esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Detection in Receiver
### Channel Coding
#### 3.3.2 LDPC Coding  
We employ low-density parity-check (LDPC) codes [26] to add redundancy to the transmitted binary sequence. LDPC codes have recently attracted a great deal of research interest, because of their excellent error-correcting performance and linear complexity decoding. Binary LDPC codes are now known to be capacity approaching on various channels when the block length tends to infinity. LDPC codes can be decoded by an iterative message-passing (MP) algorithm which passes messages between the variable nodes and check nodes iteratively. If the messages passed along the edges are probabilities, then the algorithm is also called belief propagation (BP) decoding, which is the optimal if there are no cycles or cycles are ignored. We propose to measure the BER performance of EBPSK system by using SVM detector with posterior probability output after a LDPC channel decoder has detected the received sequence. The procedure of LDPC decoding is as follows:  
1. Initialization:  
\[p_{n}^{0}\big{(}x\big{)}=q_{nm}^{0}=p\big{(}x_{n}=x\mid y_{n}\big{)} \tag{20}\]  
where \(p\big{(}x_{n}=x\mid y_{n}\big{)}\) is the PPEs of detector outputs.  
2. Horizontal Step: the MAP output from \(c_{m}\) to \(v_{n}\):  
\[r_{mn}^{k}\ \big{(}0\big{)}=\ p\ \big{(}v_{n}=0\mid c_{m}=0\,,\,y_{i \in B\ \big{(}m\big{)}_{i}\,,n}\big{)}\,\] \[r_{mn}^{k}\big{(}0\big{)}=\frac{1}{2}+\frac{1}{2}\prod_{i\in B \big{(}m\big{)}_{n}}\!\!\!\!\!\
# Does Power Output at Critical Power Intensity Interchange between Cycling and Running?
## 3 Results  
Table 2 shows the values of both absolute power (W) and relative power to body mass (W/kg) of the triathletes during the 9/3 Stryd CP test of running. The absolute and relative power values were higher in the 3-min effort than in the 9-min effort, these efforts being performed at 110.11 \(\pm\) 13.36% CP and 104.01% CP, respectively. The determination of CP in the triathletes was carried out at 301.8 \(\pm\) 41.5 W and 4.23 \(\pm\) 0.51 W/kg.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **9-Min Effort** & **3-Min Effort** \\ \hline Distance (km) & 2.52 \(\pm\) 0.21 & 0.93 \(\pm\) 0.08 \\ Absolute Power (W) & 313.9 \(\pm\) 35.5 & 332.3 \(\pm\) 40.3 \\ Relative Power (W/kg) & 4.40 \(\pm\) 0.40 & 4.66 \(\pm\) 0.52 \\ \% Critical Power & 104.01 \(\pm\) 11.75 & 110.11 \(\pm\) 13.36 \\ Critical Power (W) & & 301.8 \(\pm\) 41.5 \\ Critical Power (W/kg) & & 4.23 \(\pm\) 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Critical Power in running, determined with the 9/3 Stryd Test. Mean \(\pm\) SD.  
The absolute (W) and relative to body mass (W/kg) power values of the triathletes during the 9/3 Stryd CP test in running are shown in Figure 2. In the 3-min effort, the triathletes achieved the highest absolute (332.3 \(\pm\) 40.3) and relative power (4.66 \(\pm\) 0.52) records. In the 9-min effort, the absolute (313.9 \(\pm\) 35.5) and relative power (4.40 \(\pm\) 0.40) records were lower. The CP values obtained from the Stryd CP calculator were lower than both the 3-min and 9-min values.  
Figure 3 shows a comparison of the CP values between the cycling and running disciplines. There are significant differences and large ESs between the cycling and running CP in both absolute watts (W) (\(p\) < 0.001; ES = 1.29) and watts relative to body mass (W/kg) (\(p\) < 0.001; ES = 1.25). The running CP (301.8 W \(\pm\) 41.5 W) was 20.2% overestimated compared with the cycling CP (251.1 W \(\pm\) 37.0 W). Linear regression analysis showed that cycling power output explains only 26.7% of the running power output (\(R^{2}\) = 0.267; \(p\) = 0.284).
###### Abstract  
In seasonally frozen areas, physical and mechanical soil properties change dynamically under the effect of the freeze-thaw cycles (FTCs), which is a problem that cannot be ignored in geotechnical engineering. In order to study the effect of the FTC on the strength and the mechanism of deformation and failure of saline soil, this paper took Nong'an saline soil as the research object. In total, 105 groups of remolded samples with different salt contents (S) after FTCs were examined in unconsolidated-undrained tests. On the basis of the experiment results, the influence of FTCs on the mechanical properties of Nong'an saline soil was analyzed. The failure principal stress difference \((\sigma_{1}-\sigma_{3})_{f}\) and cohesion (c) were both decreased with FTC. This occurred especially rapidly after the first cycle and became stable between 30 and 60 cycles. The internal friction angle \(\varphi\) increased at first and then decreased. According to experimental data, a modified Duncan-Chang model was established. Compared with the experiment results, this model was reasonable to simulate the stress-strain relationship of Nong'an saline soil. Furthermore, the empirical formulas of Duncan-Chang model parameters were obtained by regression analysis. This provides a theoretical basis for saline-soil foundation and subgrade engineering in seasonal frozen areas.  
saline soil; freeze-thaw cycle (FTC); Duncan-Chang model; failure principal stress difference \((\sigma_{1}-\sigma_{3})_{f}\) cohesion (c); internal friction angle (\(\varphi\)); regression analysis +
Footnote †: journal: Applied Sciences
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 2 Internet Latency
### RTT Measurements with Adversary  
Figure 7 shows the impact of a relay over the Round Trip Time for exchanges between the node in Poland and the one in Oregon. We display an alternance of standard communications and relayed communications going through the node in France.  
It appears that the relay creates a drastic impact on the measured time. Indeed, the RTTs get increased by more than 150 ms.  
For this specific route, the impact on the time caused by the relay is more than enough to efficiently distinguish between a genuine route and a relayed one.  
The impact of a relay may be caused by many factors, such as: the number of traversed routers, the location of the attacker, his proximity to a genuine route, his control over some network equipment, and so forth. This means that there exists one or multiple optimum setups, lowering the impact of a relay to a minimum. With that information in mind, we choose to define our decision process so that it can modify its detection sensibility. By doing that, we provide users a dynamic capacity to face adversaries even in very efficient setups, see Section 3.
## 5 Experiment Analysis  
The proposed methodology was simulated using MATLAB R2017a on a system with Intel 7 th generation 4770 @3.40 GHz, and validated using the dataset as mentioned in Section 1. The dataset was divided into 4 types of ratio images of NIH3T3 and HCT-8 cells. In the experiment analysis to evaluate the proposed methodology's performance, the capability of the foreground cell clustering algorithm (TLBO) was evaluated on the basis of qualitative and quantitative results as described in Section 5.1. Likewise, the region estimation algorithm's ability for cell quantification and in reducing the biasing error was evaluated by comparing it with ImageJ software as described in Section 5.2. All results are shown for 2:0.5 ratio images for reference, and the rest of the ratio images figures, graphs, and tables are provided as supplementary material.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 3 Methodology
### System Architecture  
This system is designed to enable continuous environmental data collection and real-time analysis for stress level prediction. The system architecture is illustrated in Figure 1, which provides a detailed representation of the integration between data collection, processing, and predictive analysis.  
The system comprises the following key components:  
* **IoT Devices:** These devices are equipped with sensors to continuously monitor temperature and humidity levels, which are critical environmental factors influencing emotional states. The real-time data collected by these sensors form the foundation for predicting stress levels by capturing fluctuations in environmental conditions.
* **Data Collection and Processing Platform:** This platform handles the acquisition and preprocessing of sensor data using Python libraries such as pandas and NumPy. The preprocessing workflow includes addressing missing values, normalising temperature and humidity, and calculating z-scores to standardise the data. This ensures the data are clean and ready for machine learning model training and real-time predictions.
* **Data Processing and Machine Learning Framework:** The processed data are utilised to train and evaluate multiple machine learning models, including Logistic Regression (LR), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. Training is performed using a 70-30% train-test data split, with hyperparameter tuning applied to optimise model performance. These models are rigorously evaluated using metrics such as accuracy, precision, recall, and F1 score to ensure dependable predictions of stress levels.
* **Model Deployment:** The trained machine learning models are deployed to provide real-time predictions of emotional states based on live environmental data. The models categorise stress levels into five predefined categories, enabling dynamic assessment of a user's emotional well-being in response to current temperature and humidity conditions.
## 3 Results and Discussion
### Turbulence Model  
Similar to the previous sections, based on the six NACA0012 models, three far-field distances, and two flux types, the below simulation configurations are obtained: 12 m + blunt(ROE/AUSM), 16 m + blunt (ROE/AUSM), 20 m + blunt (ROE/AUSM), 12 m + NACA4 (ROE/AUSM), 16 m + NACA4 (ROE/AUSM), 20 m + NACA4 (ROE/AUSM), 12 m + 132 points (ROE/AUSM), 16 m + 132 points(ROE/AUSM), 20 m + 132 points  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Mean error Ratios under Different Configurations**} \\ \cline{2-7}
**Simulation Configurations** & \multicolumn{3}{c}{**ROE**} & \multicolumn{3}{c}{**AUSM**} \\ \cline{2-7}  & **P/Pt** & **T/Pt** & **U/Pt** & **P/Pt** & **T/Pt** & **U/Pt** \\ \hline SST + blunt & 2.20\% & 4.49\% & 1.53\% & 8.16\% & 13.63\% & 5.25\% \\ \hline SA + blunt & 3.52\% & 4.38\% & 1.98\% & 5.89\% & 6.11\% & 3.63\% \\ \hline SST + NACA4 & 6.23\% & 3.25\% & 2.36\% & 5.96\% & 8.19\% & 2.07\% \\ \hline SA + NACA4 & 5.33\% & 5.24\% & 1.61\% & 5.12\% & 4.57\% & 1.71\% \\ \hline SST + 132 points & 2.14\% & 4.21\% & 1.57\% & 2.78\% & 6.14\% & 1.78\% \\ \hline SA + 132 points & 11.85\% & 8.20\% & 1.66\% & 7.32\% & 5.83\% & 1.26\% \\ \hline SST + 200 points & 8.58\% & 7.83\% & 1.83\% & 3.74\% & 4.06\% & 2.00\% \\ \hline
**SA + 200 points** & **2.54\%** & **1.86\%** & **1.74\%** & 4.79\% & 3.75\% & 1.29\% \\ \hline SST + 264 points & 2.62\% & 4.00\% & 1.65\% & 18.77\% & 17.84\% & 1.62\% \\ \hline SA + 264 points & 8.82\% & 4.45\% & 2.92\% & 6.81\% & 8.17\% & 1.23\% \\ \hline SST + 400 points & 2.21\% & 4.08\% & 1.09\% & 7.75\% & 9.69\% & 1.89\% \\ \hline SA + 400 points & 6.96\% & 2.86\% & 1.32\% & 3.66\% & 4.98\% & 1.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 19: The mean error ratios of P/Pt, T/Tt, and U/Ut of the 16 m far-field distance.  
Figure 26: Comparison of the simulation performances of 16 m far-field distance under different simulation configurations.  
(ROE/AUSM), 12 m + 200 points (ROE/AUSM), 16 m + 200 points (ROE/AUSM), 20 m + 200 points (ROE/AUSM), 12 m + 264 points (ROE/AUSM), 16 m + 264 points (ROE/AUSM), 20 m + 264 points (ROE/AUSM), 12 m + 400 points (ROE/AUSM), 16 m + 400 points (ROE/AUSM), and 20 m + 400 points (ROE/AUSM). Then for each turbulence model, combined with the parameters of Table 8, thirty-six simulations are performed. By comparing the mean error ratios under different configurations, we confirm the corresponding optimal result and the simulation configuration.  
Figure 27: (a) Distributions of \(\mathrm{P}/\mathrm{P}_{\mathrm{t}}\) of 20 m far-field distance; (b) Distributions of \(\mathrm{T}/\mathrm{T}_{\mathrm{t}}\) of 20 m far-field distance.
# BIZON-UGV for Airport Pavement Testing: Mechanics and Control
## 2 Mechanical Design and Analysis
### Numerical Analysis  
The main mechanical part of the platform is the chassis. The UGV platform chassis works under vertical forces that simulate the load of two packs of power batteries and hydraulic devices used in the vehicle as a power control system for measuring actuators.  
The analysis is performed using models developed in the SolidWorks 2018 software package, computing the stress and deformation considering the mesh based on tetrahedral elements [16]. Figure 2 shows the chassis geometry, where each part is made from steel. Figure 3 shows meshes of the mentioned platform chassis component.  
For the purpose of the complete analysis, an upper vertical load is considered as a static excitation computing chassis deformation and stress. Simulations are performed for the force acting on the chassis upper elements. The simulations consisted of finding deformations and stress for the force push F = 3000 kg, as shown in Figure 4.  
Figure 2: Platform chassis geometry.  
The analysis performed for each of the above-mentioned cases shows how working forces excite stress and deformation of the chassis components and indicates areas where the deformation/displacement is dangerous.  
Considering static displacement computation for a 3000 kg load (presented in Figure 4), the maximum value of displacement is only 2.65\(\cdot 10^{-5}\) mm. It means that the construction strongly satisfies its performance.  
Another important element of the mobile platform, a set of actuators with a fixed beam, is examined. The set of actuators creates a platform measuring system for airport pavement testing. The geometry of the subsystem is shown in Figure 5.  
The element is essential from a mechanical point of view because, during the measurement process, the middle hydraulic actuator works under high-pressure conditions. The pressure in the actuator reaches 160 bar, but another actuator also works at about 10% of this value. Figure 6 shows the deformation of applied actuators with mounting beam and fork as a wheel support system.  
Figure 4: Platform chassis deformation.  
Figure 5: Geometry of the measuring system.  
Figure 3: Mesh of the chassis.  
The result of the simulation using the SolidWorks software package is shown in Figure 6. Consideration of the static displacement computation for extremal forces working on the analyzed beam (left side) shows that the maximum value of displacement is only 0.56 mm. In the case of the fork of the wheeled measuring system (right side), the value of maximum displacement is significantly lower and is only about 0.0086 mm. It means that the construction of the measuring system is well-designed and can be applied in the designed UGV platform.
# Design and Material Optimization of Oil Plant Piping Structure for Mitigating Erosion Wear
## 5 Results and Discussions
### Optimize Model  
For designing an optimize piping structure (OP), the simulation results of TDR and ER for all cases were compared, as shown in Figure 11a,b. This comparison aimed to identify the best material choice, optimal pipeline wall thickness, suitable elbow angle, and the most appropriate velocity for the design. Among the material cases for A90 and I90, the I90, where the Inconel 617 alloy was utilized, exhibited the lowest TDR and ER compared with C90. Similarly, in the context of pipeline wall thickness cases for C90-tn and C90-tk, the C90-tk, in which pipeline wall thickness of 14.02 mm and an outer diameter of 87.02 mm were employed in pipeline design, displayed a TDR and ER comparable to C90. Moreover, in the CFD analysis of elbow angle cases C45 and C25, the C25, in which the elbow angle of 25\({}^{\circ}\) was used, demonstrated the least TDR and ER. Furthermore, in the investigation of fluid velocity cases for C90-hv and C90-lv, the C90-lv, in which the fluid velocity was reduced to half of the C90, exhibited a lower TDR and ER when compared with C90.  
Figure 9: Simulation results of C45 and 25 and their comparison with simulation results of C90.  
Figure 11: Comparison b/w TDR and ER among all simulation cases.  
Figure 10: Simulation results of C90-hv and C90-lv and their comparison with simulation results of C90.  
In light of the above findings, a pipe wall material of I90, pipeline thickness (t/D) of C90-tk, elbow angle of C25, and velocity of C90-lv were employed for designing OP, as shown Figure 11(a), and the same inflation meshing size was used as employed in C90 and is displayed in Figure 11(b).  
Upon comparing the C90 model with the OP model, it can be clearly seen in Figure 12(a),b that the TDR decreased from 9.29 \(\times\) 10\({}^{-2}\) m\({}^{2}\)/s\({}^{3}\) to 1.45 \(\times\) 10\({}^{-2}\) m\({}^{2}\)/s\({}^{3}\) and the ER decreased from 1.24 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\)s to 9.91 \(\times\) 10\({}^{-11}\) kg/m\({}^{2}\)s, as shown in Figure 14.  
Figure 14: Comparison of TDR and ER between C90 and OP.  
Figure 12: Design and Meshing of OP pipeline.  
Figure 13: TDR and ER result of OP.
# MARIE: A Context-Aware Term Mapping with String Matching and Embedding Vectors
## 3 Results  
### Dataset and Experiment Setups  
To validate the performance of MARIE, we tested it by mapping 3489 medical terms currently used in Seoul National University Hospital (SNUH corpus) with the Observational Medical Outcomes  
Figure 1: The overall mapping process of MARIE. To find a mapping between the input and the target datasets, MARIE initially compares a clinical term T\({}_{1}\) from the input dataset with a clinical term T\({}_{2}\) from the target dataset. To capture the contextual similarity between T\({}_{1}\) and T\({}_{2}\), it generates BioBERT embedding vectors for both T\({}_{1}\) and T\({}_{2}\) and calculates their vector similarity, based on a cosine similarity measure. Along with their structural similarity, computed from the string matching method, MARIE calculates the weighted average similarity score between T\({}_{1}\) and T\({}_{2}\). By ranking these final similarity scores computed from MARIE, T\({}_{1}\) is mapped to T\({}_{2}\) with the highest similarity score.  
Partnership Common Data Model (OMOP CDM). Among various code standards available in the CDM, we limited our mapping to SNOMED CT, Logical Observation Identifiers Names and Codes (LOINC), RxNorm and RxNorm Extension [39; 40]. With Usagi software and manual reviews from six medical professionals, we created a true mapping between SNUH corpus and the CDM. Given the terms from SNUH corpus as inputs, the performance of MARIE was evaluated on how accurately it could reconstruct this true mapping between SNUH corpus and the CDM. For example, given a term T\({}_{1}\) from SNUH corpus, we calculated its similarity scores with all of the terms in a target dataset and selected the term with the highest similarity score T\({}_{1}^{{}^{\prime}}\) as its predicted mapped term. The mapping between T\({}_{1}\) and T\({}_{1}^{{}^{\prime}}\) was considered correct if it corresponded to the true mapping between SNUH corpus and the CDM.  
For robust evaluation, we tested MARIE on three different artificial target datasets. Each of these target datasets contained 3489 correctly mapped terms, as expressed in the CDM. In addition to these correctly mapped terms, each of these target datasets also contained 5000, 10,000 and 50,000 randomly sampled terms from the CDM, respectively. These random samples included only the terms that were excluded or ignored during the true mapping between SNUH corpus and the CDM. The sampled target datasets increased the number of possible mapping candidates that MARIE needed to explore, thereby introducing additional complexity to accurately mapping the terms in SNUH corpus.  
As for BioBERT, we used the BioBERT-Large v1.1 model from its official Github repository. Without additional model training, we extracted the term embedding vectors from its pre-trained weights.
## 2 Materials and Methods
### Component Decomposition  
The component decomposition method is commonly employed to achieve precise interpretation. This method decomposes complex space targets into simple geometries,  
Figure 2: Flowchart for component recognition.  
performs electric field calculations on each component, and finally obtains the scattering characteristics of the entire target by vector synthesis.  
However, modeling and performing electromagnetic simulations on individual components may insert scattering mechanisms that are not present in the overall target, such as the cross-section illustrated in Figure 3. As shown in Figure 4, the conventional component decomposition method leads to a significant increase of 19.9 dbsm in the average RCS, as cusp bypassing is replaced by specular scattering. In contrast, our proposed component decomposition method allows the scattering mechanism of the components to be largely maintained.  
Consequently, this study does not model the components independently. Instead, it applies ideal absorptive materials to structures that do not participate in electromagnetic calculations. The black portion depicted in Figure 3 symbolizes a perfect black body that exclusively absorbs electromagnetic waves. This method not only avoids adding scattering mechanisms that are absent in the whole target, but also avoids exploring the transmission characteristics of local-to-global scattering. In this way, the purpose of decomposing the target structures is achieved. As shown in Figure 5, the target is divided into three sections: fairing, swept wings, and bottom base. Selection and division of these components are primarily based on their physical and engineering principles. For example, swept wings are critical for aerodynamic performance, and are typically characterized by their planar geometry. Similarly, fairing is often associated with specific shapes and orientations that influence the interaction with electromagnetic waves. By focusing on the electromagnetic behavior of these physically meaningful components, the space target can be identified directly even without imaging.  
Figure 4: Modeling components independently adds other mechanisms that are absent in the whole target.  
Figure 3: Independently modeled component electromagnetic simulation scenario.
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.1 MODIS Data and Preprocessing  
MODIS is a new generation of optical and infrared remote sensing instruments that \"integrate image and spectrum\" in the current world. It is widely used in the carbon cycles of terrestrial ecosystems because of its high time and spectral resolutions. This study uses MODIS normalized difference vegetation index (MODIS NDVI) (MOD13A2), MODIS land surface reflectance (MODIS REF) (MOD09A1), and MODIS LAI (MOD15A2) from NASA ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)), accessed on 13 May 2020) to extract information on bamboo forests in China and simulate the carbon fluxes of bamboo forests in China. The MODIS data are shown in Table 1.  
The MODIS Reprojection Tool (MRT) was used to preprocess MODIS data, such as mosaicking, format conversion, reprojection, and resampling. MOD9A1 was reprojected to the WGS84 coordinate system, and the spatial resolution was resampled to 1 km using the nearest neighborhood method. After resampling, these data were clipped to the boundaries of China.