esa-sceva/satcom-chunk-collection huggingface dataset:
# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 5 Medical Consultation Flows
### Selecting an Already Defined Case Type
```
1:CT\({}^{M}\leftarrow\) selectSimilarCT0
2:applyCaseType(P,CT\({}^{M}\)) is called
3:Physician M define CT\({}^{M}\) by modifying the selected CT\({}^{M}\)
4:personalInfo \(\leftarrow\) getUqptUnits(CT\({}^{M}\))
5:P.personalData.add(personalInfo)
6:CT\({}^{M}\leftarrow\) removeUqptUnits(CT\({}^{M}\)) units marked with \(uqpt\) are removed
7:mr\({}_{t}^{p}\leftarrow\) T\({}^{*}\)(CT\({}^{M}\))
8:MR\({}^{p}\leftarrow\) MR\({}^{p}\)\(\bigcup\) {mr\({}_{t}^{p}\)}
9:chronElements \(\leftarrow\) getAllActiveChonicElementsInludedIn(CT\({}^{M}\))
10:P.chronicElems.add(chronElements)
11:ifisChronic(CT\({}^{M}\)) then
12:P.chronicCaseTypes.add(CT\({}^{M}\).name)
13:endif
14:CT\({}^{M}\leftarrow\) removeUqcnUnits(CT\({}^{M}\)) units marked with \(uqcn\) are removed
15:ifCT\({}^{M}\) is saved as an improvement then
16:CT\({}^{M}\leftarrow\) CT\({}^{M}\)
17:CRB\({}^{M}\) is updated with the new version of CT\({}^{M}\)
18:else
19:CT\({}^{M}_{new}\leftarrow\) CT\({}^{M}\) is saved as a new case type
20:CRB\({}^{M}\leftarrow\) CRB\({}^{M}\bigcup\) {CT\({}^{M}_{new}\)} the base is incremented
21:endif
```
**Algorithm 5** Usual attention flow of a patient \(P\)

### New Case Type Flow

Algorithm 6 details the flow followed by the physician when they need to address a new case type that is not included in their \(CKB\). Since there is no case type to be re-used, Algorithm 6 needs to create an empty case type in which the new workday scenario can be detailed (lines 1-2). To define a new case type \(CT^{M}_{new}\), the physician can re-use any predefined unit of thought, and can also create units of thought specifying new clinical phrases. Furthermore, predefined conceptual elements can be re-used and new elements can be created (lines 3-4). Each element defined by the physician is referenced from one clinical section. Therefore, new conceptual components are created in order to group elements sharing the same section type (lines 5-11). It is mandatory that the physician assigns a name to the new clinical case type. The case type can also be marked as chronic, and in that case, the physician needs to specify the chronic attribute of each new element, created while defining the new case type (lines 12-21). All units of thought marked as unique to the patient are stored as personal data, and are removed from \(CT_{new}^{M}\) (lines 22-24). Then, \(T^{*}\) transformation is applied to generate a new medical record in the patient's history (lines 25-26). All chronic conceptual elements of \(CT_{new}^{M}\) are associated with the patient, and if the case type is chronic it is associated as permanent patient data (lines 27-31). Finally, all units of thought marked as unique to current consultation are removed from the case type, and the clinical data base of the physician is enriched by including the new case type.
## 4 Results
### Spatiotemporal Characteristics of the Land Use Conflict  
#### 4.2.1 Changes in the Structure of the Land Use Conflict  
The land use conflict index (LUCI) is composed of the CI, FI, and SI. Between 1990 and 2018, the FI and LUCI exhibited S-shaped curves of different forms, while the CI and SI presented relatively stable patterns, except in the core zone (Figure 6). From 2000 to 2010, the CI and SI in the core zone experienced significant changes, which is consistent with findings from previous studies [49; 50]. The normalized value of CI decreased from 0.4415 to 0.27, while the SI increased from 0.7703 to 0.8742. In the fringe and suburban zones, minor fluctuations of the CI and SI were most noticeable during the study period. The FI and LUCI continued to rise from 1990 to 2010 but began to fall and stabilize from 2010 to 2018.  
Figure 5: Polynomial regression of human development activities intensity and the USAI.  
In our research, the LUCI is divided into four types using the inverted \"U\" classification method, i.e., mild conflicts [0, 0.35], moderate conflicts [0.35, 0.7], intense conflicts [0.7, 0.9], and severe conflicts [0.9, 1]. Our results indicate that the intensity of the spatial conflict in the Su-Xi-Chang region shows an overall upward trend. However, it is still within the basic controllable range (Table 5). The mean values of LUCI in 1990, 2000, 2010, and 2018 were 0.269, 0318, 0.407, and 0.418, respectively. Although the proportion of mild conflicts showed a decreasing trend, it remained a dominant proportion with values between 56.37% and 81.24% during the study period. The proportion of intense and severe conflicts demonstrated an overall upward trend. Likewise, the number of severe conflicts units increased from 23 in 1990 to 68 in 2018, indicating that the land use conflict in the Su-Xi-Chang region gradually intensified.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Degree**} & \multirow{2}{*}{**Classification**} & \multicolumn{4}{c}{**Unit Number**} & \multicolumn{4}{c}{**Proportion/\%**} \\ \cline{3-10}  & & **1990** & **2000** & **2010** & **2018** & **1990** & **2000** & **2010** & **2018** \\ \hline Mild & 0.0–0.35 & 2417 & 2165 & 1757 & 1677 & 81.24 & 72.77 & 59.06 & 56.37 \\ Moderate & 0.35–0.7 & 434 & 591 & 737 & 794 & 14.59 & 19.87 & 24.77 & 26.69 \\ Intense & 0.7–0.9 & 101 & 179 & 420 & 438 & 3.39 & 6.02 & 14.12 & 14.72 \\ Severe & 0.9–1.0 & 23 & 40 & 61 & 68 & 0.77 & 1.34 & 2.05 & 2.29 \\ Total & & 2975 & 2975 & 2975 & 2975 & 100 & 100 & 100 & 100 \\ Average & & 0.269 & 0.318 & 0.407 & 0.418 & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Types of land use conflict from 1990 to 2018.  
Figure 6: The curve of the (**a**) Complexity index, (**b**) Fragility index, (**c**) Stability index, and (**d**) Land use conflict index.
## 4 Simulation Results and Discussion
### Presentation and Discussion of Results
As observed, in higher values of DR participation (i.e., more than 60%) the expected cost reduced slightly because in higher DR participants, new peaks of demand may occur (also known as rebound peak effect) and expensive units may need to be committed. Also, it is seen that in all the rates of DR participants, the expected cost in CPP program has lower value compared with other TBR-DR programs.
## 4 Implemented MCDM Methods
### EDAS Method  
The EDAS (Evaluation Based on Distance from Average Solution) [45,59,60,61] method is similar to the TOPSIS method. In the TOPSIS method, the desirable alternative has a lower distance from the ideal solution and a higher distance from the nadir solution. In the EDAS method, the best alternative is related to the distance from the average solution [45]. In this method, we have two measures dealing with the desirability of the alternatives. The first measure is the positive distance from the average (_PD_), and the second is the negative distance from the average (_ND_). The evaluation of the alternatives is made according to higher values of _PD_ and lower values of _ND_. The steps for using the EDAS method are presented as follows:  
Step 1: Construct the decisions matrix (\(R\)):  
\[R=\|r_{ij}\|, \tag{3}\]  
Create the criterion statistics (experimental criterion values) and criteria weights vector [46]:  
\[\Omega=(\omega_{j}), \tag{4}\]  
where \(i=1\), \(2\),..., \(n\); \(j=1\), \(2\),..., \(m\); \(m\) is the number of criteria; and \(n\) is the number of compared options [20].  
Step 2: Calculate the average of all criteria:  
\[AV_{j}=\sum_{i=1}^{n}r_{ij}/n, \tag{5}\]Step 3: Calculate the positive distance from the average (_PD_) and the negative distance from the average (_ND_):  
\[PD_{ij}=\frac{\max\bigl{(}0,\bigl{(}r_{ij}-AV_{j}\bigr{)}\bigr{)}}{AV_{j}}, \tag{6}\]  
\[ND_{ij}=\frac{\max\bigl{(}0,\bigl{(}AV_{j}-r_{ij}\bigr{)}\bigr{)}}{AV_{j}}, \tag{7}\]  
where the \(j\)-th criterion is maximized (beneficial), and:  
\[PD_{ij}=\frac{\max\bigl{(}0,\bigl{(}AV_{j}-r_{ij}\bigr{)}\bigr{)}}{AV_{j}}, \tag{8}\]  
\[ND_{ij}=\frac{\max\bigl{(}0,\bigl{(}r_{ij}-AV_{j}\bigr{)}\bigr{)}}{AV_{j}}, \tag{9}\]  
where the \(j\)-th criterion is minimized (non-beneficial), and \(PD_{ij}\) and \(ND_{ij}\) denote the positive and negative distance of the \(i\)-th alternative from the average solution in terms of \(j\)-th criterion, respectively.  
Step 4: Determine the weighted sum of _PD_ and _ND_ for all alternatives:  
\[SP_{i}=\sum_{j=1}^{m}w_{j}PD_{ij}, \tag{10}\]  
\[SN_{i}=\sum_{j=1}^{m}w_{j}ND_{ij}, \tag{11}\]  
where \(\omega_{j}\) is the weight of \(j\)-th criterion.  
Step 5: Normalize the values of _SP_ and _SN_ for all alternatives:  
\[NSP_{i}=\frac{SP_{i}}{\max_{i}SP_{i}}, \tag{12}\]  
\[NSN_{i}=1-\frac{SN_{i}}{\max_{i}SN_{i}}, \tag{13}\]  
Step 6: Calculate the appraisal score (_AS_) for all alternatives:  
\[AS_{i}=\frac{1}{2}(NSP_{i}+NSN_{i}), \tag{14}\]  
where \(0\leq AS_{i}\leq 1\).
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 2 Description of the Project Site  
### Site Location and Details of Pavement Structure  
The project site is located in the southern part of Texas and consists of a six-lane highway (three main lanes on the eastbound and westbound sides, respectively). The subject section is a part of the main lanes on the west side of the bridge on the westbound highway and extends from the start of the bridge approach slab to 600 ft (183 m) west near the exit to a frontage road (Station 1026+92 to Station 1020+92), as shown in Figure 1. The three main lanes on the westbound side are supported on the MSE wall, and those on eastbound side are supported on an embankment with a 4:1 slope (see Figure 2). The subject section showed significant distresses at the time of this study, whereas the pavement structures on the eastbound and east side of the bridge on the westbound highway showed no major distresses.  
The subject section was constructed in 1997 and has been in service ever since. The plan set drawings show that the pavement structure consists of 12 in (30.5 cm) of continuously reinforced concrete pavement (CRCP), 1 in (2.5 cm) of asphalt stabilized base (ASB), 6 in (15.2 cm) of Portland cement-treated base (PCTB), and 6 in (15.2 cm) of lime-treated subgrade (LTS). Figure 2 shows the typical section of the pavement structure presented in the plan set drawings.  
Figure 1: Overview of the project site and subject section.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 3 Results
### Harmonic Distortions over Time  
After considering the THD and the contributions of individual harmonics, we now extend the analysis to include variations in time. Figure 8 shows the monthly statistics (median, 1 to 99, 10 to 90, and 25 to 75 percentile ranges) for the THD as well as third, fifth, and seventh harmonic for a single phase on all three sites. We find the following.  
1. For Sites 1 and 2, non-zero THD values are present during the entire measurement period from 2015 to 2018. For site 3, only 16 out 24 months in 2015 and 2016 and 18 out of 24 months between 2017 and 2018 record THD values above the compression threshold;
2. For all sites, THD appears to follow a seasonal pattern. For Site 1 and Site 2, median THD is about 50% higher in summer and autumn than during the winter and  
Figure 7: Total average contribution to THD over the period of one month per phase (columns) and site (rows). For each cycle, we calculate THD as well as each harmonics’ contribution to THD. We then average over all samples of January 2017.  
spring. For Site 3, the difference is more pronounced due to many periods without observed THD. For 2015 and 2016, non-zero THD values are recorded only in the summer months;
3. The spread (difference between the 1 and 99 percentile) of observed THD values (binned monthly) decreases with voltage level. Aggregating across months, the maximum spreads are 1.71, 1.45, and 1.00% for Sites 1, 2, and 3, respectively. In the same order, the average spreads are 0.73, 0.67, and 0.27%. Independent of voltage level, larger spreads always occur in the summer and authumns months;
4. For Site 1 (and phase 1), the contribution of the third, fifth, and seventh harmonics to THD over a period of 48 months is in-line with the results for a single month (cf. Figure 7). Over time, the majority of THD is accounted for by the 5 th and 7 th harmonics. For most months, both harmonics track similar medians (and spreads), except in the spring and autumn of 2017. Over these periods, the 5 th harmonic follows a seasonal pattern (lower during winter/spring, larger during summer/autumn) while the 7 th harmonic keeps an almost constant median. Their combined contribution leads to the deviation from seasonality earlier observed in THD;
5. For Sites 2 and 3, the contribution of individual harmonics to THD is more complicated. For Site 2, considering only January 2017 suggests that the 3 rd and 7 th harmonic should contribute most to THD. However, over time, we observe a different pattern. Here, the 7 th harmonic appears to set a baseline of distortion (with slight seasonality), the 5 th harmonic modulates additional (stronger) seasonality in the median as well as additional noise (larger spread), and the 3 rd harmonic adds even more noise (larger spread). This shows that the analysis of a single month is insufficient and unlikely to be representative of THD and harmonic contributions over longer time frames.
# Numerical Modelling of the Multiphase Flow in an Agricultural Hollow Cone Nozzle
## 2 Research Object and Methodology
Under-relaxation factors for pressure and momentum were set at 0.5. The calculations were performed with a time step size of \(10^{-5}\) s with 20 iterations per time step. In this study, the VOF method was selected, as it enables a detailed representation of the formation and outflow of a liquid jet under two-phase water-air flow conditions. This method allows for accurate tracking of the phase interface by computing the volume fraction of each phase within individual mesh cells. Such capability is essential for precisely capturing the phenomena occurring during the initial stages of spraying, including the formation of the conical liquid jet, its acceleration, and the early phases of breakup. An alternative approach would be to use the DPM, which is better suited for analyzing dispersed droplets moving through a continuous medium. However, the DPM requires prior specification of droplet size distribution, injection location, and velocity, which limits its applicability in cases where the goal is to model the continuous formation of the liquid jet and its dynamic interaction with the surrounding air. The choice of the VOF method is, therefore, justified both by the nature of the investigated phenomenon and by the need to obtain detailed information about flow parameters across the entire nozzle geometry--without the need to impose additional assumptions regarding initial droplet characteristics. This approach enables a more accurate representation of real operating conditions and provides a solid foundation for further analysis of jet breakup and droplet dispersion in an extended spatial domain.
## 3 Results  
### Test Set Comparison  
The test set data were fed into the model to obtain predicted Ne values, which were then compared against the ground-truth Ne labels. Figure 5 illustrates the comparative  
Figure 4: Model input–output workflow diagram. Schematic diagram illustrating the workflow of the Multilayer Perceptron (MLP) model used for ionospheric vertical decomposition. Inputs to the model include the following: (1) Spatiotemporal data (e.g., geographic coordinates, time). (2) 2D Total Electron Content (TEC) maps derived from IGS data, representing the macroscale ionospheric state. (3) Space environment parameters, specifically geomagnetic indices (AE, SYM-H) and the solar activity index (F10.7), capturing external driving factors. The MLP processes these combined features to predict the primary output: a vertically layered electron density (Ne) profile covering the 60–800 km altitude range.  
results. In Figure 5a, the 2019 full-year dataset (8760 hourly samples) is analyzed using altitude-stratified metrics, calculated independently for each altitude layer. The evaluation metrics are defined as follows:  
\[RMSE=\sqrt{\frac{1}{n}\frac{\overline{\textit{\emph{i}}}}{\overline{\textit{ \emph{i}}}}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{2}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y_{i}})^{2}} \tag{3}\]  
where \(y_{i}\) denotes the predicted Ne, and \(\hat{y_{i}}\) represents the ground-truth Ne. Notably, the altitude-stratified metrics exhibit a superior performance at lower and higher altitudes compared to intermediate layers. This discrepancy arises from the heightened spatiotemporal variability of electron density in the primary ionospheric regions, where nonlinear interactions between solar radiation intensity, neutral wind fields, and electric field coupling amplify modeling complexity. These intermediate altitudes correspond primarily to the ionospheric F-region, where the electron density is largely controlled by complex production, loss, and transport processes highly sensitive to these coupled drivers.  
Figure 5b compares the global electron density distributions at specific altitudes (300 km, 500 km, 700 km) for selected time instances.
## 5 Numerical Example  
In this numerical example, we assume that the finite production horizon equals to \(H=12\cdot\Delta_{\text{t}}\) with period length \(\Delta_{\text{t}}=1\). The index of service level is \(\theta=90\%\). The demand is random characterized by the means that are given in Table 1 and the standard deviation is fixed for all production periods and equals to \(\sigma_{d}=100\). The initial inventory level \(w_{m}(k=0)=w_{r}(k=0)=0\).  
* Lower and upper boundaries of manufacturing and remanufacturing capacities: \(P_{mmin}=1500\); \(P_{mMax}=2300\); \(P_{min}=50\); \(P_{rMax}=150\);
* Assuming the degradation law of the manufacturing unit is characterized by the Weibull distribution with two parameters shape \(\eta=2\) and scale \(\beta=100\).  
The other data of the problem are presented as following  
Cost coefficients: \(C_{hm}=20\); \(C_{thr}=25\); \(C_{pm}=30\); \(C_{pr}=25\); \(C_{ps}=35\); \(C_{c}=1250\);  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Periods** & **Demands** & **Manufacturing** & **Remanufacturing** & \(\mathbf{\kappa_{k}}\) & **Subcontractor** & \(\mathbf{w_{m}}\) & \(\mathbf{w_{r}}\) \\ \hline
1 & 1961 & 1491 & 0 & 1 & 639 & 169 & 0 \\
2 & 1833 & 1579 & 0 & 1 & 677 & 593 & 0 \\
3 & 1975 & 1496 & 101 & 1 & 374 & 589 & 683 \\
4 & 1984 & 1861 & 54 & 0 & 15 & 584 & 565 \\
5 & 2024 & 1725 & 11 & 0 & 103 & 399 & 846 \\
6 & 2049 & 1834 & 19 & 0 & 32 & 235 & 795 \\
7 & 2081 & 1376 & 120 & 1 & 590 & 240 & 1080 \\
8 & 1962 & 1258 & 137 & 1 & 839 & 512 & 942 \\
9 & 2048 & 1551 & 137 & 1 & 665 & 817 & 1013 \\
10 & 1775 & 588 & 96 & 1 & 1372 & 1097 & 1898 \\
11 & 1909 & 357 & 78 & 1 & 1430 & 1076 & 2208 \\
12 & 2042 & 371 & 123 & 1 & 1485 & 1013 & 2617 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Production plan during \(H=12\) periods.  
Carbon coefficients: \(\xi\) = 9.3; \(Q_{cm}\) = 118,000; \(Q_{cr}\) = 2000; \(Q_{1m}\) = 0.6 \(\times\)\(Q_{cm}\); \(Q_{2m}\) = 0.4 \(\times\)\(Q_{cm}\); \(Q_{1r}\) = 0.8 \(\times\)\(Q_{cr}\); \(Q_{2r}\) = 0.4 \(\times\)\(Q_{cr}\); \(\rho\) = 2;  
For the maintenance strategy, the failure rate of manufacturing unit follows the Weibull distribution with factors shape \(\gamma\) = 2 and scale \(\beta\) = 100 and the unit costs of preventive and corrective maintenance actions are respectively \(M_{p}\) = 500 and \(M_{c}\) = 2000.
## 3 Results
### The Influence of Relative Azimuth Angle on the Prediction Results
#### 3.2.1 The Influence of Relative Azimuth Angle on MC Prediction Results  
Table 3 presents the results of the MC prediction model using multispectral data at different relative azimuth angles. The best modeling effect was observed at an azimuth angle of 90\({}^{\circ}\) in both the PLSR and SVM models. For the PLSR model, the correlation coefficient (R\({}_{\text{c}}\)) of the training set was 0.5798, the root mean square error (RMSEC) of the training set was 2.7122, the correlation coefficient (R\({}_{\text{p}}\)) of the prediction set was 0.3733, and the root mean square error (RMSEP) of the prediction set was 3.1731. Similarly, for the SVM model, the R\({}_{\text{c}}\) of the training set was 0.5409, the RMSEC of the training set was 3.2294, the R\({}_{\text{p}}\) of the prediction set was 0.5036, and the RMSEP of the prediction set was 3.2907.  
Figure 5: Spectral curves of jujube fruit under different azimuth angles. (**a**) Jujube fruit original reflectance; (**b**) Reflectance after continuum removal.  
Figure 6 presents the prediction results of the MC of jujube fruit at a relative azimuth angle of 90\({}^{\circ}\). Despite being the optimal angle, the model constructed using PLSR and SVM exhibited low correlation and accuracy. Hence, relying solely on the multispectral data at this specific azimuth angle is insufficient for the quantitative detection of the MC in jujube fruit.
## 3 Results and Discussion  
### Validation and Grid Independence Test  
To verify the numerical method described above, we compare the computational results of the Nusselt number and friction factor with the well-known empirical formulas for plain tubes, as shown in Figures 3 and 4, respectively. It is clear that the CFD results are in good agreement with the empirical correlations, showing less than 10% and 3.1% deviation for the Nusselt number and friction factor,  
Figure 2: Partial computational domain: (**a**) external grid; (**b**) grid near the wall; (**c**) internal grid.  
respectively. The empirical formulas for the plain tube based on the Dittus-Boelter equation and Darcy-Weisbach equation are written as follows [23]:  
\[Nu_{p}=0.023\text{Re}^{0.8}\text{Pr}^{0.4} \tag{17}\]  
\[f_{p}=0.3164\text{Re}^{-0.25} \tag{18}\]  
where Re number is a function of the mean velocity \(u\) and the tube diameter \(D\) as shown in Equation (15).  
Since the solution should be independent from the grids, the grid independent tests were performed by using three different grid cases. The test problem was the turbulent flow in a tube inserted with BTs with \(BDR\) = 0.5, 0.75 and 1 at the spacer length of \(S\) = 40 mm and Reynolds number of 10,000, as shown in Table 3. From the calculated values, the 1,507,296-, 1,439,776-, and 1,414,464-grid systems were found to be dense enough to be used as the grid independent solutions. Thus, the grid systems with 1,507,296, 1,439,776, and 1,414,464 grids were respectively employed to conduct the subsequent calculations. The grid independent tests were also carried out for other parametric situations.  
Figure 4: Data verification of the friction factor with the Reynolds number for the plain tube.  
Figure 3: Data verification of the Nusselt number with the Reynolds number for the plain tube.