esa-sceva/satcom-chunk-collection huggingface dataset:
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 2 Materials and Methods
### Test Parameters  
#### 2.4.1 Parameter Selection Criteria  
To model ice friction during navigation, the expedition of the research vessel _Polarstern_ in the Antarctic in 2006 is referenced to analyze vessel motion against ice [41]. A typical case involving the icebreaker repeatedly ramming thick ice was extracted. During the process, the vessel moved along and vertical to the track, and the along- and vertical-track velocities were recorded using the shipboard scientific navigation platform. Assuming that the ice sheet was stable, the speed for ice-hull friction was set equal to the vessel velocity.  
Figure 2a shows the frequency distribution of the along-track velocity of the icebreaker during the ramming process. The positive and negative values denote forward and backward movement respectively. During the horizontal movement, the velocity ranged between \(-1.60\) and \(1.50\) m\(\cdot\)s\({}^{-1}\). Figure 2b shows the frequency distribution of the vertical-track velocity during the ramming process, with positive and negative values denoting upward and downward movement of the vessel respectively. The velocity ranged between \(-1.30\) and \(1.40\) m\(\cdot\)s\({}^{-1}\), and compared with the along-track velocity, the vertical-track velocity adhered much more closely to a normal distribution.  
In addition to ice-hull friction, friction also occurs between broken sea ice blocks. In particularly, because broken ice turn over, ice-hull friction and ice-ice friction occur on different sides of the ice blocks.
# The Tractor-Cart System Controller with Fuzzy Logic Rules
## 2 Tractor and Cart System Modeling
### Hydraulic Hitch Modeling  
An active cart guidance system steers the cart independently and allows the cart to operate independently within the limits of tractor's steering. The hitch steering system is used to control the cart position with respect to changes in the reference pattern. A hydraulic drive was taken as an actuator, mounted on the rear axle of the tractor. The proposed hydraulic hitch design shown in Figure 3 takes the changes within the extension length of a pair of cylinder rods (\(L_{left}\) and \(L_{right}\)). The extension change in cylinder rods leads to the hitch angle (\(\alpha\)) and, subsequently, the hitch arm (R) position changes with respect to tractor's rear axle centerline. Changes in angle (\(\alpha\)) result in changes in the cart position. The hydraulic hitch actuator controls the length of hydraulic cylinder rods, hitch angle (\(\alpha\)), and hitch arm (R). The following geometric parameters for the proposed hydraulic  
Figure 2: Kinematic bicycle model of the tractor-cart system.  
actuator unit were taken: \(d_{nom}=50\) cm, \(L_{H}=80\) cm, \(R=70\) cm and \(r=20\) cm. The turning angle (\(\alpha\)) ranges within: \(\alpha=[-0.5 rad,0.5 rad]=[-28.65^{\circ},28.65^{\circ}]\).  
The hydraulic equation of the cylinder speed is \(Q_{H}=v_{H}A_{H}\). \(C_{d}\) is extraction and its retraction length in one cylinder is equal to  
\[C_{d}=v_{H}dt=\frac{Q_{H}}{A_{H}}\Delta t \tag{18}\]  
where \(v_{H}\) is velocity (retracting or extending motion) of a hydraulic cylinder rod, \(\Delta t\) is a sampling time, \(A_{H}\) is the area of either blind end or rod end side (depends on the direction of hydraulic cylinder motion), and \(Q_{H}\) is the pump flow rate going to the cylinder. \(L_{H}\) is the length interval depends on the position change of the hitch within \(\alpha=-28.65^{\circ}\) and \(\alpha=28.65^{\circ}\), \(L_{P}\) is the arc length changing within \(\alpha=-28.65^{\circ}\) and \(\alpha=28.65^{\circ}\), and r is the distance between two cylinders.  
According to the geometry of hydraulic hitch design \(C_{d}=\frac{r}{2}\sin\alpha\), the angle (\(\alpha\)) of hitch arm is calculated from Equation (19):  
\[\alpha=\sin^{-1}(\frac{2Q_{H}}{rA_{H}}\Delta t) \tag{19}\]  
The length of left and right cylinders of hydraulic hitch is calculated from the next equation:  
\[\left\{\begin{array}{l}L_{left}=d_{nom}+\frac{r}{2}\sin\alpha\\ L_{right}=d_{nom}-\frac{r}{2}\sin\alpha\end{array}\right.=\left\{\begin{array}[ ]{l}L_{left}=d_{nom}+\frac{Q_{H}}{A_{H}}\Delta t\\ L_{right}=d_{nom}-\frac{Q_{H}}{A_{H}}\Delta t\end{array}\right. \tag{20}\]  
The variable \(L_{left}\) and \(L_{right}\) must be controlled with respect to translational speed of the hydraulic cylinder. It should be noted that hitch arm (R) turns to the \(\alpha\) angle due to translational motions of two hydraulic cylinders and angular velocity \(\dot{\alpha}\) is equal to the change of angular displacement values in a unit time as \(\dot{\alpha}=\frac{da}{dt}=\frac{\Delta\alpha}{\Delta t}=\frac{\alpha_{t}- \alpha_{t-1}}{t_{t}-t_{t-1}}\).
## 3 Proposed Method
### Cognitive Emotion Recognition Using Active Learning Based on Complexity and Uncertainty
#### 3.4.1 Uncertainty and Complexity  
\(k\) unlabeled samples were defined as \(\{(x_{1},P_{1}),(x_{2},P_{2}),\ldots,(x_{k},P_{k})\}\). In this study, \(x_{i}\) is the feature vector \(\mathbf{Feature}\) that was described in the previous section and \(\mathbf{P}_{i}\) is the probability vector of the probable class of sample \(\mathbf{x}_{i}\). Since 4 cognitive emotions were defined in this study, \(\mathbf{P}_{i}\) is expressed as follows:  
\[P_{i}=[p_{i,1},p_{i,2},p_{i,3},p_{i,4}],\ \sum_{j=1}^{4}P_{ij}=1 \tag{17}\]  
In order to improve the training speed and classification speed and solve the problem of over fitting, Random Forest (RF) was used to predict the probability vector of each sample. The probability of sample belonging to each cognitive emotion was defined as the proportion of decision tree classification results to the total. RF was naturally used as a classifier in the active learning. C4.5 was used to construct decision trees.  
(1) Uncertainty  
Samples with a large uncertainty often contain more information and are difficult to classify. In order to measure the uncertainty, information entropy was introduced to measure the uncertainty of samples. The formula for calculating the uncertainty of sample \(x_{i}\) is as follows:  
\[UN(\mathbf{x}_{i})=-\sum_{j=1}^{4}p_{ij}\ln p_{ij} \tag{18}\]  
Uncertainty is used to help active learning algorithms find samples that are difficult to classify.  
(2) Complexity  
In classification problems, the correct label often appears in the first two categories with the largest probability. This phenomenon leads to the complexity of samples and makes them difficult to classify. Therefore, this study introduced the complexity of samples, defined as the difference in probability between the class with the largest probability and the class with the second largest probability. The formula for calculating the complexity of \(\mathbf{x}_{i}\) is as follows:  
\[CO(\mathbf{x}_{i})=\bigr{|}p_{i,1 st}-p_{i,2 nd}\bigr{|} \tag{19}\]  
where \(p_{i,1 st}\) and \(p_{i,2 nd}\) denote the largest class probability and the second largest class probability of \(\mathbf{x}_{i}\), respectively. Complexity was used to select the sample that was the most difficult to classify in this round of learning.
## 5 Numerical Experiments
### Analysis of Performance of DP, ar-DP, and PPO
#### 5.3.2 Optimality Gap Analysis  
We analyze the optimality gap of the three algorithms. Table 7 shows the optimality gap for Cases 1 to 7. The optimality gap of DP and ar-DP is 0% in all cases. The optimality gap of PPO is below 10% in all cases except Case 4. In Cases 1, 2, and 3, the optimality gap is less than 1%.
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 3 Overview of the Study Area  
This research utilizes GNSS station data and soil moisture reference data provided by the Plate Boundary Observatory (PBO) for experimental analysis. These include data from station P041 (105.1943\({}^{\circ}\)W, 39.9495\({}^{\circ}\)N) for 2012 from day of year (DOY) 57 to 359, and from station P037 (105.10468\({}^{\circ}\)W, 38.42175\({}^{\circ}\)N) for 2017 from DOY 29 to 302. Additionally, the study also analyzes GPS data and in situ soil moisture data collected from Lamasquere (1.22891\({}^{\circ}\)E, 43.48734\({}^{\circ}\)N) in Toulouse, France, during DOY 35 to 80 in 2014. Data collected at Lamasquere, France, are hereafter referred to by proxy with the LM site. For the LM site, the reference data were collected from two ML3 Theta Probe soil moisture sensors placed approximately 2 m from the ground projection of the antenna phase center, with an accuracy of \(\pm 1\%\), at a depth of 5 cm, and a sampling interval of 10 min. The terrain around the experimental sites is flat and open, covered by low shrubs, with no significant obstacles, facilitating soil moisture retrieval experiments. Figure 4 is a schematic diagram of the surrounding environment of the research areas for the P041 and P037 stations. Figure 5 is a schematic diagram of the surrounding environment of the research area for the LM station.  
The sampling interval of the SNR data used in this study is 15 s. Since the multipath effect is more significant at lower satellite altitude angles, it produces a stronger interference effect at the receiver antenna [19; 20; 21]. For a single site, the first Fresnel zone (FFZ) can be  
Figure 4: Schematic of the location and surroundings of sites P041 and P037.  
Figure 5: Schematic of the location and surroundings of sites LM.  
used to determine the effective sensing area of the site [4]. Therefore, the L2-band SNR data in the range of 5\({}^{\circ}\)-25\({}^{\circ}\) elevation angle are used in this study.  
Figure 6 shows the observed SMC and precipitation data series for station P041 with a DOY of 57-359 in 2012 and station P037 with a DOY of 29-302 in 2017, which are presented in the form of line and bar charts, respectively. As shown in Figure 6, about 10 significant precipitation events occurred at station P041, and about 17 significant precipitation events occurred at station P037 during the test period. During precipitation events, SMC increased significantly, and continuous precipitation resulted in a significant non-linear increase in SMC, which tended to decrease as precipitation decreased or ceased. It can be seen that precipitation is the primary factor causing sudden changes in SMC. The data selection intervals of the two sites contain rich information on rainfall and soil moisture changes, which is suitable for soil moisture research and analysis.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 5 Discussion
The results of the seasonality analysis derived from gauge data and long period signals (IMFs) show dissimilar correlation values between those GPS signals, where the mean correlations for GBGR-CS, CHLT, and VOGH are 0.43, 0.09, and 0.61, respectively. However, thanks to this approach, the results are consistent with theory and aligned with previous studies [71]. In summary, our results deserve further analysis by combining more field data with models that can address the complexities in the interconnected hydrological-glaciological-tectonic Patagonian natural system.
# Multi-Agent Simulation Environment for Logistics Warehouse Design Based on Self-Contained Agents
## 3 Proposed Multi-Agent Simulation Modeling Environment for Logistics Warehouses  
### Strategy of Proposed Simulation Environment  
As mentioned in Section 1, we focus on reducing development costs and achieving modifiability of a multi-agent environment in a logistics warehouse simulation. We designed the multi-agent simulation environment with following features to achieve the above objectives. The features are explained in detail in the following subsections.  
* **Self-contained architecture of agents representing entities in a logistics warehouse**: This architecture makes it easy for the model developer to add, replace, and modify the agents in the environment. We made each agent self-contained and individually executable without the need of installing a specific software development kit (SDK) and sharing information only with messages.
* **Unified message architecture and message primitives for a logistics warehouse**: To reduce the development costs of a logistics warehouse simulation model, we designed a unified architecture of agent messages and pre-defined primitives to make the description of message easier. Our simulation environment has a template of agents that has basic functionalities to process messages with the pre-defined primitives, which reduces the development costs.
## 2 Experimental Apparatus and Setup  
The HEV driving test was conducted in the IM240 mode. From Figure 1, we note that the IM240 mode was set as the default mode in the experiment. Figure 2 shows the chassis dynamometer (eddy-current dynamometer, Jastec, Sungnam, Korea) and hybrid electric vehicle (2011 YF Sonata HEV, Hyundai Motors) used in the vehicle driving experiment. A PicoScope oscilloscope (Pico Technology, Cambridgeshire, UK) and an on-board diagnostics (OBD)-II monitoring system (Cantalker, D & K Information Communication Technology, Sungnam, Korea) were used to measure the charge and discharge current. Table 1 lists the specifications of the HEV used in the present study.  
Figure 1: IM240-mode profile.  
To measure the current in the LIB available for the hybrid drive motor in real-time, we performed current measurements using high-power battery ammeter clamps (9287 Universal Clamp-on CT, Hioki Co., Japan), as shown in Figure 3a. The ammeter clamps were installed on the wires that connect the high-voltage battery and drive motor. The measured current data were stored in the PicoScope (Pico Technology), shown in Figure 3b, and the charge and discharge statuses were analyzed using the measured data. The OBD signals (engine torque, motor torque, current, gear stage, SOC, and engine speed) were acquired using the Cantalker, which is shown in Figure 3c. Figure 4 shows the overall schematic of the experimental setup employed in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Specification** \\ \hline Engine & - & 2.0 L Atkinson cycle \\  & & Double overhead cam \\ Max. power & PS & 150 \\ Max. torque & kg\textminus m & 18.3 \\ Motor & - & 30 kW (41 PS)/20.9 kg\textminus m \\ HSG & kW & 8.5 \\  & & Lithium polymer \\  & & 5.3 A\textminus k\times 270\textminus \textminus \
## 4 Use Case Evaluation
### Green Route: A Use Case Scenario to Find a Sustainable Path
The S2 NeTM aligns and assesses the data from the various entities to find the most environmentally friendly route for [PERSON] to commute to the port. Figure 6 displays an end user application designed for the Green Route use case scenario. The figure illustrates the interface of the application, which allows users to input their starting location and desired destination, and then generates an optimized route based on various factors such as traffic, air quality, and noise. The application also provides real-time updates on the user's progress and environmental impact.
## 3 System Model  
The system model consists of three main modules; i.e., source (S), relay (R), and destination (D), as shown in the Figure 1. Each module is equipped with single antenna.
## 2 Materials and Methods
### Health Risk Assessment  
The potential health risk (according to Risk Assessment Information System [[https://rais.ornl.gov/](https://rais.ornl.gov/)]([https://rais.ornl.gov/](https://rais.ornl.gov/))) of consuming the studied oils was evaluated as the target hazard quotient (THQ). The THQ was calculated using Formula (1) [23; 24]:  
\[\text{THQ}=\frac{\text{EDI}}{\text{RfD}}=\frac{\text{C\cdot IR\cdot ED\cdot EF }}{\text{BW\cdot AT\cdot RfD}}(\frac{\text{mg}}{\text{kg\cdot day}}), \tag{1}\]  
where EDI represents estimated daily intake; C is the concentration of metal in oil samples (mg/kg); IR is ingestion rate (25 g/day); EF, exposure frequency (365 day/year); ED, exposure duration (adults 70 years); BW, body weight (70 kg for adults); AT, average time exposure (25,550 days). In this model, the RfD values were \(5\times 10^{-4}\), \(4\times 10^{-2}\), \(1\times 10^{-3}\), \(3\times 10^{-3}\), \(2\times 10^{-2}\), \(2.4\times 10^{-1}\), and \(2\times 10^{-2}\) mg kg\({}^{-1}\) day\({}^{-1}\) for Pb, Cu, Cd, Cr, Co, Mn, and Ni, respectively [25; 26].  
THQ \(\geq\) 1 indicates that the target heavy metals have a potential non-carcinogenic risk; THQ < 1 indicates that the target heavy metal's non-carcinogenic risk is negligible.  
The hazard index (HI) was determined to estimate the total potential health effects of non-cancer risks. These interactive effects were taken into account given the exposure to a mixture of heavy metals found in the analyzed oils. HI represents the sum of the THQ values for all heavy metals and was calculated using Formula (2) [27]:  
\[\mathrm{HI}=\sum\mathrm{THQ} \tag{2}\]  
If the HI value is <1, consumers are considered to be safe, and if the HI value \(\geq\) 1, the oils are a health hazard to the exposed local population (consumers).  
After calculating THQ and HI it is necessary to calculate the cancer risk (CR or ILCR--incremental lifetime cancer risk). This is performed using Formula (3):  
\[\mathrm{ILCR}=\mathrm{EDI}\times\mathrm{CSF}\times\mathrm{ADAF}, \tag{3}\]  
where CSF is the cancer slope factor (kg.day/mg) used to assess the lifetime probability of a person exposed to chemical agents in terms of carcinogenic risks, and ADAF is the potency adjustment factor for adults and children (1 for adults and 3 for children--dimensionless) [27].  
CSFs for Pb, Cd, Cr, and Ni are 0.085, 0.38, 0.5, and 0.84 kg.day/mg, respectively. In addition, CSFs for Co, Cu, and Mn do not exist, and the ILCR for Co, Cu, and Mn cannot be computed.  
According to U.S. EPA recommendations, the carcinogenic risk for lifetime cancer risk (CR or ILCR) should be in the range of 1 \(\times\) 10\({}^{-6}\)-1 \(\times\) 10\({}^{-4}\) (1 in 1,000,000 to 1 in 10,000) [28].  
In order to calculate the total (cumulative) cancer risk as a result of multiple heavy metal exposure, the ILCR sum was calculated using Formula (4) [23]:  
\[\sum\mathrm{ILCR}=\mathrm{ILCR_{Pb}}+\mathrm{ILCR_{Cd}}+\mathrm{ILCR_{Cr}}+ \mathrm{ILCR_{Ni}}, \tag{4}\]
## 4 Discussion
### High-Temperature Creep Deformation and Mechanisms
According to the time-temperature-transformation (TTT) diagram of Incoloy 800 in [24], carbides continue to precipitate after prolonged exposure at high temperature. It was reported that the fine carbides (Ti and Cr carbides) are precipitated at 550 to 760 \({}^{\circ}\)C in regions of high density of dislocations inside the grains after short periods of thermal exposure, leading to strengthening of the grains. At the grain boundaries, coarse chromium carbides were produced, leading to chromium depletion in their vicinity so that precipitation-free zones (PFZs) caused low strength beside the grain boundaries [24]. In this study, Cr depletion and PFZs were not observed in this region; however, significant amounts of coarse Cr- and Mn-rich M\({}_{23}\)C\({}_{6}\) carbides were present along the grain boundaries, as shown in Figure 17a. In the final stage of the tertiary phase, the nucleation of cavities followed by growth and interlinkage along the grain boundary led to final failure [25]. As observed in both base metal and weldment specimens, cracks developed along a direction perpendicular to the applied load, and intergranular cracking was the more dominant fracture mode in the weldment specimens due to the prolonged high-temperature exposure and extensive carbide precipitates and growth (Figures 19-21). Intergranular cracking is considered to be the dominant fracture mechanism of high-temperature Incoloys [25]. It was reported that creep cavities and intergranular cracks developed in the perpendicular direction of the stress in Inconel 617, and M\({}_{23}\)C\({}_{6}\) carbides were found to form along the grain boundary and inside the matrix at temperatures of 649 to 1093 \({}^{\circ}\)C [26].
# Ontology Attention Layer for Medical Named Entity Recognition
## 2 Related Works  
### Medical Named Entity Recognition  
In the development of medical knowledge graphs, named entity recognition (NER) has received widespread attention as a critical component for constructing medical knowledge graphs. It aims to automatically detect the desired entities from given text.  
Conventional general NER frameworks such as a bidirectional-LSTM [3] or a GPT [4] could be used for medical NER tasks. But medical NER has its own challenges, such as the lack of data and named entity normalization (NEN).  
For the lack of data, some researches used few-shot or zero-shot learning techniques. [PERSON] et al. [5] employed a combination of a small amount of robust labeled data and a large amount of weak labeled data to train the model in stages and mitigate the problem of few-shot learning. [PERSON] et al. [6] introduced a novel approach that used learnable logical rules for weakly supervised NER. [PERSON] et al. [7] introduced a conditional hidden Markov model for multi-source weakly supervised NER. [PERSON] et al. [8] leveraged the naturally occurring facts in the textual descriptions of various entity categories to complete a zero-shot NER task.  
Other research has used external additional information. [PERSON] et al. [9] fused the structural information of Chinese characters to improve the performance of Chinese NER. [PERSON] et al. [10] improved the NER task using external context retrieval and cooperative learning. [PERSON] et al. [11] proposed enhancing the knowledge on the basis of the pre-trained model, which improves the effect of NER, NLU, and NLG.  
NEN involves standardizing different representations of the same medical entity, such as a disease or drug, and it has a substantial impact on NER performance as well. For NEN, some researchers utilize multi-task learning to address named entity normalization in medical NER [12]. [PERSON] et al. [13] proposed a model based on state transition, which transforms end-to-end disease recognition and normalization tasks into action sequence prediction tasks.  
Some research has focused on difficult entity names such as nested names, long names, or discontinuous names. [PERSON] et al. [14] designed an objective function for training neural models to handle nested entity label sequences as suboptimal paths for nested NER tasks. [PERSON] et al. [15] developed a network for long names utilizing both segment-level information and word-level dependencies. [PERSON] et al. [16] addressed the issue of discontinuous text in NER tasks by adopting a fragment graph approach. [PERSON] et al. [17] presented a span-based model capable of identifying overlapping and discontinuous entities, as well as determining whether entity relationships overlap.