esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion
### Effect of Stabilizers on Arterberg Limits
As described by [PERSON] et al. [25] and [PERSON] et al. [26], the addition of branched polymers with high viscosity can reduce permeability and improve mechanical properties. In this study, molasses with a viscosity of 18,400 mPa.s (at 30 \({}^{\circ}\)C) (Table 1) facilitates particle bridging between soil aggregates, closing voids, and enhancing the soil matrix's mechanical strength. This is consistent with previous findings where polymer-based stabilizers improved soil performance by reducing void space and increasing resistance to deformation. Stabilizing expansive clay soil with a combination of molasses and cement is a relatively new concept, and the literature on the interactions among these components is limited. Therefore, the observed reduction in the Atterberg limits may be due to one or more of the following mechanisms: cation exchange, hydration reaction, pozzolanic reaction, or the adhesive properties of molasses. Research indicates that the combination of molasses and cement effectively stabilizes subgrade soil, with all tested mixtures yielding PI values below 20%, as shown in Figure 7. This threshold is significant as it meets the  
Figure 6: Plastic limit results with constant molasses content and varying cement percentages. criteria for adequate soil stabilization, which is crucial for construction and infrastructure projects. For instance, one study demonstrated that the addition of molasses to cement significantly improved the mechanical properties of the soil, resulting in enhanced stability and reduced plasticity [27].
## 5 Performance Evaluations
### Efficiency of Convex Hull Based Geocasting
Another reason of this difference in delays of GEAR and GeoCHT is their different forwarding mechanisms. GEAR uses unicasting to reach next hop but GeoCHT uses a special restricted broadcasting (_i.e._, multicasting) which is quicker in the delivery of the message to the next node. The network load of GEAR and GeoCHT are shown in Figure 9(b). The network load of GEAR is higher than the network load of GeoCHT in this mobile scenario. The network load of GEAR is slightly decreasing when we increase the number of mobile nodes since for a node, the probability of changing its neighborhood is getting higher so the query packet can get stuck at a node which does not have a neighbor that is closer to the area of interest anymore. Therefore, the number of sent query packets decreases in GEAR. As a results of this comparison, we can conclude that geocasting based on convex hulls requires less messages to be injected from sinks to deliver queries to a target area. Also, the latency for geocasting based on convex hulls is lower. These results point out that our protocol achieves a more efficient use of resources than pure greedy forwarding towards the target region and restricted flooding inside the target region based geocasting approaches (_i.e._, GEAR). On the other hand, convex hull based geocasting needs a special mechanism to update the geocast structure in case of mobility of sensors to improve ER.
# Sensitivity Analysis of PROMETHEE II for the Evaluation of Environmental Websites
## 3 Results
### Sensitivity Analysis  
In order to evaluate the consistency of the results of PROMETHEE II, a sensitivity analysis was performed. The main aim of a sensitivity analysis is to evaluate how the MCDM models change the ranking of the alternatives when input data are slightly modified [44]. For this purpose, we used a second scheme of weights which assigned equal weight to each of the criteria [45; 46]. Since there are 10 criteria, the weight for each criterion was determined to be 0.1. The values of the utility function \(U\) using SAW or WPM, the values of C using TOPSIS and the values of \(\phi(\alpha)\) for each website were recalculated using the second weighting scheme. All these new values of U, C and \(\phi(\alpha)\), as well as the new raking orders of the websites using each one of the three MCDM models, are presented in Table 5.  
In order to compare the consistency of the MCDM models, we evaluated the robustness of the ranking produced by each model based on the results of the sensitivity analysis. The consistency of an MCDM model is low if the ranking of the alternatives is severely modified when the weights differentiate. Therefore, we compare the rankings produced by the same model using the different schemes of weights. In order to compare how close the rankings of each model are using the two schemes of weights, we use Spearman's Rho correlation test to analyze the correlation among the obtained rankings.  
The Spearman's Rho correlation is estimated by:  
\[R=1-\frac{6\sum_{i=1}^{n}d_{i}^{2}}{n(n^{2}-1)}\]  
where \(d_{i}\) is the rank different at position i and \(n\) is the number of ranks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SAW** & **SAW Ranking** & **WPM** & **WPM Ranking** & **PROMETHEE II** &
\begin{tabular}{c} **PROMETHEE** \\ **II Ranking** \\ \end{tabular} & **TOPSIS** & **TOPSIS Ranking** \\ \hline A1 & 34.875 & 13 & 33.584 & 13 & \(-\)0.123 & 14 & 0.529 & 13 \\ \hline A2 & 35.213 & 12 & 33.549 & 14 & \(-\)0.168 & 15 & 0.561 & 9 \\ \hline A3 &  
The values of [PERSON]'s Rho for the pair-wise comparisons of MCDM models are: \(\text{Rsaw}=0.929\), \(\text{Rwpm}=0.936\), \(\text{Rpromethe}=0.967\), \(\text{Rtopsis}=0.841\). The Spearman's Rho has its highest value for PROMETHEE II and, therefore, PROMETHEE II has the higher consistency the results when compared with SAW, WPM and TOPSIS. This means that it is less affected by changes in the values or in the weights of the criteria. Consistency is considered to be important when selecting an MCDM model. Since PROMETHEE II is less affected by the subjectivity of the reasoning of the decision-makers, it proved to be better than the other three models.
## 4 Case Study
### Calculating FRPNs for IVIS
#### 4.3.2 Calculating the FRPN  
Through the constructed fuzzy logic system, the FRPN was obtained. Figure 7 illustrates the rule viewer of the constructed system, which can receive the defuzzification values of the risk parameters for the three inputs and generate the FRPN for the output. For example, for failure mode No. 1, it can be expressed as follows: if the defuzzification value of occurrence was 7.342, the defuzzification value of severity was 7.488, and the defuzzification value of detection was 3.498, then the FRPN was 6.395. The FRPNs of all the failure models produced by using the constructed fuzzy logic system are presented in column 2 of Table 14, and the prioritizations as per FRPNs are presented in the last column of Table 14.  
Figure 5: Schematic of the constructed fuzzy logic system.  
Figure 6: Three-dimensional surface viewers. (**a**) Severity, occurrence, and FRPN; (**b**) Detection, occurrence, and FRPN.
## 3 Bayesian Attractor Model
The prediction error between predicted the mean \(\hat{\vec{x}}_{t}\) and the actual observation \(\vec{x}_{t}\) is calculated by  
\[\vec{\epsilon}_{t}=\vec{x}_{t}-\hat{\vec{x}}_{t}. \tag{3}\]  
Then, the decision state prediction \(\hat{\vec{z}}_{t}\) and its posterior covariance matrix \(\hat{\vec{P}}_{t}\) are updated via a Kalman gain \(\vec{K}_{t}\):  
\[\hat{\vec{z}}_{t}=\hat{\vec{z}}_{t}+\vec{K}_{t}\vec{\epsilon}_{t}, \tag{4}\]  
\[\hat{\vec{P}}_{t}=\hat{\vec{P}}_{t}-\vec{K}_{t}\hat{\vec{c}}_{t}^{T}. \tag{5}\]  
The Kalman gain is calculated by  
\[\vec{K}_{t}=\hat{\vec{C}}_{t}\hat{\vec{z}}_{t}^{-1}, \tag{6}\]  
where \(\hat{\vec{C}}_{t}\) is the cross-covariance between predicted decision state \(\hat{\vec{z}}_{t}\) and predicted observation \(\hat{\vec{x}}_{t}\), and \(\hat{\vec{z}}_{t}\) is the covariance matrix of the predicted observations. [PERSON] et al. [8] used posterior density over the decision state evaluated at the stable fixed point \(\phi_{i}\), is used as a confidence level metric of each attractor \(i\). However, we used the state value \(z_{i}\) as the confidence metric in this work, because \(z_{i}\) is less oscillatory and more clearly bounded than the posterior density \(p(\vec{z}_{t}=\phi_{i}\mid\vec{X}_{\Delta t:t})\). As for the decision criterion, we used the difference between the state value \(z_{i}\) of attractors. After sorting the state values from highest to the lowest, let's say the attractor \(j\) has the highest and the attractor \(k\) has the second highest state value. If the difference between the state values of these two attractors becomes higher than a threshold \(\lambda\), then the attractor \(j\) is identified as the new state. That is  
\[z_{j}-z_{k}>\lambda. \tag{7}\]
## 10 Experimental Analysis
### Statistical Comparisons on CEC2014 Problems
#### 10.1.1 Analyzing the Effectiveness of the Chaos Maps on BOA Performance  
To evaluate the effectiveness of the chaos theory-based sensory fragrance, as proposed in Section 7.2 in Equation (55), on the optimization performance of the BOA algorithm we have performed the following statistical analysis. The performance of the BOA algorithm is evaluated with five chaos maps, as shown in Figure 3, and compared to the conventional BOA algorithm using the Friedman test. The following variations of BOA algorithm were considered, which have the same operators as conventional BOA, except that the sensory fragrance is set according to Equation (48) with the following chaos map:  
1. BOA-c-Picewise, using the Piecewise map given in Equation (49),
2. BOA-c-Iterative, using the Iterative map given in Equation (51),
3. BOA-c-Logistic, using the Logistic map given in Equation (52),
4. BOA-c-Sine, using the Sine map given in Equation (53),
5. BOA-c-Tent, using the Tent map given in Equation (54).  
Table 1 shows a summary of the Friedman test's statistical results of the performance comparison between the five chaos-improved BOA algorithms and the conventional BOA.  
According to the results in Table 1, we conclude that the chaos-enhanced BOA algorithms have significantly better performance than the conventional BOA. Moreover, the results demonstrate that the best performance among the tested algorithms is achieved with the BOA-c-Sine algorithm, which demonstrates the effectiveness of the proposed modification. Therefore, we conclude that the improved chaos-based sensory fragrance given in Equation (55) will be taken in hybridization with the DE algorithm with the sine map given in Equation (53).
## 4 Discussion
### Fusepool in Practice
#### 4.1.2 Weyeser's Data Transparency Mash-Up for the Dutch Parliament  
Weyeser is a Dutch software consultancy working with private and government clients. They participated in the boot camp of the Data|Hack|Award|2014 in Berlin (DHA14) and presented their work at the hackathon. Together with Tulp Interactive, they implemented in less than two month a complete data processing system based on core components of the Fusepool platform and added a new visualization implementation on top of it. Weyeser and Tulp Interactive presented the final system at the Fusepool showcase event in Brussels.  
Weyeser envisioned an application together with the Dutch Parliament to classify and visualize the data stream of parliamentary work. Citizens face significant hurdles to get a grip on what is discussed or voted on in parliament. Such insight empowers citizens: they can more actively engage in the conversation with their representatives. At the same time, the overview of actively discussed and voted on topics helps fulfill government's transparency agenda. Fusepool enabled Weyeser to re-use hand-classified documents collected over decades to build a content labeling system within the platform.  
To get started, Weyeser had Fusepool and its components at their disposal and a clear goal:  
1. single entry for parliamentary documents and visualization of the evolution of topics over time;
2. amend the platform with new tools to visually analyze the results and emerging patterns.  
The data consisted of the database from the Dutch parliament which contains all parliamentary documents that had been generated over the past 30 years, all hand classified and in full-text. The data provided a rich stock for the Fusepool platform to build dictionary annotators.  
The work involved in getting the VIP data into the Fusepool platform required building RDFizers to get the XML structures of the VIP data mapped to RDF and extracting the classes to build the annotators. The data is then loaded into the Fusepool platform and ran through the processing steps to index and annotate the documents. At this point, all the built-in features of the platform are available to end users. Weyeser then built their own data visualization layer on top of it to display results.  
Figures 6 and 7 show the 2010 subset of the VIP data. Each row displays the documents (red line) that mention the label shown in the middle of the row. More mentions make the row display thicker than others, and the total count over the year is shown in brackets. In this example, citizens learn that cost control (Kostenbeheersing) was an active topic in 2010 (Figure 6). They can proceed to click on it and see how cost control relates to other topics in 2010 (Figure 7). This enables citizens to inspect topics discussed in relationship to cost control and get a clearer picture of how they are related.
# FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
## 2 Instrument Overview  
The FY3E satellite is in a Sun-synchronous polar-orbiting orbit at an altitude of 836 km and an inclination of 98.5\({}^{\circ}\). The satellite is a 5.7 m \(\times\) 3.2 m \(\times\) 11.6 m hexahedron, with a total mass of about 2675 kg. The attitude of the satellite is controlled by three-axis stabilization and monitored by an onboard star sensor [13]. Due to its great size and weight, the satellite has an advantage in attitude stability control.  
Figure 1 shows an image of the GNOS II instrument. The payload has a total mass of 24.5 kg and power consumption of 55 W. It has two up-pointed antennas for positioning,  
two side-pointed antennas for GNSS RO and one nadir-pointed antenna for reflectometry. It has eight GNSS-R channels, which can track eight specular points at the same time. It can receive GPS L1, BDS B1 and GAL E1 signals. As it is now in the half-year in-orbit testing stage, the reception of Galileo signals was temporarily turned off. Four channels are now used to process GPS signals and another four channels are used for BDS signals. For BDS, while the transmitting satellites are in three different orbits, including geostationary orbit (GEO), inclined geosynchronous orbit (IGSO) and medium earth orbit (MEO), the receiver was designed to exclude signals from GEO satellites. The major difficulty of using GEO satellites for GNSS-R is that the complete profile of their transmitted effective isotropic radiated power (EIRP) cannot be measured by limited power monitors due to the relative stationary geometry [17].  
Figure 2 illustrates the working of GNOS II. The nadir reflection antenna is tilted 20\({}^{\circ}\) backward. This can maximize the signal-to-noise ratio (SNR) of received signals at specular points, as analyzed in [18]. Figure 3 shows the gain footprint on the Earth's surface for the tilted reflection antenna. The peak antenna gain is about 15 dB.
## 3 Results
### Scenario I
The OBIA labels missed some of the white and grey facility buildings in training data as shown in Figure 9(1A). This can be the reason that the Manual-OCOS models perform better than the OBIA-OCOS models for this type. For brownish facility buildings, both models cannot perform well, as shown in Figure 9(2E). This may be due to the very limited number of such structures in training data and the high similarity to the surrounding environment. Figure 8: The spatial distribution of predicted FP (blue) and FN (red) pixels of the OBIA-OCOS model (**a**) and the Manual-OCOS model (**b**) from the Kule Wet case. The occlusion of trees increases the difficulty of detecting target built-up structures. Both types of models cannot detect most brownish dwellings partially covered by trees, as shown in Figure 9(2D). However, due to the limited number of tree-occluded brownish dwellings, this type of error may not play a significant role in the results. Additionally, we can observe the IoU values from the wet season are higher than those from the dry season. This indicates that the similarity between target-up structures and the surrounding environment may play a more important role in this extraction task. The prediction errors around the boundaries of target built-up structures widely spread the whole testing area for both models, as shown in Figure 8. Figure 9(1B) presents an example showing the difference in the annotation of dwellings between the OBIA labels and the Manual labels. We can observe an obvious shift of the OBIA labels compared with the Manual labels, which may cause more predicted FN and FP pixels. In addition, due to the difficulty of determining the exact boundaries of built-up structures, the Manual-OCOS models also unavoidably have FN and FP pixels around the boundaries. These incorrectly predicted pixels around the boundaries are the major source of errors at pixel levels for both types of labels. This type of error is also the main reason that the Manual labels outperform the OBIA labels in terms of IoU values. Last but not least, models trained with either type of label are unable to detect brown cloth on the top of large dwellings that are densely distributed, as shown in Figure 9(2C). This is likely because both spatial distribution patterns (too dense) and the size (much larger) are different from most dwellings. However, for smaller dwellings covered by brown cloth, the Manual-OCOS models perform better than OBIA-OCOS models. This is mainly because the OBIA labels usually ignore brown parts within a dwelling and separate them into several individual dwellings, as shown in Figure 9(1C). Figure 9: **(1)** The annotated errors in the original OBIA labels and (2) examples (**A–E**) showing the differences in FP (blue) and FN (red) pixels between the OBIA-OCOS model (middle) and the Manual-OCOS model (right) from the Kule Wet case.
## 3 Results
### OCT Analysis
Maximum, standard deviation, median and minimum have been calculated, as previously explained, for the mean and comprise each 16 entries in the OCT texture feature vector. As seen in Figure 4a, the feature intensity is very low for the standard deviation-based features (features 33-48) which indicates that there is a little change in the texture features within one batch. The first three PCs evaluated via PCA are shown in Figure 4b. Significant features mainly comprise contrast in PC 1, energy in PC 2 and correlation in PC 3. While the contrast feature detected in PC1 is very similar in intensity throughout all stages, the energy feature based on PC 2 is not. This is an interesting observation as the energy feature is a property for the uniformity of the analyzed area. As the cancer progress to higher stages fewer layers are visible in the OCT data which increases the uniformity of the image which results in this strong correlation with the energy feature. Similarly, the correlation in PC 3 is a result of the increased uniformity of the OCT image towards higher stages. Figure 3: OCT images of pathologic bladder wall: Structural discrimination of the urothel, lamina propria and muscularis is lost due to the invasion of the tumor into the bladder wall. (**a**) T1 carcinoma is non-muscle invasive bladder cancer that invades the urothel down to the lamina propria. Structural features of these layers are thereby lost, few features coming from small vessels are still intact (see arrows). (**b**) T2 carcinoma invades additionally the muscularis. All structural features are lost.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 2 Materials and Methods
### Data Extraction  
Data extraction was conducted independently by two reviewers and recorded in a pre-defined Excel spreadsheet. Extracted data included study characteristics such as design, country, period, and sample size; patient demographics and clinical parameters; and the outcomes of machine learning model performance. The variables included in the datasets were demographic (age, gender, body mass index), clinical (vascular access type, comorbidities, duration of hemodialysis), and laboratory parameters (e.g., levels of hemoglobin, albumin, calcium, phosphorus, potassium, sodium, and creatinine). Mortality status during the follow-up period was the primary outcome variable. Any disagreements were resolved through discussion among the reviewers.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### \(P_{sat}\) and \(P_{const}\) Calculations
#### 2.3.2 \(\text{P}_{\text{sat}}\) Calculation for B1C and B2a  
For any satellite at any time \(t\), the B1C/B2a SIS health status is determined by the integrity parameters as follows:  
\[\text{HS}=0,\ \text{SIF}=0,\ \text{and DIF}=0\ (\text{Health}) \tag{5}\]  
where HS, SIF, and DIF are the integrity status parameters of B1C/B2a.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & \(C_{1}\) \\ \hline MEO & 0.2285 \\ GEO/IGSO & 0.1512 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of \(C_{1}\) for satellites of different orbit types.  
The SISURE of the B1C/B2a signal is calculated using the same method as the B1I signal in Section 2.3.1, and the satellite service failure condition is determined as follows:  
\[\mathrm{F_{P_{sat,B1C/B2a}}}=\left\{\begin{array}{ll}1,(\mathrm{SISURE}\geq 4. 42\ \mathrm{SISA},\ \mathrm{without\ a\ timely\ alert})\ \mathrm{and}\ \mathrm{(HS,SIF,DIF=0)}\\ 0,\mathrm{other}\end{array}\right. \tag{6}\]  
where the SISA indicators of B1C and B2a signals can be obtained by the SIS accuracy index (SISAI) broadcast in the B-CNAV1 [24] and B-CNAV2 [25] navigation messages, respectively.  
More specifically, SISA can be calculated by the function as following [24; 25; 29]:  
\[\mathrm{SISA}=\sqrt{(\mathrm{SISA_{oe}}\times\sin 14^{\circ})^{2}+\mathrm{SISA _{oc}^{2}}} \tag{7}\]  
where \(\mathrm{SISA_{oe}}\) denotes the elevation-dependent component, and \(\mathrm{SISA_{oc}}\) denotes the non-elevation-dependent component. After that, Equation (4) is also suitable for calculating the \(\mathrm{P_{sat}}\) of B1C and B2a signals.