esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.  
urban rail transit; virtual coupling; full-length and short-turn routes; train diagrams; genetic-simulated annealing algorithm +
Footnote †: journal: Applied sciences
# Independent System Calibration of Sentinel-1B
## 6 In-Flight Antenna Model Verification  
The antenna model is a key element for accurate calibration and achieving high image quality as a result of the SAR data processing. Aside from the pointing determination described in the previous section, the antenna model is also used for (i) the generation of the reference antenna patterns for radiometric corrections in the SAR processor and (ii) the prediction of the relative gain between the sub-swaths of the IW and EW mode and the SM beams, respectively, i.e., referred to as beam-to-beam gain offset. The latter is quintessential for deriving one absolute calibration factor which is valid for all beams and SAR modes [4; 5].  
In the following, the verification of the antenna patterns is discussed by comparing the in-flight measured patterns with the theoretical patterns as predicted by the antenna model. Unlike for the pointing determination using notch-patterns, for antenna pattern verification, the nominal antenna beams and excitation coefficients are applied. The strategy presented here has already been successfully used for the satellites TerraSAR-X [17], TanDEM-X [18] and Sentinel-1A [3].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database ID** & **Measurement Date** & **Pass** & **Polarization** & **Mispointing [mdeg]** \\ \hline
2283/1 (A495) & 13 August 2016 & asc. & HH & \(-11\) \\
2283/2 (3D2F) & 13 August 2016 & asc. & HH & \(-11\) \\
2283/3 (F631) & 13 August 2016 & asc. & HH & \(-13\) \\
2283/4 (4932) & 13 August 2016 & asc. & HH & \(-21\) \\
2380/1 (5279) & 15 August 2016 & desc. & VV & \(-16\) \\
2380/2 (9 CBA) & 15 August 2016 & desc. & VV & \(-17\) \\
25D2/1 (A9C4) & 18 August 2016 & asc. & HH & \(-13\) \\
25D2/2 (EDOE) & 18 August 2016 & asc. & HH & \(-13\) \\
25D2/3 (50D5) & 18 August 2016 & asc. & HH & \(-13\) \\
25D2/4 (B0C9) & 18 August 2016 & asc. & HH & \(-19\) \\
26F5/2 (3311) & 20 August 2016 & asc. & VV & \(-18\) \\
26F5/3 (52 FF) & 20 August 2016 & asc. & VV & \(-16\) \\
26F5/4 (DF60) & 20 August 2016 & asc. & VV & \(-15\pm 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Antenna elevation mispointing values, i.e., differences between measured notch positions and antenna model prediction, derived from all elevation notch measurements acquired after the star tracker realignment.
# Synthesis and Application in Cell Imaging of
## 3 Results
### Subsection
#### 3.1.6 Investigation of Cellular Imaging Application  
We observed that both MeAcd12C and MedAcc12P appeared fluorescent in the mixed solvents (Figure S5). This indicates that these surfactant-like compounds would autoassembled to form AIEgens due to their amphiphilic structures. Both the AIEgens MeAcd12C and MedAcc12P could be examined by fluorescence microscopy (Figure S5c,d). The sizes of the MeAcd12C fluorogens were approximately 500 nm, and those of the MeAcd12P fluorogens were approximately 400 nm. The formation of fluorogens indicated that the TICT of the two compounds reduced under the mixed solvent conditions. Moreover, the major AIEgen sizes of MeAcd12C and MedAcc12P were 250 and 150 nm in H\({}_{2}\)O/THF  
Figure 4: Normalized cell viability of Hela and H184B5F5/M10 cells treated with various concentrations of MeAcd12C (**a**) and MeAcd12P (**b**). Compounds were prepared with concentration values of 0, 0.1, 0.3, 1.0, 3.0, 10.0, and 30.0 uM, respectively. After 24 h incubation, trypan blue staining was used to calculate cell viability. Each data represented three measurements with the calculated standard error.  
Figure 5: Fluorescent images of Hela and H184B5F5/M10 (H184) cells treated with 1 uM of MeAcd12C (**a**) and MeAcd12P (**b**) after 24 h of incubation. A: ex, 350; em; 425, long pass filter (lp). I: ex, 450; em; 515, long pass filter (lp). N: ex, 510; em; 590, long pass filter (lp).  
mixed solvents (H\({}_{2}\)O/THF = 50%:50%), respectively (Figure S6a,b). These indicated that fluorescence of MeAcd12C and MeAcd12P appeared due to the AIE formation.  
The primary optical spectra from MeAcd12C and MedAcd12P in H\({}_{2}\)O/THF mixed solvents were examined. Absorption spectra from the MeAcd12C fluorogens displayed similar patterns in the polar aortic solvent as well (Figure 6a). The volume fraction of 50% H\({}_{2}\)O exhibited the highest fluorescence emission (Figure 6b), thereby indicating that a substantial proportion could produce MeAcd12C fluorogens. Similarly, the absorbance of MedAcd12P fluorogens matched with its primary basic spectra, with the major absorbance peaks appearing near 450 nm (Figure 6c). The appropriate volume of H\({}_{2}\)O to produce MedAcd12P fluorogens was at a range of 25-50%, which yielded high fluorescence emission intensities (Figure 6d). The AIEgen spectra of MedAcd12C and MedAcd12P were similar to the spectra of their solid-state emission (Figure S7). We also found that fluorescence intensity of MeAcd12C reduced in the present of BSA and DNA (Figure S8). However, there is little fluorescence change when the MeAcd12P met BSA and DNA. The results implied that the increasing fluorescence of the MeAcd12C and MeAcd12P was irrelevant to the existence of BSA and DNA in cells.
## 2 Literature Review
### Related Work
#### 2.2.2 Textual Data Approaches
This model is efficient in identifying keywords associated with secondary crashes. The authors of ref. [41] conducted a study to measure the injury severity of pedestrian and bicyclist trespassing crashes at non-crossings. The authors proposed a two-stage statistical and machine learning hybrid approach to extract information regarding the severity of trespassers' injuries. This study focused on pedestrian trespassers and did not discuss the injury caused by all types of crashes. Moreover, thestudy focused on rail trespassing crashes. The authors of ref. [42] developed a classifier to discover missing classified work-zone crashes using the information in text narratives. The study used three years of crash narrative data (2017-2019). The classifier used key information from the crash textual data and proved to be efficient and effective in classifying road crashes. The authors of ref. [43] conducted a study on NLP's effectiveness in analyzing mine accident narratives. The authors developed nine models to classify mine safety narratives into nine accident types. The automatic classification process achieved an accuracy of 96%. The study demonstrated that NLP and text analysis could be powerful tools for analyzing textual data. The authors of ref. [44] carried out a study to inspect road safety through applying Latent Dirichlet Allocation (LDA). The purpose of the study was to identify the safety issues on main roads. The paper showed how topic modeling can identify run-off-road crashes through highlighting the associated patterns embedded in the narratives. Topic modeling was able to divide the topics into two groups of problems and solutions. The study gathered crash reports over six years, reflecting 4011 km of Irish roads. The findings showed the importance of keywords related to \"forgiving roadside\" and \"clear zone\" topics. Moreover, the authors found that categories related to road safety were higher in the problem reports, which means that problems are discovered more easily than solution interventions. The study showed that the topic modeling methodology is appropriate to identify specific patterns related to traffic safety and road crashes. However, their approach was based on a bag-of-words representation, which does not consider semantic correlations between the words. Also, their study could be improved by considering other types of crashes, such as multi-vehicle crashes.
## 3 Proposed Architecture  
This section presents the architecture of our proposed approach for coordination and management of VANETs resources. The information acquired by many vehicles, not only by an individual vehicle, increases the accuracy; thus, better decisions and predictions can be made. However, there is a major problem in gathering, processing, and analyzing the enormous amount of data generated while keeping the network cost at a minimum. Managing the resources of the network while providing the application's requirements is yet another challenge. Our proposed approach addresses these issues. The proposed approach considers a layered cloud-fog-edge SDN architecture that is coordinated by a fuzzy system implemented in the SDN Controller (SDNC) and SDN modules. This architecture is illustrated in Figure 2.  
SDNC manages the resources of the edge, fog, and cloud layer and determines the appropriate layer for data storage and computing, based on the output of the fuzzy system. The edge layer includes the resources of all On-Board Units (OBUs) of the smart vehicles that are able to communicate with each other. The vehicles act not only as a relay node, but they also process and analyze the data by themselves and at the same time share their available resources with some vehicle that has a shortage of resources (hereinafter will be referred to as _the vehicle_). The fog layer consists of the RSUs, RSU Controllers (RSUCs), Base Stations (BSs), and fog servers, which are ideally only a few hops away from the vehicles. It offers more resource capabilities, compared to the edge layer, while still providing computing in real-time. Whereas the cloud layer offers abundant storage and computing facilities, located far away at the cloud data centers.  
We consider this architecture from a bottom-up approach, which implies that the edge layer is the first layer considered, based on the available connections and service requirements. If the application requirements are not fulfilled or there are no or only very few available connections, then the fog layer is the layer taken into consideration. For instance, safety applications are suitable for either edge layer or fog layer, as both these layers support real-time processing and high QoS. The cloud layer is used to process applications that are delay tolerant and require long-term analytics. Through this approach, all network resources are utilized effectively and massive traffic flow in the core network is avoided.  
IFS-CMR is implemented in the SDNC and the vehicles which are equipped with an SDN module. In case a vehicle does not have an SDN module, it sends the information to SDNC which computes the needed information and sends back its decision.  
In the next subsections, we give details of the composition of the proposed approach and describe the input and output parameters of each subsystem. In addition, we present the data gathering and communication module which is integrated into the vehicles and explain the FL Controllers (FLCs) of the system.
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 3 Overall Design of Tooth Rail Wind Shaft Inspection Robot
### Design of Lifting Module
#### 3.2.1 Selection and Calculation of Hoisting Wire Rope  
The <Coal Mine Safety Regulations> stipulate that the safety factor of steel wire rope used for a single-rope winding hoisting device for lifting materials shall not be less than 6.5 [26]. In order to facilitate the selection and calculation of lifting wire rope, the lifting module is simplified, as shown in Figure 7. Let \(h_{0}\) be the distance between the detection module and the guide wheel in the lifting module, m; \(H_{0}\) is the maximum hanging length of the tail rope, m; and \(H_{\text{C}}\) is the maximum hanging length of lifting rope, m.  
When the vertical shaft is lifted, total load on lifting wire rope:  
\[Q_{d}=Q_{t}+Q_{c} \tag{1}\]  
where \(Q_{d}\) represents the total load of the lifting container, kg; \(Q_{t}\) represents the load capacity of the lifting container, kg; and \(Q_{c}\) represents the self-weight of the lifting container, kg. The calculation formula of unit weight (\(q_{t}\)) of the wire rope is  
\[q_{t}=\frac{Q_{d}}{\frac{1.1\sigma_{B}}{m_{a}}-H_{c}} \tag{2}\]  
In the above formula, \(\sigma_{B}\) represents the nominal tensile strength of steel wire rope; \(m_{a}\) represents the static safety factor of steel wire rope; In this paper, \(\sigma_{B}\) = 157 kg/mm\({}^{2}\) and \(m_{a}\) = 6.5 are selected for calculation. Te known solution conditions include: the depth of the wind shaft is about 500 m, the weight of the lifting container is about 50 kg, the load of the lifting container is about 50 kg and the maximum hanging length of wire rope is 500 m. According to the calculation of Equations (1) and (2),  
\[q_{t}=0.046\text{kg/m}\]  
According to the selection standard of steel wire rope, we can see from the parameter table of steel wire rope that it is more suitable to choose \(18\times 7\) multi-strand steel wire rope with a diameter of 12 mm, a mass of 0.0619 kg/m per unit length, a tensile strength of 1570 MPa and a breaking force of 72.4 kN.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Variation in Runoff in the GRB
#### 4.4.2 Spatial and Temporal Variation of Runoff  
Figure 3c shows the spatial distribution of the annual average runoff at small basin scales in the GRB from 2002 to 2018. The annual average runoff in the GRB ranges from 500 mm to 1100 mm, with an average value of 844 mm. The spatial patterns of runoff are similar to precipitation that gradually increases from the southwest to northeast, and there is a low-value region of runoff in the northwest and a high-value region in the south.  
Figure 6: Comparison of flow duration curves of simulated runoff using the SWAT model against observed runoff at Waizhou station: (**a**) calibration; (**b**) validation.  
Figure 7: Scatter plot of simulated and measured monthly runoff of (**a**) Fenkeng station, (**b**) Hanlinqiao station, (**c**) Xiashan station, and (**d**) Junmening station in 2012 and 2015.  
Annual and monthly mean runoff variations in the 172 sub-basins from 2002 to 2018 are presented in Figure 4e,f. The time series of annual mean runoff has good consistencies with precipitation during the study period. The annual runoff of different sub-basins is not completely consistent. However, there is a similar variation rule in 2010 and 2011, with the peak value in 2010 and the valley value in 2011. Precipitation variations from 2010 to 2011 (Figure 4a,b) may cause the runoff changes during the same period. Figure 4f shows the change in monthly average runoff in 172 sub-basins from 2002 to 2018. Overall, it shows an upward trend in the first half of the year, reaches a peak in June, and shows a downward trend in the second half of the year. However, the monthly average runoff in the different sub-basins is quite different, and the difference can reach 100 mm. It should be noted that the seasonal runoff is similar to seasonal precipitation.
[28] used the deep neural network (DNN) to predict hepatitis B surface level antigen levels against statistical techniques. The study demonstrated that DNN offered better performance than statistical methods in predicting clinical outcomes. For a time-series forecasting of hepatitis B incidence, [PERSON] et al. [29] applied three deep learning algorithms based on Long Short-Term Memory (LSTM) prediction model, Recurrent Neural Network (RNN) prediction model, and Back Propagation Neural Network (BPNN). The study concluded that deep learning techniques offered a significant predictive forecast for hepatitis incidences and could assist decision makers in disease control and management. However, to the best of our knowledge, the use of model interpretability in HPV detection studies is still limited. Several ML studies in the literature did not consider model explainability; hence, the model outcomes cannot be adequately understood by medical practitioners and health policymakers. Meanwhile, interpreting ML models to understand the features that cause HBV infection could be beneficial in real-world applications and for stakeholders in the health sector. Consequently, the main contributions of this study include 1. building and optimizing ML algorithms toward effective HBV detection and diagnosis, 2. Introducing model explainability using SHapley Additive exPlanations (SHAP), a game-based theoretical approach aimed at explaining and visualizing the predictions of the ML models, 3. interpreting and ranking the HPV causative features. In essence, this study seeks to understand why a machine learning model makes a prediction and what features contribute to the decision. The machine learning algorithms used to develop HPV detection models include decision trees, logistic regression, support vector machine (SVM), random forest, XGBoost, and AdaBoost. The remainder of the manuscript is organized as follows. Section 2 describes the proposed methods for data collection, pre-processing, and models used. Section 3 presents some experimental results. Section 4 presents the model interpretation. In Section 5, the discussion of this paper is presented. This study concludes in Section 6.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 2 Materials and Methods
### Controller Design
#### 2.3.2 The Dahlin Controller for the Single Tank Model  
When the process that is being controlled has the first-order transfer function,  
\[G(s)=\frac{K}{T_{I}s+1}e^{-\tau_{\rm S}}, \tag{15}\]  
The following dependence between \(\tau\) and \(NT\) is applicable:  
\[\tau=NT+(1-m)T,\;0<m\leq 1, \tag{16}\]  
where \(m\) is the parameter of the modified Z transformation.  
The Dahlin controller has the Z\(-\)transform [27; 30; 31],  
\[D(z)=\frac{(1-e^{-\lambda T})\Big{[}1-(e^{-T/T_{1}})z^{-1}\Big{]}}{K[1-e^{- \lambda T}z^{-1}-(1-e^{-\lambda T})z^{-N-1}](C_{1}+C_{2}z^{-1})}, \tag{17}\]  
where  
\[C_{1}=1-e^{-m\tau/T_{1}},\;C_{2}=e^{-m\tau/T_{1}}-e^{-T/T_{1}}. \tag{18}\]  
Occasionally, digital controllers provide a control signal that oscillates with a decreasing amplitude in regard to the stationary level. This is known as \"ringing\" and is caused by a negative, real pole that is close to the \"\(-1\)\" point in the \(z\)-plane and the complex conjugate poles within the unit circle. A quick way to avoid ringing, as the authors proposed, is to include \(z=1\) only in the factor \((C_{1}+C_{2}z^{-1})\). In this way, the modified Dahlin controller, which should eliminate the effect of sudden changes in the control signal, has the following form:  
\[D(z)=\frac{(1-e^{-\lambda T})\Big{[}1-(e^{-T/T_{1}})z^{-1}\Big{]}}{K[1-e^{- \lambda T}z^{-1}-(1-e^{-\lambda T})z^{-N-1}](C_{1}+C_{2})}. \tag{19}\]  
The control law for non-ringing control signals can also be written as the control law of a digital PI regulator. The transfer function of the Dahlin PI controller is [30]:  
\[D(z)=K_{P}\bigg{[}1+\frac{T/T_{I}}{1-z^{-1}}\bigg{]}, \tag{20}\]  
\[K_{P}=\frac{1-e^{-\lambda T}}{K(e^{T/T_{1}}-1)[1+N(1-e^{-\lambda T})]},\;T/T_{ I}=e^{-T/T_{1}}-1. \tag{21}\]
## 2 SPME-GC/MS Methods
### Higher Alcohols and Esters in Wine  
By using a PDMS 100-\(\upmu\)m fiber, effective methods for analysis of higher alcohols and aliphatic esters in wine were performed [82; 83]. This coating fiber showed high affinity for non-polar compounds such as ethyl esters and acetates [84; 85; 86], while CW/DVB fiber is suitable for more polar compounds, such as 1-hexanol, hexen-1-ol, 1-octanol, and monoterpenschs [84].  
[PERSON] _et al._, developed a SPME and GC/MS-SIM method which provided simultaneous determination of 32 esters in wine in concentration between ng/L and mg/L [87]. Seven different fibers were tested: DVB/CAR/PDMS 50/30 \(\upmu\)m, CAR/PDMS 85 \(\upmu\)m, PDMS 100 \(\upmu\)m, PDMS/DVB 65 \(\upmu\)m, PA 85 \(\upmu\)m, CW/DVB 70 \(\upmu\)m, and polyethyleneglycol (PEG) 60 \(\upmu\)m. PDMS was the most efficient in extracting the less polar and less volatile compounds; for more volatile esters the best coating was CAR/PDMS, and aromatic esters were better recovered by CW/DVB. In general, the PDMS fiber showed high efficiency for all compounds and provided LOQs between 0.4 ng/L and 4.0 \(\upmu\)g/L.  
Recent applications showed that the tri-phase fiber DVB/CAR/PDMS provides extraction of the highest number of wine volatiles, including ethyl esters (56% of the compounds identified), alcohols, and acids [2; 88].
## Abbreviations  
**AC** articular cartilage, **OA** osteoarthritis, **X-ray** radiography, **MRI** magnetic resonance imaging, **CT-arthrography** computed tomography with arthrography, **MRE** magnetic resonance elastography, **dGEMRIC** delayed gadolinium-enhanced MRI of cartilage, **VAG** vibroarthrography, **OCT** optical coherence tomography, **ECM** extracellular matrix, **COL** collagen, **ACAN** aggrecan, **BMSCs** mesenchymal stem cells, **COLZA1** alpha-1 type collagen gene, **Runx2** runt-related transcription factor 2, **PRG4** proteoglycan-4, **Sox9** SRY-Box transcription factor, **TGF-\(\mathbf{\upbeta}\)** transforming growth factor-beta, **BMPs** bone morphogenetic proteins, **MAPK** mitogen-activated protein kinase, **H\(\mathbf{{}_{A}}\)** aggregate modulus, **E\(\mathbf{{}_{C}}\)** compressive Young's modulus, **E\(\mathbf{{}_{t}}\)** tensile Young's modulus, **\(\mathbf{{}_{u}}\)** Poisson's ratio, **\(\mathbf{{}_{k}}\)** hydraulic permeability, **E\(\mathbf{{}_{eq}}\)** equilibrium tensile modulus, **G\(\mathbf{{}_{eq}}\)** equilibrium shear modulus, **\(\mathbf{{}_{8}}\) shear loss angle, **GAG** glycosaminoglycans, **SF** synovial fluid, **PSB** phosphate buffer medium, **SM** synovial membrane, **SAPL** surfactant phospholipids, **HA** hyalurone, **MCR** Raman curve resolution, **AI** Artificial Intelligence, **FEA** finite element analysis, **DSC** Dice Similarity Coefficient, **AUC** Area Under the Curve, **MLP** Multilayer Perceptron, **RBF** Radial Basis Function Network, **NCA** neighbourhood component analysis, CCK open kinematic chain, **CKC** closed kinematic chain.