in fields such as geological exploration, hydrogeology, and environmental monitoring. Currently, the 3D electrical resistivity surveys in the field are primarily deployed using regular grid and equidistant electrode layouts. However, when the survey area is influenced by obstacles or when the target being investigated is particularly complex, this may hinder the deployment of the electrode array in accordance with a rectangular grid. In some instances, it may even be impossible to position the electrodes directly above the target of investigation. The only viable option is thus to place electrodes around obstacles or special targets. In view of this, this paper studied unconventional electrode arrays and inversion imaging techniques of 3D electrical resistivity surveying in confined areas. It presents the design of three electrode arrays: \"\(\square\)\"-shaped, \"U\"-shaped, and \"L\"-shaped. Furthermore, the study explores 3D resistivity finite element numerical simulation and inversion imaging techniques based on continuous electrical media. Finally, model experiments were conducted to verify the feasibility of these three special electrode arrays. We have applied the \"\(\square\)\"-shaped electrode array to investigate the foundation structure of an ancient pagoda in Zhejiang Province, achieving promising results. This study provides valuable reference for the research and application of special observation in 3D electrical resistivity exploration.  
restricted exploration area; unconventional electrode array; finite element; numerical simulation; least-squares inversion; 3D resistivity imaging surveying +
Footnote †: journal: Applied sciences  
Article
## 4 Results and Discussion
### Energy Losses during the Transition Process  
When the engine operates under VCD condition, each cylinder switches between the active cycle and the inactive cycle frequently. Energy losses during the deactivation and reactivation of the cylinders have a significant impact on the brake thermal efficiency of the engine. Figure 13 shows an example of the pressure-volume (PV) during one inactive cycle and one active cycle when the intake valves open at 180 CA and close at 420 CA. During the transition from the active cycle to the inactive cycle, energy losses are mainly consumed in two periods including gas exchange from exhaust stroke of the active cycle to intake stroke of the inactive cycle (Period I) and the two-stroke compression and expansion process of the inactive cycle (Period II). In the paper, the energy losses during the transition process are analyzed under the VCD operation of one active cycle followed by one inactive cycle (50% firing density).  
Figure 14 shows the energy losses of Period I and Period II with a different IVO and IVC. In the area below the green line, the large amount of exhaust gas remaining in the intake manifold leads to an insufficient fresh air charge of the active cycle, so there are no data in this area for Figure 14 and the following figures. This will be explained in Section 4.3.  
The energy losses of Period I (see Figure 14a,b) decrease with the delay of the IVC, because delaying the IVC causes a decrease in in-cylinder pressure later and the area of the pressure curve in Period I is reduced. In Period II, the cylinder goes through the compression stroke and expansion stroke. The pressure during the expansion stroke is less than that during the compression stroke due to heat transfer. The energy losses of Period II increase with the delay of IVC (see Figure 14c,d). This is due to the fact that the delaying IVC leads to more high temperature exhaust gas being sucked into the inactive cylinder, resulting in more heat transfer losses. Figure 14e,f show the total energy losses of Period I and Period II. It can be seen that delaying IVC is conductive to reducing  
Figure 13: The in-cylinder pressure of one inactive cycle and one active cycle.  
the total energy losses. Therefore, the intake valves should close later for the VCD operation from the perspective of energy losses.
## 3 Power Performance Assessment Methodology
### Preprocessing  
Based on the control principle of the wind turbine, SCADA data can be filtered using the following steps: (1) Filter out data for which power P = 0. (2) Eliminate data for which the wind speeds are less than \(v_{\text{in}}\) or greater than \(v_{\text{out}}\). (3) Filter out artificially limited electricity data by the control command, such as the limit value of the wind turbine power and generator power. (4) Determine the wind speed and power data in the MWPTR and RPOR according to \(v_{\text{in}}\), \(v_{\text{out}}\) and \(v_{\text{rated}}\). In other words, \(v_{\text{in}}<v<v_{\text{rated}}\) correspond to the MWPTR, while \(v_{\text{rated}}<v<v_{\text{out}}\) correspond to the RPOR.  
SCADA data including numerous abnormal data such as sensor abnormality and icing are identified using the density-based spatial clustering of applications with noise (DBSCAN) clustering method [29]. The DBSCAN clustering method, a typical clustering method based on density, can identify a cluster by setting a density threshold. This clustering algorithm has two key parameters--Eps and Minpts. Eps represents the radius of cluster, and Minpts is the number of neighbors within the cluster. With reference to [30,31], Minpts is set to 4 in this study, and Eps is calculated using the following equation:  
\[\text{Eps}=\left(\frac{V\times\text{MinPts}\times\gamma(0.5\times n+1)}{m\sqrt{ n^{n}}}\right)^{\frac{1}{n}} \tag{1}\]  
where \(m\) denotes the number of objects in the experimental data set, \(n\) is the dimensionality of the experimental space, \(\gamma(\cdot)\) is the factorial function, and \(V\) is the volume of the experimental space formed by \(m\) objects:  
\[V=\prod_{i=1}^{n}\{\max(\mathbf{x}_{i})-\min(\mathbf{x}_{i})\} \tag{2}\]  
where \(\max(\cdot)\) is the largest value function, \(\min(\cdot)\) is the smallest value function, \(\mathbf{x}_{i}\) is the \(i\)-th column data of the \(m\)-by-\(n\) experimental data matrix.
## 2 Materials and Methods
### Time-by-Time Inversion Algorithm
The corrections to the model parameters can be derived from the derivatives of the total losses to the parameters:  
\[\mathrm{g}(r)=\frac{\partial\mathrm{loss}_{t}}{\partial\mathrm{V}(r,t)} \tag{8}\]  
\[\mathrm{g}(r)=\sum_{0}^{t}\frac{\mathrm{loss}_{t}}{\partial\mathrm{u}(r,t)} \cdot\frac{\partial\mathrm{u}(r,t)}{\partial\mathrm{V}(r)} \tag{9}\]  
where the partial derivatives of the loss with respect to the wave field can be computed by Equation (4) and the backpropagation of the network is accomplished by passing the computation to the initial moment through the chain rule. After obtaining the parameter update quantity, the parameters are updated by Equation (10), which show the learning rate. \[\mathrm{V}_{n+1}(r)=\mathrm{V}_{n}(r)-\alpha\cdot\mathrm{g}(r) \tag{10}\]  
The gradient is propagated through the chain rule, so the velocity correction is related to \(\mathrm{d}_{t}\), which causes the residual. Through the two-dimensional finite difference acoustic wave equation in the time domain (5), it is known that \(\mathrm{d}_{t}\) is related to the velocity model and the wavefields of the previous two time steps. The spatial domain variations at different time steps are caused by the difference in the wavefields of the previous time step. Therefore, the range of velocity correction gradually expands with the range of the wavefield. Through the step-by-step correction algorithm in the time domain, the velocity model can be gradually corrected in the spatial domain, starting from the source and progressively correcting the velocity model in all directions. The gradual correction in the spatial domain makes the inversion parameters smaller, thereby reducing the non-uniqueness of the inversion problem. Moreover, a more accurate shallow velocity can improve the accuracy and stability of deep velocity corrections. The schematic diagram of the step-by-step inversion process is shown in Figure 6.