esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement  
The latent heat flux (LE), sensible heat flux (H), and net ecosystem exchange (NEE) were measured using an EC system comprising a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open-path infrared CO\({}_{2}\)/H\({}_{2}\)O analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The EC system was installed at a height of 30 m on a tower situated at the center of the plantation. Raw data were collected at 10 Hz and stored in a data logger (CR5000, Campbell Scientific Inc.). The distance from the tower to the nearest boundary of the plantation is approximately 6 km, ensuring that the measured signal originated from the plantation.  
Air temperature (T\({}_{\mathrm{a}}\)) and relative humidity (H\({}_{\mathrm{a}}\)) were measured using seven T\({}_{\mathrm{a}}\)-H\({}_{\mathrm{a}}\) sensors (HMP45C, Vaisala Co., Ltd., Helsinki, Finland) at heights of 5, 8, 11, 14, 18, 26, and 32 m. A net radiometer (CNR1, Kipp and Zonen B.V., Delft, The Netherlands) was used to collect solar radiation (S\({}_{\mathrm{r}}\)) and net radiation at a height of 17 m. The soil heat flux at a 5 cm depth was measured using three soil heat flux plates (HFP01 SC, Huksellux  
Figure 1: Location of the study site (**a**) and the observation tower (**b**).  
B.V., Delft, The Netherlands) around the tower. The instruments were connected to a data logger (CR1000, Campbell Scientific Inc.), and the mean data were stored at 30 min intervals. Photosynthetically active radiation (PAR) was collected at 30 min intervals using a PAR sensor (LI190 SB, Li-COR Inc.) connected to a separate data logger (CR1000, Campbell Scientific Inc.) located 750 m from the tower at a standard meteorological station. The annual precipitation (P), annual S\({}_{r}\), and annual mean T\({}_{a}\) during 1960-2019 were collected from the Jiyuan National Meteorological Station located 15 km from the tower. The trends in the annual S\({}_{r}\), annual mean T\({}_{a}\), and annual P during 1960-2019 are shown in Figure 2. The monthly mean normalized difference vegetation index (NDVI), an indicator of vegetation characteristics, was downloaded from the World Meteorological Organization ([[http://climexp.kruni.nl/start.cgi](http://climexp.kruni.nl/start.cgi)]([http://climexp.kruni.nl/start.cgi](http://climexp.kruni.nl/start.cgi)), (accessed on 15 May 2021)).
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
###### Abstract  
Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models.  
fault; fuzzy; kalman filter; parameter estimation +
Footnote †: journal: Applied Sciences
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### Second Stage: Path Planning
#### 3.2.3 Determine Navigation Route from Reference Points
\tag{4}\]

where _distance_ refers to the pixel-distance of point \((x,y)\) in the image to the closest obstacle. The final _Speed_ value is set to value 1 when the pixel corresponds to an obstacle, whereas it has a value higher than 100 in those pixels which correspond to the class \"path\". The final procedure carried out to calculate the _Speed_ image is detailed in Algorithm 11. ```
Add detected tree lines to the path binary mask Path  Select Non-Path points with at least one neighbor which belong to path: ObstablePoints  Set initial Speed values: Speed\((x,y)=1\)\(\forall\)\(x\in[1,\textit{TotalWidth}]\)\(y\in[1,\textit{TotalHeight}]\) forx=1:TotalWidthdo fory=1:TotalHeightdo ifPathMask(x,y) belongs to Path then  Calculate Euclidean distance to all ObstaclePoints  Select the lowest distance value: \(mindistance\)\(Speed(x,y)=100+mindistance^{3}\) endif endfor endfor Return: Speed
```

**Algorithm 11**\(Speed\) = CalculateSpeed\(\{\textit{Path}\,\ \textit{,\ \textit{\textit{\textit{\textit{\textit{\textit{\textit{\textit{ \textit{\textit{\



The final _Distances_ matrix obtained is used to reach the destination point from any other position of the image with an iterative procedure. The procedure carried out to navigate from any point to another determined point is given by Algorithm 12. First, the initial position (position where the robotic system is currently placed) is assigned to the path; then, the algorithm seeks iteratively for the neighbour with lowest distance value until the destination is reached (_Distance_ value equal to 0). This algorithm will provide the path as a sequence of consecutive points (coordinates of the distance image). Since the geometry of the satellite imagery is well known, the list of image coordinates can be directly translated to real-world coordinates. This information will be used by the low-level navigation system to move the system according to the calculated path. ```
\(Distances=FastMarching(DestinyPt,Speed)\) \(CurrentPoint=InitialPt\) while\(Distances(Currentpoint.x,Currentpoint.y)>0\)do  Search the neighbor with lowest Distance Value: NewPoint \(CurrentPoint=NewPoint\)  Add \(CurrentPoint\) to PathPoints endwhile Return: PathPoints
```

**Algorithm 12**\(PathPoints=\)NavigationPt2 Pt\([InitialPt,DestinyPt,Speed]\)

Finally, the final full path that the robotic system should follow consists of reaching sequentially all the intermediate points previously determined. This strategy is fully described in Algorithm 13, but it is only valid for the case of using only one single robotic system in the grove. For more than one robot present in the grove, an advanced algorithm is needed. Algorithm 14 introduces the strategy for two robots when they are close to each other.
## 6 Numerical Experiment
### Results and Discussion: Comparison of the GMM and IDNN
\begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline
**Month** & **Data** & **GMM-SVC** & **GMM-RF** & **GMM-DNN** & **IDNN-SVC** & **IDNN-RF** & **IDNN-DNN** \\ \hline \multirow{3}{*}{January} & Train & 0.631 (0.008) & 0.597 (0.011) & 0.415 (0.027) & 0.907 (0.019) & 0.794 (0.017) & 0.350 (0.039) \\  & Test & **0.336 (0.013)** & 0.331 (0.011) & 0.310 (0.012) & 0.323 (0.014) & 0.298 (0.012) & 0.268 (0.018) \\  & Param. & (200) & (60) & (180) & (2, 200) & (2, 200) & (2, 200) \\ \hline \multirow{3}{*}{February} & Train & 0.575 (0.033) & 0.599 (0.016) & 0.390 (0.030) & 0.907 (0.017) & 0.785 (0.024) & 0.313 (0.033) \\  & Test & **0.298 (0.010)** & 0.296 (0.008) & 0.276 (0.009) & 0.296 (0.009) & 0.270 (0.009) & 0.236 (0.012) \\  & Param. & (160) & (50) & (200) & (2, 200) & (2, 150) & (2, 200) \\ \hline \multirow{3}{*}{March} & Train & 0.482 (0.065) & 0.635 (0.035) & 0.310 (0.020) & 0.905 (0.058) & 0.812 (0.029) & 0.285 (0.036) \\  & Test & 0.234 (0.006) & **0.243 (0.004)** & 0.221 (0.007) & 0.226 (0.007) & 0.224 (0.006) & 0.205 (0.011) \\  & Param. & (120) & (180) & (120) & (2, 200) & (3, 200) & (2, 200) \\ \hline \multirow{3}{*}{April} & Train & 0.453 (0.080) & 0.590 (0.035) & 0.326 (0.028) & 0.712 (0.203) & 0.743 (0.059) & 0.290 (0.037) \\  & Test & 0.233 (0.005) & **0.244 (0.005)** & 0.227 (0.007) & 0.226 (0.004) & 0.229 (0.006) & 0.216 (0.006) \\  & Param. & (130) & (180) & (190) & (2, 200) & (2, 150) & (2, 200) \\ \hline \multirow{3}{*}{May} & Train & 0.500 (0.095) & 0.599 (0.038) & 0.331 (0.025) & 0.715 (0.222) & 0.756 (0.054) & 0.294 (0.033) \\  & Test & 0.235 (0.004) & **0.250 (0.004)** & 0.230 (0.004) & 0.228 (0.005) & 0.232 (0.004) & 0.220 (0.006) \\  & Param. & (180) & (150) & (190) & (2, 200) & (2, 200) & (2, 200) \\ \hline \multirow{3}{*}{June} & Train & 0.443 (0.102) & 0.586 (0.062) & 0.316 (0.027) & 0.578 (0.195) & 0.736 (0.107) & 0.285 (0.040) \\  & Test & 0.224 (0.004) & **0.238 (0.004)** & 0.221 (0.006) & 0.220 (0.005) & 0.223 (0.004) & 0.211 (0.010) \\ \cline{1-1}  & Param. & (160) & (80) & (160) & (2, 200) & (2, 200) & (2, 200) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average and standard deviation of 20 accuracies on training and test data in \(\theta_{\text{th}}=1.00\) by the combinations of feature extraction methods and ML models for each month. The highest average accuracy and its standard deviation are indicated in bold. The parameters of the GMM and IDNN that yielded the highest average accuracy on test data are indicated. spatial patterns in the image, causing noise accumulation. For sparse trajectories, the lack of structural information on the IDNN may limit its effectiveness. By contrast, GMM-based feature generation identifies RPs on the basis of the distribution of human flows and aggregates trajectories around those RPs. This approach may minimize the accumulation of noise caused by sparsity. As a result, GMM-based features demonstrate superior generalization performance for sparse trajectories.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
###### Abstract  
Volatile compounds play vital roles in food sensory attributes and food quality. An analysis of volatile compounds could illustrate the sensory attributes at the microscale level. Here, untargeted profiling approaches for volatiles in five most-consumed meat species were established using headspace SPME-GC/high resolution Orbitrap MS. An extended high-resolution database of meat volatile compounds was established to enhance the qualification accuracy. Using sulfur-containing compounds, aldehydes, and ketones as the research model, the parameters including fiber coating types, extraction temperature, extraction time, and desorbing time were optimized. Principle component analysis, volcano analysis and partial least squares discriminant analysis were applied to run the classification and the selection of discriminant markers between meat varieties, respectively. Different varieties could be largely distinguished according to the volatiles' profiles. 1-Octen-3-ol, 1-octen-3-one, 2-pentylfuran and some other furans degraded from n-6 fatty acids would contribute to distinguishing duck meat from other categories, while methyl esters mainly from oleic acid as well as dimethyl sulfoxide and carbon disulfide possibly produced from the sulfur-containing amino acids contributed to the discrimination of beef. Therefore, volatiles' profiling not only could interpret the aroma style in meat but also could be another promising method for meat differentiation and authentication.  
differentiation; GC-HRMS; SPME; statistical analysis; untargeted profiling; volatiles 2022
## 4 Numerical Examples
### Uniaxial Compression Test on Rock with a Prefabricated Crack
The phase field method's load-displacement curve closely matches the existing method [14] in peak load and overall trend. In the elastic stage (0-2.0 mm displacement), both have a slope of 3.0 kN/mm (error \(<\) 5%, R\({}^{2}\)\(>\) 0.99), confirming consistent elastic response. At peak load, this method yields 8.5 kN (2.2 mm) vs. 8.4 kN (2.3 mm) for the existing method (1.2% relative error), validating the accuracy and reliability of simulating uniaxial tensile fracture mechanics. The simulation results capture the dynamic evolution of the phase field variable \(\phi\) during crack initiation, propagation, and penetration, demonstrating that the phase field model effectively resolves crack growth characteristics in the complex failure process of prefabricated-crack rock. This framework reveals the failure mechanisms of stress concentration, energy release, and progressive damage accumulation under uniaxial compression. Consistent with theoretical failure laws for compressed rock containing prefabricated cracks, these results validate the efficacy of the proposed phase field model in simulating rock failure processes. Figure 8: Uniaxial compression test phase field simulation results for rock with a prefabricated crack: (**a**) 250 steps; (**b**) 500 steps; (**c**) 750 steps; (**d**) 1000 steps.
# Cocrystal Prediction Using Machine Learning Models and Descriptors
## 5 Conclusions  
To address the co-crystal prediction problem, we extracted molecular descriptor values using the Mordred tool and performed preprocessing. We performed experiments on molecular descriptor values extracted from our collected data, and found that the ANN model was the best among the various known machine learning models. In particular, ANN gave the best recall 0.800 of the'success' label and the best F1 score of 0.817. This implies that the ANN finds about 80% of co-crystal formation without losing too much precision. We also found that the model achieved comparable performance with only half of the descriptor values (i.e., selected molecular descriptor values), and explained that these selected molecular descriptors are related to the results of some previous studies; for example, the module'Estate' refers to the electrostatic interaction between atoms, which is known to be important in pharmaceutics. We believe that this study will be helpful in the process of co-crystals development. In the future, we will examine to collect more data as the size of adequate data is crucial to develop better machine learning models.  
Conceptualization, M.-J.L., G.L.M. and G.J.C.; Data curation, M.-J.L. and P.K.; Investigation, M.E.M.; Methodology, M.E.M., J.K. and [PERSON]-S.J.; Resources, G.J.C.; Software, G.L.M.; Supervision, Y.-S.J.; Writing--original draft, M.E.M., M.-J.L. and [PERSON]-[PERSON]; Writing--review & editing, M.-J.L. and [PERSON]-S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-01108, Big data-based development of novel solid forms for P-CAB drugs and establishment of dedicated AI platforms) This work was supported by the Soonchunhyang University Research Fund.  
**Data Availability Statement:** The data presented in this study are openly available in the website at [[http://ant.sch.ac.kr/](http://ant.sch.ac.kr/)]([http://ant.sch.ac.kr/](http://ant.sch.ac.kr/)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON] et al. [16]** & **[PERSON] et al. [66]** & **Our Work** \\ \hline Total \# of features & 391 & 78 & 2207 \\ Feature generation tool & RDKit & DeepChem [67] & Mordred \\ Feature selection & - & - & RFE, Kbest \\ Best model & SVM & ANN & ANN \\ Best accuracy (\%) & 64.0 & 80.0 & 82.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of comparison with recent studies.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 2 Proposed RMLP-18T Cell and Its Operations
### Memory Operation of the Proposed RHLP-18T Cell
#### 2.1.3 Write Mode  
The write operation is initiated by driving WL to VDD and WWL to GND, activating all access transistors (N5, N6, P11, P12). Consider writing a '0' into a cell currently storing '1' (Q = 1, QB = 0), for which BL is driven to GND and BLB to VDD. Node Q is pulled down towards '0' as the active access transistor N6 provides a path to BL (GND). Simultaneously, node QB is pulled up toward '1' as the active write-assist transistor P11 provides a strong path to BLB (VDD). These driving actions through the activated transistors directly force the storage nodes Q and QB to flip their states, thereby completing the write '0' operation. The three operating states--hold, read, and write--are illustrated in the memory operation waveforms shown in Figure 3.
## 1 Introduction
Thus, prolonged exposure to abnormal atmospheric conditions can lead to changes in brain structure and function, as well as psychological issues [7; 18]. Oxygen saturation in the body can be measured noninvasively through peripheral monitoring or invasively by drawing arterial or venous blood samples [22; 23; 24]. Additionally, changes in cerebral oxygen saturation (COS) can be measured indirectly using blood oxygenation level-dependent functional magnetic resonance imaging [25]. However, this method has limitations, such as being invasive, costly, and time-consuming. These limitations can be addressed using functional near-infrared spectroscopy (fNIRS), which noninvasively measures the COS and Hb concentrations in the frontal lobe of the brain in real time. fNIRS operates on the principle that, when infrared light with wavelengths of 650-925 nm penetrates the brain, light? 790 nm is strongly absorbed by oxyhemoglobin (HbO), whereas light! 790 nm is primarily absorbed by deoxyhemoglobin (HbR). This enables the analysis of cerebral blood flow and energy metabolism [26]. The primary region for fNIRS measurement is the frontal lobe, where [PERSON]'s area, which is responsible for language function, is located. The frontal lobe is also heavily involved in emotion regulation, decision-making, social behavior, and higher-order cognitive functions [27; 28]. Furthermore, fNIRS can be used to indirectly assess the activity of the entire brain because the frontal lobe is connected to the default mode network and other cerebral areas [29]. These neural networks are typically active when the brain is not focused on external stimuli, such as during rest or internal self-directed thought processes [29]. The brain is highly sensitive to changes in atmospheric concentration, which can cause physiological changes throughout the body. However, the application of appropriate concentrations for specific durations can have positive effects, including therapeutic benefits. Therefore, this study analyzed changes in the O2 saturation and Hb concentration in the frontal lobe of the brain using fNIRS following the application of controlled O2 and CO2 concentrations. We also investigated the effects of these changes on brain function to provide insights that may aid in the maintenance of cerebral blood flow and the prevention of cerebrovascular diseases.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 2 Related Work
Without extra hardware and measurement, the range-free methods are not very accurate compared with the range-based schemes.
# Analysis and Test of the Tillage Layer Roll-Type Residual Film Recovery Mechanism
###### Abstract  
With the extensive use of plastic film mulding in planting, the amount of residual plastic film in soil has been increasing, causing soil and water pollution, harming crop growth, and reducing agricultural product quality and yield. In response to this issue, this study proposes a roll-type residual film recovery mechanism using the tillage layer method. The structure and operation principles of this system are discussed, and a detailed analysis of its key components and working process is provided. The lifting cutter operates at a speed of 22.37 rad/s, the pick-up drum at 4.58 rad/s, the roll forward rotation picks up the film at 13.74 rad/s, and the roll reverse film rotation unloads the film at 17.57 rad/s, with the unloading wheel rotating at a speed of 4.5 rad/s. ADAMS (software of automatic dynamic analysis of mechanical systems) Version No.2019 is used for modeling and analysis, and the displacement and velocity change patterns of MARKER_499, MARKER_500, and MARKER_505, which are marked points of the spring-tooth tips and are found to be the same. The maximum resultant displacement of MARKER_499 and MARKER_500 is 22.146 mm when picking up plastic film and 17.047 mm when unloading plastic film. Meanwhile, the maximum resultant displacement of MARKER_500 and MARKER_505 is 231.715 mm in the film-picking area and 234.028 mm in the film-unloading area. After analyzing the velocity of MARKER_499 during picking and unloading of the film, it was determined that the absolute velocity for the picking direction was 79,809.407 mm/s, and for the unloading direction it was 10,2266.168 mm/s. Bench tests show a tillage gathering rate of 71.6% and a surface gathering rate of 83.4%, meeting the performance requirements of the roller-type residual film recovery mechanism. These findings provide a theoretical basis for the design of the structure and operational parameters for the roll-type residual film recovery mechanism using the tillage layer method.  
residual film recovery; winding up roller; low layer residual film; operational parameters; simulation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Custom PD-AFO Design and Production
#### 2.1.1 Foot and Leg Scanning  
In order to obtain the shape and dimension of the custom AFO, a weight-bearing scan of the foot and leg [17] is performed. The subject stands on a 15 mm thick glass plate, and a Kinect depth sensor (Microsoft, Redmond, WA, USA) is manually rotated by 360 deg to obtain a full 3D representation of the foot plantar surface in weight-bearing. A scan of the leg in the same weight-bearing posture is also obtained by moving the Kinect sensor around the subject. A commercial software (Skanect version 1.8, Occipital, Inc., San Francisco (CA), USA) is used for the raw 3D point cloud data acquisition up to 21 fps. The raw 3D data are preprocessed in Skanect, and the STL files are then imported into Geomagic Control (Geomagic Control version 12; 3D Systems, Rock Hill, SC, USA) for finer editing, by removing possible noise and undesired parts. The 3D scans of the foot and leg are merged to create a full 3D representation of the weight-bearing lower limb (Figure 2), which is used to design the custom AFO.  
Figure 1: Schematic representation of the sagittal-plane components of the gravitational and inertial forces acting on the foot during the swing phase of gait. The acceleration due to the foot/leg swing (a) and to the gravity (g) applies a plantarflexion moment (M) to the ankle that must be sustained by the dorsiflexion muscles.