esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis Model and Experimental Verification  
### Fundamentals of Dynamics Theory  
In Abaqus nonlinear dynamics analysis, both explicit dynamics and implicit dynamics can solve for the acceleration of each node, achieving balance between force and node acceleration. The equations of motion of the friction system can be written as Equation (1).  
\[[\ M\ ]\ddot{x}{=}\ F\ -\mathrm{P} \tag{1}\]  
In the equation: \([\ M\ ]\) stands for mass matrix; \(\mathrm{P}\) stands for the internal force acting on the structure; \(\mathrm{F}\) stands for the external force on the structure.  
In Equation (1), the actual equilibrium equation is replaced by an implicit integration operator of the weight of the inertial force at the end of the time step and the weighted average of the static force at the beginning of the time step, resulting in  
\[\mathrm{M}\ddot{x}_{(t+\Delta t)}-\ (1+\alpha)(\mathrm{F}_{(t+\Delta t)}- \mathrm{P}_{(t+\Delta t)})+\alpha(\mathrm{F}_{t}\ -\mathrm{P}_{t}\ )=0 \tag{2}\]  
In the Newmark method [33], the displacement and velocity of the system can be expressed as  
\[x_{(t+\Delta t)}=x_{t}+\Delta t\cdot\dot{x}_{t}+\Delta t^{2}\bigg{(}\bigg{(} \frac{1}{2}-\beta\bigg{)}\ddot{x}_{t}+\beta\ddot{x}_{t+\Delta t}\bigg{)} \tag{3}\]  
\[\dot{x}_{(t+\Delta t)}=\dot{x}_{t}+\Delta t((1\ \ -\ \gamma)\ddot{x}_{t}+ \gamma\ddot{x}_{t+\Delta t} \tag{4}\]  
In the equation: \(\beta=\!\frac{1-\alpha^{2}}{4},\ \gamma=\!\frac{1}{2}\ -\ \alpha,\ -\ \frac{1}{3}\leq\ \alpha\ \leq\ 0\).  
The implicit time integration method uses automatic incremental steps to iteratively solve using the Newton method. At the end of the incremental step of time \"t + \(\Delta t\)\" analysis, Newton iteration calculates the displacement at this time.
## 3 Methods  
### Estimation of Vegetation Phenology and Productivity Metrics  
The phenology metrics of LOS, SOS, and EOS of GIMMS3g are calculated per pixel from time series parameterization by a Savitsky-Golay filter in the TIMESAT software [47; 48]. The growing season NDVI integral (GSI) is used here as a proxy for vegetation productivity (greenness) [49], and is also computed from TIMESAT. The advantage of this approach is to minimize any influence from year-to-year variations in the length of seasonal snow cover on the calculated NDVI trends [49]. The Savitsky-Golay filter is a moving filter to fit values from least square to polynomial [48] and the degree of adaptation to the upper envelope can be tuned to the desired level [50]. The start and end of the growing season (SOS and EOS) are determined, respectively, for individual years from the per-pixel polynomial fit based on a parameterization of the fitted seasonal NDVI curve. The parameterization is calculated from a percentage NDVI threshold of total seasonal NDVI amplitude. The LOS is determined  
Figure 1: Major land biomes of mid- and high-latitudes of the NH aggregated from MODIS MCD12C1 global land cover product in 2001. ENF: evergreen needleleaf forest, DNF: deciduous needleleaf forest, DBF: deciduous broadleaf forest, MF: mixed forest, SHB: shrublands, SVN: savannas, GRS: grasslands, CRP: croplands. Note: SHB: including closed shrublands and open shrublands; SVN: including woody savannas and savannas.  
by the duration between the start and end of the growing season in each year. The GSI representing growing season vegetation productivity is calculated by the area between the fitted function and the base level from the start to end of the growing season. The parameters that are applied for the TIMESAT analysis are as follows: seasonal parameter = 0.5, number of envelope iterations = 2, adaptation strength = 1, Savitzky-Golay window size = 2, amplitude season start and end = 20%. For the case of croplands with multiple seasonal peaks, we have chosen to make the fitting procedure always output a primary season with maximum annual amplitude.
## 2 Materials and Methods
### DT-Based Prediction Model  
Combining the PCa process with ML created a DT-based predictive model. Figure 5 shows the method used to construct the DT-based prediction model. Using the backward chaining method, the ML model was designed in reverse order from the last to the first process. The proposed model was completed by removing unnecessary attributes to increase the accuracy of the last inferred BCR.  
Step 1. The priority of the data to be predicted was determined based on the process configuration. The priorities were arranged sequentially in the waiting pool.  
Step 2. The input variables for the learning model were determined using a feature selection algorithm and information gain (IG) [33]. The software used for the IG algorithm was Java SE 17 and Weka 3.6.3. The feature selection method is illustrated in Figure 6.  
Figure 4: Structure of the ML models (machine learning: ML; body mass index: BMI; initial prostate-specific antigen: PSA; Gleason score (primary): GS (primary); Gleason score (secondary): GS (secondary); extracapsular extension: ECE; seminal vesicle: SVI; perineural invasion: PNI; lymph node metastasis: LVI; surgical margin: SM; pathology T stage: PT; pathology Gleason score (SUM); PGS (SUM); biochemical recurrence: BCR).  
Figure 5: Method for the DT-based prediction model.  
The rank of the attributes was determined using the IG method. The ML model was trained by removing the attributes with the lowest rank one after the other. The ML algorithm used a random forest (RF) [34]. When the model with the highest accuracy was generated during the training, the training was terminated. The completed model was placed into the complete pool. In the next order, the highest-ranked data became the priority of the next learning model.  
Step 3. The priority of the next model was trained according to the data priority of the waiting pool. The model creation proceeded as in Step 2.  
Step 4. If the priorities overlapped, the model with the highest rank among the attributes of the last completed model was determined to be the next priority. The model creation was the same as in Step 2.  
Step 5. Steps 2, 3, and 4 were repeated until all models were complete.  
Step 6. The process ended when all of the models were completed and constituted a DT-based prediction model. The configuration started with the input data, and was then connected to the last predictive model.  
The DT-based prediction model consists of the patient's biopsy data as input data. When input data are input to the DT-based model, they become an input to the last generated machine learning model. The output results are then sequentially input to the connected predictive model. Finally, pathology and BCR are output according to the biopsy data.
# Design and Performance Evaluation of eLoran Monitoring System
###### Abstract  
The monitoring system is one of the indispensable components of the eLoran system, which can monitor the reliability and integrity of the eLoran system. In this paper, an eLoran monitoring system is designed based on the BPL time service system, and an integrity monitoring method based on the receiver time difference prediction model is designed according to the stability and accuracy of the receiver time difference. The deviation between the solved time difference and the predicted time difference is utilized to assist in integrity monitoring at the user's end. And the test results show that the monitoring system can effectively determine the signal quality and system health of the eLoran system and provide early warning service for the system performance.  
eLoran; integrity monitoring; BPL time service +
Footnote †: journal: Applied sciences
## 4 Experimental Investigations
### Subjective Experiment for Auditory Localization
#### 4.3.2 Procedure  
During the experiment, the testing stimuli were presented to the participants directly through headphones [Sennheiser HD 560]. Before the test, the instructions and questionnaires for the experiment were handed out to all the participants. On the questionnaire, all the testing azimuths were listed for each testing stimulus. The participants were asked to select the most accurate azimuth according to their own judgments after hearing the stimulus. During the test, each stimulus was presented five times in succession. Then, there would be a 5 s interval, in which the participants made their judgments to the presented stimulus on the questionnaire. After that, the next stimulus would be presented in the same way, until all the testing stimuli of the two testing HRTF species were finished. The test continued until this process was repeated three times. To avoid the possible bias of the localization results caused by auditory fatigue, there was a 20 min rest between repetitions.  
The presentation order of the stimuli was randomized using an altered Latin square scheme. Thus, the possible bias effects caused by the order effect and sequential dependencies could be minimized. The auditory localization experiment lasted about 1.5 h for each participant.
# Design Procedures for Sustainable Structural Concretes Using Wastes and Industrial By-Products
## 3 Results
### Physical Properties
#### 3.3.4 Resistance to Carbonation  
Carbonation resistance was measured using an accelerated carbonation test. The specimens were exposed to an environment with a concentration of 5 \(\pm\) 0.5% of carbon dioxide at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 55 \(\pm\) 5% for up to 186 days. For each of the exposure times adopted, as shown in Table 18, the failure surfaces obtained after splitting the beam specimens into two halves were sprayed with a phenolphthalein solution, resulting in a pink coloration showing the uncarbonated concrete portion. As shown in Table 17, this color difference was used to measure the carbonation depth. The carbonation resistance, \(R_{c65}\), was calculated using equation (10), following the recommendation of the Portuguese Standard LNEC E 465 [57]):  
\[R_{c65}=\frac{2\times C_{accl}\times t_{1}}{X_{1}^{2}} \tag{10}\]  
where \(C_{accl}\) is the CO\({}_{2}\) concentration adopted to accelerate the carbonation process (=90 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\)), and \(t_{1}\) is the time required to reach a carbonation depth \(X_{1}\) in the specimen. The obtained variables of carbonation resistance (\(R_{c65}\)), average carbonation resistance (\(R_{c65m}\)), time (\(t_{1}\)) and average carbonation depth (\(X_{1m}\)) for REF2, EAFS8D1 and FRC_EAFS mixtures are shown in Tables 19 and 20.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen** & \(\mathbf{L_{m}}\) & \(\mathbf{A_{m}}\) & \(\mathbf{T_{10m}}\) & \(\mathbf{v}\) & \(\mathbf{K_{G}}\) & \(\mathbf{K_{Gm}}\) \\
**(mm)** & **(mm\({}^{2}\))** & **(s)** & **(\(\times\)10\({}^{-8}\) m\({}^{3}\)/s)** & **(\(\times\)10\({}^{-16}\) m\({}^{2}\))** & **(\(\times\)10\({}^{-16}\) m\({}^{2}\))** & **CoV** \\ \hline EAFS8D1\_C1 & 39.13 & 1640.66 & 9.279 & 5.290 & 0.584 & & \\ EAFS8D1\_C2 & 39.02 & 1649.10 & 10.082 & 4.869 & 0.533 & & \\ EAFS8D1\_C3 & 39.15 & 1642.63 & 9.060 & 5.418 & 0.597 & 0.558 & 0.07 \\ EAFS8D1\_C4 & 39.37 & 1642.63 & 9.036 & 5.432 & 0.602 & & \\ EAFS8D1\_C5 & 39.38 & 1640.30 & 10.635 & 4.616 & 0.513 & & \\ EAFS8D1\_C6 & 38.45 & 1634.56 & 10.274 & 4.778 & 0.520 & & \\ \hline \end{tabular}
\end{table}
Table 16: Results of air permeability obtained for EAFS8D1 mixture.  
Figure 8: Representative EAFS8D1 specimens after water permeability test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**t\({}_{1}\) (Days)** & **REF2** & **EAFSSD1** & **FRC\_EAFS** \\ \hline
28 & & & \\
56 & & & \\
70 & & & \\
90 & & & \\
122 & & & \\
143 & & & \\
149 & & & \\
163 & & & \\
170 & & & \\
174 & & & \\ \end{tabular}
\end{table}
Table 18: Evolution of carbonation depth at increasing exposure times for the REF2 mixture and for the mixtures designed with EAFS and RTSF.
## 3 Synergism in Mixtures of Sweeteners
### Binary Mixtures of Sweeteners
Table 1 summarizes the previously reported most popular binary mixtures of sweeteners showing significant synergism, as well as their concentrations and the evaluation methods applied [20; 22; 32]. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Sweetener 1** & **Sweetener 2** & **Concentration of 1** & **Concentration of 2** &
\begin{tabular}{l} **Evaluation** \\ **Method** \\ \end{tabular} & **References** \\ \hline Acesulfame-K & Aspartame & Equal sweetness to 3\% or 5\% sucrose 1 & Same as sweetener 1 & I & [32] \\ Acesulfame-K & Cyclamate & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Altame & NHDC 2 & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Aspartame & Saccharin & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Aspartame & Stevioside & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ NHDC & Fructose & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Fructose & Sorbitol & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ NHDC & Glucose & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Glucose & Sorbitol & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Cyclamate & Stevioside & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Saccharin & Rebaudioside-A & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ NHDC & Rebaudioside-A & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ NHDC & Stevioside & Equal sweetness to 3\% or 5\% sucrose & Same as sweetener 1 & I & [32] \\ Cyclamate & Sucrose & 5.6/12.5/22.6 mM 3 & 0.1/0.25/0.5 M & II & [20] \\ Cyclamate & Aspartame & 5.6/12.5/22.6 mM & 0.7/2.7/4.9 mM & II & [20] \\ Cyclamate & Acesulfame-K & 5.6/12.5/22.6 mM & 1.0/4.0/6.0 mM & II & [20] \\ Cyclamate & Glucose & 5.6/12.5/22.6 mM & 0.39/0.88/1.69 M & II & [20] \\ Cyclamate & Xylitol & 5.6/12.5/22.6 mM & 0.2/0.6/1.0 M & II & [20] \\ Stevioside & Aspartame & 0.25/0.62/1.1 mM & 0.7/2.7/4.9 mM & II & [20] \\ Stevioside & Acesulfame-K & 0.25/0.62/1.1 mM & 1.0/4.0/6.0 mM & II & [20] \\ Saccharin & Sucrose & 0.39/1.95/4.87 mM & 0.1/0.25/0.5 M & II & [20] \\ Saccharin & Aspartame & 0.39/1.95/4.87 mM & 0.7/2.7/4.9 mM & II & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The popular binary mixtures of sweeteners showing significant synergism reported up to now.
## Materials and Methods
### Sediment and Water Sampling, and In Situ Analyses  
Sediment samples from each mesocosm were obtained in 9 replicates, both just before treatments injections, as well as 45 days after, at the end of the experiments, to determine the variations of the studied variables. The sediment samples were obtained with a Russian peat borer sampler [26] provided of extension rods to integrate the sediment sample from surface sediments to the desired depth. This was stacked in the sediments to cut through them by turning clockwise. Accordingly, a semi-cylindrical shaped sediment core was obtained that maintained intact its structure. Depth-integrated sediment samples were obtained from the upper 20 cm and kept frozen until analysis. The variables measured in these sediment samples were the percentage (w/w) organic matter content (%OM), the chemical (COD) and the biochemical (BOD\({}_{5}\)) oxygen demand, total nitrogen (TN), total phosphorus (TP), ammonium (NH\({}_{4}\)\({}^{+}\)), nitrite (NO\({}_{2}\)\({}^{-}\)), and loosely-binding phosphorus (loosely-P). The pH of sediments was measured on a 1:2.5 sediment to water ratio aliquot after the stabilization of the pH measurement in the mix. Measurements were performed with a Thermo Scientific Orion 8102 BN Ross combination pH electrode (Thermo Fisher, Waltham, MA, USA). Dissolved oxygen (DO) and temperature in the overlaying water inside mesocosms were measured with a WTW Oxi-3310 (WTW, Weilheim, Germany) device provided with an optical oxygen sensor and an electrothermic sensor.  
The main sampling effort was dedicated to the characterization of sediments as they were the target of the study. However, an additional survey was conducted to monitor the suspended solids in water and its content in particulate OM to assess possible impacts of OM deposition on the mesocosms. Thus, a volume of unfiltered water sample was obtained from each microcosm at the end of the experiment to measure the total suspended solids (TSS) concentration and the amount of settling matter. Additionally, both at the beginning and the end of the experiments, a subsample of unfiltered water was kept in high density polyethylene (HDPE) bottles of 125 mL for the total organic carbon (TOC) analysis, whereas another bottle was filled with water filtered through Whatman GF/F glass filters (VWR, Barcelona, Spain), corresponding to a retention of approximately 0.7 \(\upmu\)m nominal particle size, for the analysis of the dissolved organic carbon (DOC). All water samples were transported refrigerated to the lab for further analyses. A sediment trap was also installed inside each control mesocosm to assess the extent of the time-integrated settling and possible re-suspension of sediments during the experiment.
While the aforementioned studies explored the link between LCZ types and LST at the city scale, detailed analysis should focus on understanding causes of intra-LCZ LST variations. The value of surface characteristics should vary in different LCZs, given that it has shown variation between LCZs, between cities, and in different seasons. While LCZ schemes partition a city at a scale ranging from hundreds of meters to several kilometers, LST variations must be expected within the different LCZs. To the best of our knowledge, no study has explained the variations of LST within an LCZ category. Furthermore, the differential strengths of the correlation of LST with surface properties, such as water content and vegetation distribution, have not been investigated at the intra-LCZ scale. While quantitative measures of surface properties such as NDVI, MNDWI, NDBaI, NDBI, UI, and DEM have shown correlations with LST at the inter-LCZ and city scales, they have not been used to explain intra-LCZ LST variations. Furthermore, the contribution of these surface properties has not been compared between the inter- and intra-LCZ scales. Hence, the objective of this study was: (i) to determine inter-LCZ variations in LST and contributions of the different LCZs to heating or cooling of the city; (ii) to explain the causes of inter-LCZ LST variations; (iii) to compare the contribution of surface properties to LST variations between inter-LCZ and intra-LCZ scales using quantitative measures of surface properties (NDVI, NDBaI, NDBI, MNDWI, Elevation, and Aspect); and (iv) to quantify the variations in the contributions of surface properties to intra-LCZ LST variations in Harare, Zimbabwe.
# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 1 Introduction
By doing so, if the patient had to be indeed admitted into the hospital after the visit, the admission process would have already been completed and there would be no extra waiting time nor unnecessary crowding, as portrayed in Figure 1.  
In spite of some studies [3; 13; 19] showing that patient visit data can improve the predictive performance slightly when compared with a model predicting admissions at the time of arrival, this study aims at predicting admissions using information readily available at the moment of triage, immediately following the arrival of the patient at the ED by analyzing data from more than 60 different centers in the Integral Healthcare System for Public Use in Catalonia (SISCAT). Triage is the first stage of an ED visit and its objective is to assess in a regulated, validated, and reproducible way, and as accurately as possible, the level of urgency of a visit to organize patients in recognizable groups to prioritize the sickest ones [20]. The primary reason for performing this is that the sooner admissions are initiated, the higher the chance of being able to reduce ED crowding, which is the ultimate objective of developing a predictive model for hospital admissions. Moreover, the predictive models might help reduce overcrowding at EDs and improve the service delivered to patients. The first step of this paper was to identify data requirements followed by pre-processing data. Then, a gradient boosting machine was used to train and test the predictive models in R. After that, variable importance for each of the models was analyzed, receiver operating characteristic (ROC) curves were created, and the area under the curve  
Figure 1: (**a**) Sequential admission model; (**b**) simultaneous admission model. (AUC) was obtained from each of them as a measure of predictive performance. Finally, the results were obtained and future works were proposed.
# Design of Platforms for Experimentation in Industrial Cybersecurity
## 5 Fulfillment of the Usage Goals
Although there are powerful solutions in the market, they generally have a higher cost, complicated configuration and monitoring or, sometimes, limited support of industrial protocol filtering. The experiments with this prototype were oriented to measure its performance under network conditions as the ones expected in small to medium-sized control systems. For that reason, the elements in the industrial control system were used to generate traffic. For that purpose, the prototype substituted the pre-existing firewall (see Figure 4) during the experiments. The results showed the feasibility of the prototype for operation under those circumstances, both with respect to its ability to filter traffic and the quality of user experience. In the area of **demonstrations of control systems**, another result is the design and implementation of the testbed necessary for the development of a functional and scalable network intrusion detection system (NIDS) in a collaboration with the National Cybersecurity Institute of Spain (INCIBE). This NIDS captures security events and system information transparently, by means of custom filters and rules applied to well-known network monitoring software. It also includes a security information and event management (SIEM) system, for the storage and visualization of those events. As a result, it allows the generation of alerts and reports both during operation (for intrusion detection) and in the past (to support forensic analysis). This NIDS allowed to analyze and visualize security events that occurred in the industrial testbed. Although the configuration of the laboratory was centered in the generation of Modbus and UMAS (Schneider Electric configuration protocol) traffic in realistic conditions, it was an example of experimentation with a wide variety of technologies. Indeed, it involved the use of industrial-oriented software, such as an historian server, a SCADA server and client and an engineering workstation running the PLC programming platform, and the coordinated operation of control devices. These elements were located in different zones in the supervision and control networks, following a realistic network design, using network and filtering devices. For the experiment operation, it was also necessary to deploy services in the management subsystem of the laboratory, including several network probes, as well as hosts for intrusion detection, event processing, storage and visualization. The testbed was used to emulate several normal conditions but was also subject to a set of attacks. The results were positive, since the developers of the NIDS were able to run all their predefined tests and experiments. In Figure 7c, a screenshot of the NIDS use is shown. In this example, logs of two days were selected. The user interface shows IP addresses of the computers that accessed to the control system, along with times and frequency. In the lower part of the screen, more information of the security event is displayed.