esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Thermal Boundary Condition on Tilting Pad Journal Bearing Behavior
## 2 Numerical Methods
### Bearing-Journal Dynamic Model  
In this study, time transient analysis was performed for the steady state analysis of bearings without using the Newton-Raphson method used in most previous studies. The transient approach gives simpler algorithm than iteration scheme, where all the degrees of freedom is converging with time. Figure 5 shows the pad-journal dynamic model. The structural flexibility of the pad is ignored, and both pad and journal are assumed to be rigid bodies. For the pad dynamic analysis, fluid force (\(\text{F}_{\text{Reynolds}}(t)\)) calculated by Reynolds equation and pivot force (\(\text{F}_{\text{Fivot}}(t)\)) calculated by Hertzian contact theory are applied to the pad as shown in Figure 5. Equation of motion for the pad is given in Equation (12). Each pad produces both tilting (\(\theta(t)\)) and translational motion (\(x_{p}(t)\)) at the pivot location as shown in Figure 5. For the journal dynamic analysis, fluid force (\(\text{F}_{\text{Reynolds}}(t)\)) and bearing load ((\(\text{F}_{\text{Load}}(t)\)) are applied as shown in Figure 5. Journal has two degrees of freedom in \(x\) and \(y\) directions and the equation of motion is provided in Equation (14).  
\[\text{M}_{\text{pad}}\ddot{\text{X}}_{\text{pad}}=\text{F}_{\text{Reynolds}}+ \text{F}_{\text{Fivot}} \tag{13}\]  
\[\text{M}_{\text{journal}}\ddot{\text{X}}_{\text{journal}}=\text{F}_{\text{ Reynolds}}+\text{F}_{\text{Load}} \tag{14}\]  
Figure 4: Schematic diagram for the calculation of the fluid film thickness.  
Figure 5: Tilting pad journal bearing dynamic model: (**a**) pad dynamic model; (**b**) journal dynamic model.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 4 Diagnostics in Kiwifruit Allergy
### Oral Food Challenge Test  
In the oral food challenge test, the patient ingests increasing doses of various foods while being monitored for an allergic reaction. There are three types of oral food challenge tests: a DBPCFC, a single-blind food challenge, and an open-food challenge test [95]. In the DBPCFC, neither the physician nor the patient knows whether it is an allergen or placebo. In the single-blind food challenge, the physician knows it is an allergen, but the patient does not. In the open-food challenge, both the patient and physician know it is an allergen. The DBPCFC is the preferred method as it minimizes bias.  
The first study using DBPCFCs, published in 2004, involved 33 patients reporting symptoms after eating kiwifruits [17]. It was shown that 70% of patients that participated in a DBPCFC had a positive oral food challenge test. This study also revealed the existence of several clinical subgroups of patients. The subgroups were defined based on the allergic reactions (mild or moderate/severe), presence or absence of associated polinosis, and the presence or absence of a latex allergy. Soon after, another study found that 53% of patients with a history of kiwifruit allergy that undertook the oral food challenge test confirmed that they had a kiwifruit allergy (n = 45) [15]. In another study involving a larger group of patients (n = 92), DBPCFCs were performed on 52 individuals [47].  
There are several drawbacks to the oral food challenge test. This test poses some risk to the patients as they are ingesting a potential allergen; therefore, testing must be performed by a trained healthcare professional in a healthcare facility with the means to treat a severe allergic reaction, should one occur. The DBPCFC test can also be expensive; therefore, physicians can perform the single-blind or open-food challenge instead [95].
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion
### Foam Stabilizer Selection  
Foam stabilizers (i.e., polymers) improve the stability of foam mainly by increasing the viscosity of the foam system's liquid phase [24; 25]. Therefore, the concentration of the polymer solution may be an important indicator of its ability to stabilize the foam system. The viscosity-temperature curve (shear rate: 0.1 s\({}^{-1}\)) and viscosity-shear rate curve (temperature at 45 \({}^{\circ}\)C) were obtained using a rheometer (Haake Mars III, Thermo Fisher Scientific, Waltham, MA, USA) to evaluate the effect of the stabilizers. The results are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **Mass** & **V\({}_{\mathbf{0}}\)** & **V\({}_{\mathbf{t}}\)** & **t\({}_{\mathbf{l/2}}\)** & **t\({}_{\mathbf{D}}\)** & **FCI** & **IFT** \\
**System** & **Ratio** & **(mL)** & **(mL)** & **(min)** & **(s)** & **(mL-min)** & **(mN/m)** \\ \hline  & 1.5:1 & 245 & 15 & 122 & 76 & 22,417.5 & 0.7145 \\ THPB:AOS & 1:1 & 260 & 15 & 103 & 71 & 20,085 & 0.7982 \\  & 1:1.5 & 270 & 15 & 92 & 78 & 18,630 & 0.9054 \\ \hline  & 1.5:1 & 360 & 10 & 101 & 145 & 27,270 & 0.0154 \\ THPB:AES & 1:1 & 450 & 10 & 114 & 161 & 38,475 & 0.0134 \\  & 1:1.5 & 530 & 5 & 300 & 192 & 119,250 & 0.0107 \\ \hline  & 1.5:1 & 310 & 15 & 76 & 78 & 17,670 & 0.0692 \\ THPB:SDS & 1:1 & 330 & 15 & 91 & 78 & 22,522.5 & 0.0631 \\  & 1:1.5 & 370 & 10 & 102 & 81 & 28,305 & 0.0545 \\ \hline  & 1.5:1 & 95 & 35 & 47 & 43 & 3348.75 & 0.5789 \\ DHPB:AOS & 1:1 & 115 & 30 & 46 & 47 & 3967.5 & 0.6541 \\  & 1:1.5 & 145 & 30 & 57 & 47 & 6198.75 & 0.7763 \\ \hline  & 1.5:1 & 80 & 45 & 18 & 36 & 1080 & 0.7316 \\ DHPB:AES & 1:1 & 90 & 45 & 18 & 39 & 1215 & 0.7145 \\  & 1:1.5 & 115 & 40 & 21 & 37 & 1811.25 & 0.7682 \\ \hline  & 1.5:1 & 470 & 5 & 234 & 240 & 82,485 & 0.0089 \\ DHPB:SDS & 1:1 & 515 & 5 & 242 & 256 & 93,472.5 & 0.0084 \\  & 1:1.5 & 560 & 5 & 280 & 334 & 117,600 & 0.0076 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Foaming properties and oil-liquid IFT of compound systems.  
The viscosity of XC at 0.1 g/L was higher than that of the other samples, given its shear rate of 0.03 s\({}^{-1}\), as shown in Figure 3a. Figure 3b shows that the viscosity of XC is higher than that of the other polymers over the temperature range of 20 \({}^{\circ}\)C to 90 \({}^{\circ}\)C. Common polymers are mostly linear structures, whereas XC has a double helix structure.  
This double helix structure induces greater intermolecular entanglement and interaction between XC molecules; thus, the XC molecules maintain their structure better under external influences. In the presence of inorganic salts, XC molecules swell, which increases their viscosity. This feature ensures that XC performs better at high temperatures and under high salinity conditions.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 1 Introduction
### Research Methodology  
Various tactics and strategies were employed to achieve the survey's objectives. We chose the exploration approach to address the research questions and goals of the survey. Specifically, the applied research methodology includes a systematic approach in which the research problems are identified, managed, and practical solutions are proposed. Consequently, the analysis to develop new ideas and transparent information about the issues was carried out by applying this approach, similarly to [4].  
The research methodology used in this paper involves two review strategies.  
The first strategy is a systematic literature review applied to identify, evaluate, and interpret the available influential studies in the specific area of interest. A systematic literature review was mainly performed to summarize existing recent and relevant academic literature and evaluate the currently available work opportunities. This helped us identify the results of research in the published work relating to the research study being undertaken.  
The second strategy was to use online resources and journals that include recent and relevant academic literature in computer science. We used relevant research databases such as the ACM digital library, Science Direct, IEEEXplore, as well as Google Scholar. Our focus was on exploring titles, abstracts, keywords, and articles that include the terms 'cloud computing', 'cloud security', 'intrusion detection', 'intrusion prevention', 'IDS', 'SDN', and 'cloud privacy'. We refine our work and present important points for fog and cloud, as well as for security and privacy for both.
## 2 Background and Related Work
### Related Work  
The goal of this subsection is to briefly present a set of RDF storage engines designed for large-scale Semantic Web applications. It should be stressed that the related work presented here is not exhaustive, but we make a detailed quantitative analysis of system performance with these engines in Section 4.  
QUAD [9] uses quads in the form of \(<\)subject, predicate, object, context\(>\). It stores RDF data persistently by using six B+ tree indices. The indices cover all the 16 possible access patterns of quads. This representation allows the fast retrieval of all triple access patterns. To speed up keyword queries, the lexicon keeps an inverted index on string literals to allow fast full-text searches. To retrieve any access pattern with a single index lookup, QUAD sacrifices storage space and insertion speed for query performance since each triple is encoded in the dictionary six times. Thus, it seems to be primarily geared for simple lookup operations with limited support for joins; it lacks DBMS-style query optimization (e.g., do not consider any join-order optimizations).  
DARQ [14] is a query engine for federated SPARQL queries. It supports transparent query access to multiple, distributed SPARQL endpoints as if querying a single RDF graph. In DARQ, the original SPARQL query is decomposed into several subqueries, where each subquery is sent to its relevant SPARQL endpoints. The results of subqueries are then joined together to answer the original SPARQL query. A service description language enables the query engine to decompose a query into subqueries. Furthermore, DARQ discusses the use of semi-joins to compute a join between intermediate results at the control site and SPARQL endpoints. The disadvantages are the latency of query execution since live exploration algorithms seek SPARQL endpoints, and repeated data retrieval from each endpoint introduces significant latency. They also have limited possibility for parallelization.  
MIDAS-RDF [18] is a distributed RDF store that is built on top of a distributed multidimensional index structure. Through the multidimensional index, it is able to support an efficient range search over the overlay network. It features the fast retrieval of RDF triples satisfying various pattern queries by translating them into multidimensional range queries, which can be processed by the underlying index in hops logarithmic to the  
Figure 2: Comparison between solid-state drive (SSD) and hard disk drive (HDD).  
number of peers. MIDAS-RDF achieves better performance by using a labeling scheme to handle expensive transitive closure computations efficiently. It also implements reasoning services for the RDFS entailment regime. However, MIDAS-RDF is limited to sharing, querying, and synchronizing distributed RDF repositories.
## 3 Results and Discussion  
### Multilayer Membrane of Humidity Responsive Deforming  
Due to the polymer chain in Nafion, it can rapidly exchange with water and form a large number of water transport channels. As the humidity increases, Nafion absorbs moisture and swells very quickly and efficiently, and moisture can be quickly removed because of the intrinsic connection of sulfonic acid groups when humidity is reduced [42; 43]. As shown in Figure 2a, once the relative humidity in the box was increased, Nafion layer absorbed moisture and swelled, while the middle polypropylene layer and the PDMS/Graphite layer remained substantially as a hydrophobic layer, so the multilayer membrane bent toward the PDMS/Graphite side, as shown in Figure 2b. Moreover, Figure 2c,d illustrate the Nafion film exhibits a water CA of 79.97\({}^{\circ}\), which is less than that of mixed PDMS/Graphite layer (123.64\({}^{\circ}\)), this obvious difference in hydrophilicity causes asymmetric deformation of the multilayer membrane when the ambient humidity changes. When the humidity was lowered, the Nafion layer lost water and shrank, so the multilayer membrane returned to its original state under the elastic recovery force of the polypropylene layer and the PDMS/Graphite layer. Figure 2e shows the rising tendency of the maximum bending angles of the multilayer membrane with the humidity increasing from 30% to 95%. Furthermore, Figure 2f illustrates the multilayer membrane reached its maximum bending angle of 224.56\({}^{\circ}\) within 5 s under conditions of 95% humidity. This fast, large-scale responseto moisture and recovery characteristics indicates the multilayer membranes have great potential applications in an environment of varied humidity.
## 5 Discussion
### Satellite Window  
In the case of Sentinel-6, the window width of 48.55 m results from the Range Migration Compensation (RMC) mode, which reduces the data transmission rate to Earth by 50% [9]. In the RAW mode, the window width is approximately 97.15 m. The operational mode and application of the RMC algorithm at each location are specified by an onboard mask [9]. It is noteworthy that a reservoir's historical WL\({}_{t}\) range being larger than 40 m is not uncommon in the Ebre basin, as evidenced by reservoirs such as Itoiz (52 m), Cavallers (55 m), Canelles (75 m), and San Salvador (44 m). Therefore, hydrological studies could greatly benefit from deactivating the RMC algorithm over inland water targets.  
Regarding the satellite's window position, for Sentinel-3A/B and Sentinel-6, it is determined by the Open Loop Tracking Command (OLTC), which provides a global list of water targets and their corresponding window positions [39]. While this global list aids in tracking historically identified targets, ensuring accurate window positions over all targets poses challenges, as demonstrated in our study. While data users can adjust window positions via the official OLTC portal, operational updates are infrequent and unscheduled.  
Pre-recording window positions of selected targets complicates the observation of unidentified water targets. This issue was previously addressed by the Close Loop approach, which automatically updated the window position in real time by analyzing obtained waveforms. A potential solution could involve combining aspects of both the Open Loop and Close Loop approaches. Such a hybrid method could ensure accurate retrieval of selected water targets, facilitate exploration of new targets, and enable analysis of extreme events.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### FlexRay Parameters  
This module provides some parameters required of a message when the message \(M_{i}\) is transmitted over the static segment of FlexRay:  
* Length of FlexRay communication cycle (\(L_{\text{{FC}}}\)): the length of one communication cycle in FlexRay;
* Length of static segment (\(L_{\text{{ST}}}\)): the static segment length of a FlexRay communication cycle;
* Number of static slots (\(N_{slot}\)): total number of slots in the static segment, within the range 1-1023;
* Length of static slot (\(L_{slot}\)): the length of a slot in the static segment, \(L_{slot}=L_{\text{{ST}}}/N_{slot}\);
* Testing time (\(\tau\)): the time required to test fault tolerance in the system. \(\tau\) is typically one hour (3,600,000 ms);
* System reliability goal (\(SR\)): reliability goal for the system. The reliability goal for a communication system (\(SR\)) means that the reliability of the system is necessary for transmitting a message.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### Application of MC Method for TD-FT  
Monte Carlo simulations were also employed for forward calculations of TD-FT [218; 219]. The perturbation MC (pMC) and adjoint MC were found to be appropriate for generating time-gated TR-Jacobians, and the positions and lifetimes of fluorophore targets were reconstructed using data of multiple time gates.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**8 min (95\%)**} & \multicolumn{3}{c}{**16 min (95\%)**} & \multicolumn{3}{c}{**8 min (68\%)**} & \multicolumn{3}{c}{**16 min (68\%)**} \\ \cline{2-13}  & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline OFF & 104.8 & 122.8 & 247.2 & 51.3 & 59.7 & 137.7 & 32.1 & 38.7 & 82.0 & 23.0 & 29.1 & 58.6 \\ PW & 59.2 & 68.7 & 136.9 & 42.0 & 52.0 & 97.6 & 25.3 & 30.5 & 57.0 & 18.2 & 22.9 & 41.0 \\ RW & 59.3 & 68.2 & 136.8 & 42.5 & 55.5 & 98.0 & 25.4 & 31.4 & 57.0 & 18.4 & 24.3 & 41.1 \\ AD & 59.2 & 68.7 & 136.9 & 42.0 & 52.0 & 97.6 & 25.3 & 30.5 & 57.0 & 18.2  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the study, made formal analysis, wrote the original draft; [PERSON] made some data analysis and visualization, and interpreted the results; [PERSON] contributed to the project administration; [PERSON] polished the manuscript. **Funding:** This study is supported by the Chinese Scholarship Council, Technical University of Berlin, German Research Centre for Geosciences Potsdam, and Institute of Space Sciences of Shandong University. This work is also funded by National Key Research Program of China \"Collaborative Precision Positioning Project\" (2016 YFB0501900), State Key Laboratory of Geo-Information Engineering, China (SKLGIE2017-Z-2-2), National Natural Science Foundation of China (41874032, 41731069, 41574013, and 41574025). **Acknowledgments:** The authors would like to thank the MGEX network for providing open access multi-GNSS observations. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Antenna Performance Measurements  
This section includes the antenna and MIMO performance parameters. First, the antenna performance parameters such as S-parameters, total efficiency, and radiation patterns are described; followed by diversity and MIMO performance matrices like ECC, MEG, DG, and channel capacity (throughput) calculation.
## 5 Discussion and Summary
However, there is a need for providing an efficient cooperative mechanism that would take all the performance metrics into consideration by maximizing packet delivery ratio, coverage, selfish detection, security and reliability, at the meantime minimizing energy consumption, delay and routing overhead.
## 1 Introduction
### Prior Works
The following four aspects were included: a hybrid network based on the Combined CNN and Gated Recurrent Unit (GRU) to absorb dimensional abstract features, three GRU networks with different structures designed for meeting prediction requirements, enhanced multi-task learning with homoscedastic uncertainty (HUMTL) for better predictions with different load variations, and the ensemble approach based on gradient boosting regressor tree (GBRT) for final prediction results of various energy features learning in different degrees. In addition, ensemble learning has been commonly used in wind energy forecasting. Due to the uncertainty and fluctuations in wind speed, it is difficult to estimate wind power with high accuracy. The research by [34] investigated an innovative method that combines Complete Ensemble Empirical Mode Decomposition (CEEMD) and Stacking-ensemble learning (STACK) based on five ML algorithms to predict the wind power of wind farm turbines. In fact, their model was an efficient and accurate model for wind energy prediction. [PERSON] et al. [35] also proposed the hybrid system, including bilinear transformation, effective data decomposition techniques, LSTM-RNN, and error decomposition correction methods, for wind direction prediction. The stability and performance of the proposed system were verified using different data collected from different wind farms. The results of the calculations indicated that the proposed hybrid method worked better than other individual techniques on the short-term horizon. Consequently, considering prior works mentioned above, this research also aims to improve the accuracy of STLF by using the ensemble method.
# Human Activity Recognition Using CSI Information with Nexmon
## 10 Conclusions  
Many of us have easy access to Wi-Fi-enabled hardware. There are enough radio waves in an indoor environment, which could be useful for HAR applications. Unlike wearable devices or supervised CCTVs, Wi-Fi-based HAR methods do not violate privacy concerns.  
This research work was focused on finding the cost-effective, viable hardware option to collect the CSI data and different available hardware options for CSI data collection. The reason is that there are not many options available where the internet provider-owned home Wi-Fi router could be patched with special firmware. The use of hardware such as Raspberry Pi 3B+, 4B, and Asus RT-AC86U was demonstrated as an excellent choice for the CSI data collection using open-source tools such as Nexmon.  
The utilization of some of the denoising techniques makes the data useful for machine learning, as explained in this work. CSI information yielded numerous waveform characteristics and acted as an invisible mirror to present the variation that happened in the surroundings unnoticed.  
There were two ML algorithms applied for the classification of human activities. SVM with features matrix and the use of LSTM to learn the features automatically performed remarkably excellent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SVM** & **LSTM** & **LSTM** & **LSTM** & **LSTM** \\ \hline  & **Experiment 1.1** & **Experiment 1.1** & **Experiment 1.2** & **Experiment 2** & **Experiment 3** \\ \hline EMPTY & 98.9 & 91.5 & 99.7 & — & 98.1 \\ \hline LYING & — & — & — & — & 98.0 \\ \hline SIT & 95.0 & 91.0 & 99.7 & 100.0 & 96.2 \\ \hline SIT-DOWN & — & — & — & 100.0 & — \\ \hline STAND & 95.5 & 88.8 & 97.0 & 96.2 & 94.1 \\ \hline STAND-UP & — & — & — & 96.2 & — \\ \hline WALK & 96.4 & 96.9 & 99.4 & 100.0 & 100.0 \\ \hline FALL & — & — & — & 100.0 & — \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of predicted precision matrix for all experiments.  
It can be observed that the HAR classification based on CSI data delivers reliable results irrespective of the hardware used for CSI data collection. The overall performance in accuracy achieved was between 97% and 99.7% (Raspberry Pi) and 96.2% and 100% (Asus RT-AC86U), for the best models.