esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 2 Study Area and Data
### Data and Processing
#### 2.2.3 Measured Yield Data  
A total of 29 samples were selected from the study area, 15 from the Tongzhou District and 14 from the Shunyi District. The location of the sample area was determined by considering the representative growth status and regional distribution of winter wheat, and the size of each sample area was not less than 100 m \(\times\) 100 m. The area of each sampling point was 1 m \(\times\) 1 m. Five-point sampling was used for sampling. After drying and weighing, the average weight of winter wheat from the five sampling points was used as the yield data for the given sample area.
## Materials and Methods
### Data Processing  
The processing of the data was carried out with EMGWORKS(r) software (Version 4, Delsys, Inc., Boston, MA, USA). Signal filtering was the first step in data processing using a 2 nd-order bandpass Butterworth filter [40] with an attenuation of 40 dB and a cutoff frequency between 20 and 300 Hz [41]. A root mean square (RMS) [42] with a window width of 0.05 s and a window overlap of 0.025 s was later applied to the filtered signal, and the signal offset was removed.
## 4 Discussion
### Connectivity  
To provide insights into the interaction of forest plantations within heterogeneous landscapes, numerous studies have examined changes in landscape pattern and connectivity [56]. The landscape connectivity of dominant land uses changed significantly over time in the Manabi province (i.e., class types and surface of land uses) as result of forest plantations, despite relatively constant dominance of agricultural cover. When we compared connectivity in scenarios considering only deforestation and reforestation, we found that forest plantations contribute to linking native forest fragments, which have a middle to high conservation value. Thus, as we initially stated in our hypothesis, forest plantations have contributed to improving connectivity, as well as the preservation of native forest remains (Figure 6).  
Threatened species receive priority in conservation planning because, among other factors, their populations are declining due to habitat fragmentation [53]. Therefore, maintaining or restoring connectivity between fragmented habitats has been suggested as the key action to reduce the negative trends for several endangered species populations. Connected habitats more effectively preserve species and ecological functions. According to our results, we found an increase in connectivity due to the new reforested areas. Despite being small, reforestation can act as a steppingstone to connect patches with larger surfaces [57; 58]. This information is important because many key species have connectivity requirements. For instance, jaguarundi (_Puma yogouaroundi_) occupy less human-influenced landscapes and are very sensitive to habitat loss and disturbed areas [53]. As a result, they face a greater risk of extinction [54]. Our research revealed that changes in connectivity due to reforestation activities may generate critical landscape connections for jaguarundi. In this instance, this species would benefit from the use of reforestation strategies that preserve or improve landscape connectivity in the study area. Many species have long-distance movement in the landscape, but others are less mobile and need \"forest islands\" to move around and spread out across the land uses matrix [54]. Thus, habitat connections between strictly Protected Areas and the mosaics of forest plantations could contribute to species conservation.
## 2 Materials and Methods
### Physicochemical Parameter Analysis
#### 2.3.7 Preparation of the DPPH Free Radical Calibration Curve  
A calibration curve of concentration versus absorbance of [DPPH\(\bullet\)] was prepared as follows: The aforementioned standard solution of DPPH was diluted with the addition of methanol. The resulting solutions (range of 0-31 mg L\({}^{-1}\)) were vortexed, left in the dark (until measurements were made), and the absorbance was measured in a UV/VIS Spectrometer (Lambda 25, PerkinElmer, Waltham, USA) at \(\lambda\)max of 517 nm. The calibration curve of absorbance (\(y\)) versus concentration (\(x\)) of [DPPH\(\bullet\)] was expressed by the following equation:  
\[y=0.0243x-0.0001;R^{2}=0.998 \tag{1}\]  
3.8 Determination of in Vitro Antioxidant Activity of _Lavandula stoechus_ Aqueous and Methanolic Extracts  
The antioxidant activity of _Lavandula stoechus_ aqueous and methanolic extracts was estimated in vitro using the [DPPH\(\bullet\)] assay. For the determination of antioxidant activity, a volume of 3.0 mL of [DPPH\(\bullet\)] solution (0.08 mM) was placed in the cuvette (final volume of 3 mL) and the absorbance of the [DPPH\(\bullet\)] radical was measured at \(t=0\) (\(A_{0}=0.7565\)). Subsequently, 0.20 mL of _Lavandula stoechas_ aqueous and methanolic extracts was placed in the respective cuvettes plus 2.8 mL of the [DPPH\(\bullet\)] solution. The absorbance was measured every 15 min (regular time periods) until the value reached a plateau (steady state, \(A_{t}\)). The reaction between the free radical and _Lavandula stoechus_ water soluble and methanolic antioxidants was accomplished at 30 min. The absorbance of the reaction mixture was measured at 517 nm.  
The [DPPH\(\bullet\)] antioxidant activity (%AA) with respect to aqueous and methanolic extract of _Lavandula stoechas_ was calculated using the following equation:  
\[\%AA=((A_{0}-A_{t})/A_{0})\times 100 \tag{2}\]  
where \(A_{0}\) is the initial absorbance of the [DPPH\(\bullet\)] free radical standard solution and \(A_{t}\) is the absorbance of the remaining [DPPH\(\bullet\)] free radical after reaction with _Lavandula stoechas_ antioxidants, at steady state (plateau). For the estimation of _Lavandula stoechas_ effective concentration EC\({}_{50}\), termed as the concentration of antioxidants that could cause a decrease in the DPPH free radical by 50%, further dilutions from the initial extracts (11,333.33 mg L\({}^{-1}\)) were prepared for methanolic and aqueous extract from the initial mother solution in the range 500-11,333.33 mg L\({}^{-1}\) and the value of EC\({}_{50}\) was obtained from graphs of [DPPH\(\bullet\)] remaining versus concentration of extracts. For this antioxidant test, methanol was used as the blank. Prior to absorbance measurements, the extracts were filtered using Whatman filters (Whatman plc, Buckinghamshire, UK) with a pore size of 0.45 \(\mu\)m. Each sample was run in triplicate (\(n=3\)).
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 4 Results
### Efficiency  
We describe the test times of different methods on a server with an Intel E5-2600 CPU, an Nvidia GeForce RTX 2080 Ti GPU, and 64 GB of memory. The calculation time is calculated in milliseconds, with an average of 100 point cloud pairs containing 1024 points. As Figure 4 shows, iPCRNet is the fastest method, with PTRNet second only to iPCRNet. The reason is that the model complexity of iPCRNet, PTRNet and DCP-V2-MLP is from low to high, respectively. Meanwhile, the ICP algorithm requires every point on the data point cloud to find the corresponding point by traversing every point on the model point cloud, so its registration speed is slow.
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 4 Proposed Solution and Efficiency Analysis  
The adaptive notch filter is a great way to remove narrowband interference. However, before being filtered, the center frequency of the interference must be detected in order to adapt the transfer function of the filter. In Figure 18, the signal also must pass through the frequency detector (shown in the red block). Usually, a digital transmitter utilizes a pulse-shaping filter. This type of filtering compresses  
Figure 17: Percentage of error in Doppler estimation based on JSR (PLL Synchronizer).  
the signal into the narrow bandwidth. Those before the detection, downsampling can improve the system resolution. The output of the detector is the estimated center frequency (\(w_{0}\)) of CWI that allows the filter to adapt its frequency to the interference peak. There are different ways of center frequency detection. Two popular methods are the least mean square (LMS) [31] and the frequency domain implemented as FFT [32, 33] or [PERSON]'s method [9] followed by peak detection. This paper mainly focuses on the system performance improvement achieved by notch filtering. The main assumption here is that the center frequency of interference is precisely known by the receiver and the notch filter is adjusted to the same CWI frequency.
## 3 Results and Discussion
### Permeability of MSWC Uniaxial Cyclic Loading and Unloading  
Permeability is a key parameter to measure the permeability performance of soil and rock mass [34; 35; 36], and it is an important indicator to evaluate the aquitard of the spoil field [37]. In this MSWC permeability test, the inlet pressure decay method was used. Compared with other methods, the inlet pressure decay method has a smaller error, higher accuracy, and more convenient calculation. The calculation formula is shown in (1):  
\[k=\frac{\mu V_{0}\Delta P}{A\Delta t}\frac{2h}{P_{\text{mean}}^{2}-P_{0}^{2}} \tag{1}\]  
where \(k\) is the effective permeability, \(A\) is the cross-sectional area of the sample, \(\mu\) is the gas viscosity coefficient, \(V_{0}\) is the volume of the buffer cylinder, \(h\) is the height of the sample, \(P_{\text{mean}}\) is the average pressure in the buffer cylinder, \(\Delta P\) is the pressure change at the inlet end, \(\Delta t\) is the test time, and \(P_{0}\) is the atmospheric pressure. When the permeability of MSWC is less than \(10^{-13}\), the MSWC fulfils the basic conditions for use as a water barrier.  
The permeability of the MSWC with different mudstone proportions is shown in Figure 12. As the mudstone proportion increases, the permeability of the MSWC gradually decreases, and its water-blocking ability gradually increases. Because the mudstone cementing material has stronger water-blocking ability than the slag cementing material, as the mudstone proportion further increases, the uniaxial compressive strength of the MSWC will decrease, but due to the complexity of the mining environment, the reconstructed water-blocking layer not only needs to have a low permeability, but also needs to have sufficient uniaxial compressive strength. Therefore, comprehensively considering the water-blocking ability and uniaxial compressive strength of the MSWC, the optimal ratio of mudstone proportion is \(r=0.6\) for a reconstructed water-blocking layer.  
Figure 13 shows the permeability of the MSWC under different uniaxial cyclic loading and unloading times. As the uniaxial cyclic loading and unloading times increase, the permeability of the MSWC overall shows an increasing trend. Due to the continuous increase of the uniaxial cyclic loading and unloading times, the growth trend of the MSWC permeability gradually slows down, and when the cycle times exceed 100, its permeability growth gradually slows down. Therefore, when the uniaxial cyclic loading and unloading times exceed 100 times, the growth trend of the MSWC permeability slows down, which fulfils the condition of being a long-term water-blocking layer for a mine dump site in engineering applications.  
Figure 11: Microstructure of MSWC specimens under different uniaxial cyclic loading and unloading times (\(n\)). (**a**) \(n=0\). (**b**) \(n=25\). (**c**) \(n=50\). (**d**) \(n=100\).
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 3 Results
### Sensors
#### 3.1.2 Measurement Data Collection Efficiency  
The daily data collection efficiency was determined for each of the four multi-sensor devices operating in our monitoring system. The daily data collection efficiency was defined as the number of seconds during the day (a fraction of 24 h \(\times\) 3600 s) when the complete set of measurement data was recorded by the device. By the complete set of data, we mean the data vector including the responses of all sensors associated with the same time point and the respective time coordinate. The temporal resolution of data collection by the system is 1 s. Table 2 presents the daily data collection efficiency for the individual multi-sensor devices in the testing period using the 5 th, 50 th, and 95 th percentile. The devices were identified by their location.  
As shown in Table 2, during 95% of the days, the data collection efficiency exceeded 99.53% (i.e., 23 h 53 min). The data losses had the form of individual gaps lasting up to several seconds and their occurrence in time was not showing any regularity. The data coverage at the level offered by the monitoring system allows for the exhaustive multivariate analysis of the measurement data in the time domain. Regarding the objectives of the monitoring, it shall be considered satisfactory.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Hydrocarbon Sealing Faults (F1-F6)
#### 4.1.4 Fault Stability Analysis  
Under a given stress field, the stress of a fault is determined based on its dip angle and strike, which in turn determines the stability of the fault. In general, faults with a low dip angle and near orthogonal to the direction of SHmax have a high normal stress and are relatively stable, and vice versa. Geomechanical analyses of faults F1, F2, F3, and F5 indicate that all four faults are stable under the present-day in situ stresses. Pore pressures within the fault rocks would need to be increased between 5.2 MPa (F5) and 8.8 MPa (F8) to force the faults into failure (Figure 8) as the faults have slip tendency values well below 0.6 (Figure 9). This indicates that the risk of vertical leakage due to fault reactivation is low and that fault stability is not controlling hydrocarbon column heights in the fault-bound traps.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour
### Scour Effect  
The most intuitive morphological changes are the erosion and deposition around pile and the scour pits. The traditional method in practical engineering is to reduce the embedment depth by removing a certain depth of soil according to the maximum scouring depth of scour pit [10; 11]. This method is simple, but too conservative in design. The other method considers the slope angle, area, and other factors of the scour pit and introduces the stress history and wedge-shaped failure mechanism [12], which improves the accuracy, but it is complex in practical application.  
More importantly, the method above is based on the appearance of scour pit, and only when the shape of scour pit is regular can it be more accurate. Generally, the small diameter piles can satisfy these conditions. However, from the previous summary, obvious scour pits only occurred in few monopiles, and there were no regular scour pits for most monopiles. If it is still approximate to the scour pit, the inherent errors will be introduced in determining the maximum scour depth, slope angle, and subsequent calculation. Therefore, the traditional method of simply removing soil layer is not suitable for large diameter monopile.
# Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks
## 2 The Job Shop with Two Jobs and Two Machines
### Generating Initial No-Idle Subschedules
\(M(A_{i+1})=M(B_{h+1})=M_{1}\). In this case, the INIS can be continued in two possible ways; i.e., scheduling either \(A_{i+1}\) or \(B_{h+1}\) on \(M_{1}\) (Figure 2b,c respectively). Therefore, \((i,h)\) has two outgoing arcs, pointing towards nodes \((i+1,h)\) and \((i,h+1)\), respectively. However, in this case \((i,h)\) is _not_ a target node, since there is no point in keeping \(M_{1}\) idle until the completion of \(M_{2}\). Clearly, if \(M_{2}\) completes before \(M_{1}\), in the four above cases the roles of \(M_{1}\) and \(M_{2}\) are exchanged. If either \(i=n_{A}\) or \(h=n_{B}\), the above cases simplify as follows, where we assume that \(h=n_{B}\); i.e., job B is finished. (A symmetric discussion holds in \(i=n_{A}\).) 1. \(M(A_{i+1})=M_{1}\). In this case, we can continue an INIS starting task \(A_{i+1}\) on \(M_{1}\). Thus we generate node \((i+1,h)\) and the arc from \((i,h)\) to \((i+1,h)\), which is the only outgoing arc of \((i,h)\). Node \((i,h)\) is a target node. 2. \(M(A_{i+1})=M_{2}\). In this case, \(M_{1}\) is necessarily idle until \(M_{2}\) completes. Hence, there is no way to continue an INIS, and therefore node \((i,h)\) has no outgoing arcs. In this case, \((i,h)\) is a target node. Again, the roles of the two machines are exchanged if \(M_{2}\) frees up before \(M_{1}\) in the partial schedule. Figure 2: Possible scenarios when \(M_{1}\) completes before \(M_{2}\): (**a**) The INIS \((i,h)\) cannot be continued, (**b**) it can only be continued scheduling \(A_{i+1}\), and (**c**) it can only be continued scheduling \(B_{h+1}\). In conclusion, the question of whether a NIS exists for the task set \(A[i]\cup B[h]\) is equivalent to asking whether node \((i,h)\) can be reached from the dummy initial node \((0,0)\) on \(G\). A few words on complexity. Clearly, \(G\) has \(O(n_{A}n_{B})\) nodes, and each node has at most two outgoing arcs. The graph \(G\) can be built very efficiently. In fact, for each node \((i,h)\), it can be checked in constant time, which condition holds among \((i)\)-\((iv)\) (or \((v)\)-\((vi)\) when one of the jobs is over), and hence whether or not it is a target node.
## 2 Background and Related Work
### Edge Computing Architectures  
There are three different edge computing schemes: Cloudlet Computing, Mobile Edge Computing, and Fog Computing [15; 16]. In this work, we use a fog computing approach, a hierarchical and distributed architecture for large-scale computing. The system is built with heterogeneous devices between the end-users and the cloud, for example, mobile nodes. We aim to process data close to the end-user to decrease latency in the SPS's responses.  
Fog computing schemes use a three-layer architecture: the end-users, a fog layer, and the cloud. This approach was first proposed in [17] and was designed for applications that require very low and predictable latency. One crucial component in the architecture is providing service orchestration by monitoring the health and state of the physical machines and services provided. Another essential component is a framework that discovers and allocates mobile resources at the edge. Kinaara [18], for example, does this job by using a multi-tier organization.  
At edge nodes, conventional approaches propose to offload heavy tasks that cannot be handled by end-users, collecting data and locally pre-processing it before sending aggregated data to the cloud [16]. Recently, some authors have stated the edge/ fog architectures are well suited for stream processing systems [14; 19; 20; 21]. [PERSON] et al. [14] made Storm suitable for execution in a geographically distributed environments. [PERSON] et al.  
have performed continuous tasks on streaming data at an edge architecture [19] using a publish/subscribe method.
## 4 Simulation of Use Cases  
Firstly, the goal of the use cases, the scope and the methodology must be defined. The goal of the simulation study is to design a generic simulation model for the quantification of the evaluation scheme parameters along the GUVEI-Model sequence. For each specific use case, a new simulation model is generated in which the simulation logic is adapted according to the different improvements applied. The scope of the study is:  
* Generic simulation model serving as basis for develop specific simulation models. It provides the required complexity level as well as implements the criteria for enabling later comparisons.
* Specific simulation models for a selection of technologies from the ones exposed in Section 3. The scope of these models does not include all of the potentials within the technology cluster, but a specific use case for the technology that will be specified in Section 4.2, where the logic and implications of the models are also to be explained. The hypotheses of the case studies are:
* Improvement of some indicators with each Industry 4.0 technology use case.
* Some Industry 4.0 case studies may need to assume previous lean activities to be coherent. For the methodological framework the following steps will be followed:  
1. Definition of the objective, scope, hypothesis and methodology, including a general description of target simulation models and scenarios;
2. Definition of the production system: flow and characteristics;
3. Definition of quantitative parameters, key performance indicators (KPIs) to obtain results and compare models;
4. Determination of the interrelations among variables within the model;
5. Description of the main assumptions for the simplification of the complexity of the model;
6. Creation of the simulation models based on various Industry 4.0 case studies;
7. Validation of the behavior of the simulation models;
8. Determination of scenarios;
9. Simulation and extraction of results;
10. Evaluation of the results and derivation of conclusions.
## 3 Semi-Physical Experiment and Analysis
### Results of Semi-Physical Experiments  
In this study, we captured the point cloud data of a satellite model and the surrounding background using a ToF camera in a laboratory setting. Without proper preprocessing, the resulting attitude estimation would only be suitable for a dynamic target in a static environment and may not be applicable to real-world space environments.  
We calculated the relative pose of the target following the process in Figure 1 The three-axis translation is represented by the three components of the translation vector \(t\), and the three-axis rotational Euler angles need to be decomposed by the rotation matrix \(R\). In this study, we assumed that the rotation angles \(\alpha\), \(\beta\), and \(\gamma\) around the XYZ axes represented the roll, pitch, and yaw in the Euler angles, respectively.  
Figure 8 illustrates the impact of the mpp-MLS algorithm discussed in Section 2.2. The point cloud data before optimization contained substantial noise due to the system noise, ambient light noise, and multi-path reflection noise inherent to the ToF camera. When displaying multiple frames of point cloud data, the XZ view showed the data overlapping, which complicated the accurate calculation of the attitude information for each frame. However, after applying the mpp-MLS algorithm, the XZ view clearly revealed significant angular rotation displacement in the point cloud data acquired at different frame rates. This processing greatly aided in the subsequent precise calculation of attitude parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Resolution & 640 \(\times\) 480 px, 0.3 MP \\ Pixel Size & 10.0 \(\upmu\)m (H) \(\times\) 10.0 \(\upmu\)m (V) \\ Illumination & 4 \(\times\) VCSEL laser diodes, Class1, @ 850 nm \\ Lens Field of View & 69\({}^{\circ}\)\(\times\) 51\({}^{\circ}\) (nominal) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the ToF cameras.  
Figure 7: Model of the target satellite, slide, rotor, and camera system in the laboratory environment.