esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Calibration Status of Landsat 8 Operational Land Imager (OLI)
### Landsat-8 Geometric Calibration
#### 2.5.2 Calibration, Reference Data, and Interoperability  
An important additional factor in Landsat-8 OLI product geometric accuracy is the use of ground control points during product generation to ensure multi-temporal registration for products throughout the Landsat archive. These control points were originally derived from the Global Land Survey (GLS) data set composed of Landsat 7 data circa 2000 [10]. The use of ground control for registration can be thought of as a type of per-product calibration wherein the ground control points serve as the calibration standard. The geometric accuracy of Landsat-8 products is thus dependent upon the accuracy of the control point standard as much as or more than the inherent accuracy of the calibrated  
\begin{table}
\begin{tabular}{c c c c} \hline
**Operation** & **Parameter** & **Purpose** & **Frequency** \\ \hline \multirow{2}{*}{OLI Sensor Alignment} & OLI to attitude control system alignment matrix & Estimate OLI orientation relative to attitude control system to improve absolute pointing knowledge/geolocation. & During commissioning, Update quarterly as needed. \\ \multirow{2}{*}{OLI Focal Plane Alignment} & Pan band sensor chip & Estimate corrections to pan band line-of-sight model for each sensor chip assembly to improve internal alignment of sensor modules. & During commissioning, Monitor and update if needed. \\ \multirow{2}{*}{OLI Band Alignment} & MS band sensor chip & Estimate corrections to MS band line-of-sight models to improve band-to-band alignment, holding pan band fixed. & During commissioning, Monitor and update if needed. \\ \hline \end{tabular}
\end{table}
Table 4: OLI geometric calibration operations and parameters.  
Landsat-8 OLI system. The accuracy of the GLS framework is estimated to be 25.8 m (CE90) so, for Landsat-8, the use of ground control to achieve multi-temporal consistency negatively affects absolute product accuracy. Although it ensures registration accuracy within the Landsat archive, the GLS control limits registration accuracy to other data sets, such as Sentinel-2, that are not tied to the GLS framework.  
Direct comparison of Landsat-8 and Sentinel-2 data at 317 globally distributed test sites, with locations shown in Figure 3, resulted in a measured registration accuracy of 22.9 m (2 \(\sigma\)). This is somewhat better than might have been expected given the estimated accuracy of the GLS framework, but it is still more than two-thirds of an OLI multi-spectral pixel. Significantly better registration accuracy is required to allow Landsat-8 and Sentinel-2 data to effectively interoperate. A global re-triangulation of the GLS control framework is underway using Landsat-8 data to improve the consistency and accuracy of the global control reference [11]. The triangulation will include tie points extracted from the Sentinel-2 global reference image (GRI) to make the two frameworks consistent and improve registration accuracy and interoperability. Completion is expected in early 2019 to support a complete Collection-2 reprocessing of the Landsat archive.
## 2 Background and Related Studies
### Challenges of Tone Modeling  
In tonal languages, tone behavior is complex in continuous speech even though there is only a finite number of tones. Several studies have been carried out on deviations in tone such languages as Thai and Mandarin [18; 34; 35; 36], but Isarn has not yet been thus considered. We examined these studies for the extracted \(F_{0}\) contours from Isarn to identify factors affecting deviations in tone contours in continuous speech. Based on past studies, many factors affect deviations in tonal contours in continuous speech, such as tone co-articulation, stress, and declination. Figure 2 shows a comparison of the \(F_{0}\) contour of a sentence in Isarn pronounced in isolation and continuously. As is shown, the \(F_{0}\) contour of continuous speech deviated due to these factors.  
Tone co-articulation is a phenomenon whereby the shape of the \(F_{0}\) contour of a given syllable is affected by \(F_{0}\) contours of adjacent syllables because the articulatory organs cannot respond rapidly enough to preserve the shape of the \(F_{0}\) contours of the uttered syllables. For example, in Figure 2b, the \(F_{0}\) contour of syllable /man/ is assimilated into syllable /ma/. Note that shapes of their \(F_{0}\) contours differ from the patterns of their pronunciations in isolation as shown in Figure 2a. The \(F_{0}\) contours of stress syllables differ from those of unstressed syllables and more closely approximate a stable pattern than them--for instance, the \(F_{0}\) contour of the syllable /man/ (without stress) and that of the syllable /da/ (with stress). The declination effect refers to the downward trend of the \(F_{0}\) level to conform to the pattern of intonation of a larger prosodic level, such as that of a phrase or a sentence.  
Figure 1: \(F_{0}\) contour of six Isarn tones.
## 4 Numerical Model
### Influence of Grouting Reinforcement Layer Thickness
#### 4.4.3 Surrounding Rock Plastic Zone  
The plastic zone referred to the yield zone where plastic deformation occurred. According to different yield forms, plastic deformation can be divided into the shear plastic zone and tensile plastic zone. The surrounding rock in the plastic zone has poor mechanical properties, and plastic deformation that cannot be recovered by itself occurred, and strength failure was even a risk when it exceeded its bearing capacity. The area of the plastic zone was a key parameter that reflected the damage degree of the surrounding rock. The larger the area of the plastic zone, the higher the damage degree of the surrounding rock. Table 4 shows the area of shear and tensile plastic zone of surrounding rock under different grouting reinforcement layer thicknesses. With the increase of the grouting reinforcement layer thicknesses, the area of the shear plastic zone first decreased and then increased slightly. When the thickness of reinforcement layer h = 1.5 m, the area of the shear plastic zone reached the minimum 126.17 m\({}^{2}\). The area of the tensile plastic zone basically decreased with the increase of the grouting reinforcement layer thicknesses. The singularity was that when h = 1.5 m, the area of the tensile plastic zone was 22.41 m\({}^{2}\), which was smaller than that obtained in the two adjacent working conditions. The surrounding rock of the tunnel could fully exert its self-supporting capacity in the case of grouting reinforcement layer thickness h = 1.5 m, the possibility of yield failure was small, the grouting reinforcement  
Figure 16: Relationship between the maximum displacement of the vault and grouting reinforcement layer thickness.  
effect of the surrounding rock was better, and the gains obtained were higher. In Table 4, the ratio of the shear plastic area of grouted surrounding rock to the shear plastic area of surrounding rock without grouting (h = 0) was \(\omega_{1}\). The ratio of the area of grouted surrounding rock tensile plastic zone to the area of surrounding rock tensile plastic zone without grouting (h = 0) was \(\omega_{2}\). When the reinforcement layer thickness was h = 1.5 m, \(\omega_{1}\) and \(\omega_{2}\) were 0.33 and 0.08, respectively. Compared with the reinforcement condition without grouting, the areas of the shear plastic zone and tensile plastic zone were reduced by 67% and 92%, respectively.
## 5 Case Studies
### Case-2 at 03:40 UTC on 27 July 2018  
The NRT SWIPE model successfully captured another medium sudden convective storm case at 03:40 UTC (Beijing time 11:40) on 27 July 2018 in the Shandong province of China. As a typical North China Plain area, the average latitude of Shandong province is 35\({}^{\circ}\)N with moist summers and dry, cold winters (four distinct seasons). The summer precipitation generally contributes more than 50% of annual precipitation [48]. Since the precipitation area and the Intertropical Convergence Zone (ITCZ) have moved northward [49], Shandong Province will be frequently subjected to severe convective storm weather systems in the summer season (June, July, and August) as shown in Figure 1. The detailed process of this convection is shown in Figure 6.  
This convective storm lasted about 2 hours. From the first row of Figure 6, it is found that the SWIPE model initially successfully captured a newborn sudden convective storm system in the central part of Shandong at 03:40 UTC. Note that, the continuous records of the 160 ground-based rainfall gauge data within 1-minute intervals in the Shandong province also clearly reveal that the  
Figure 5: A sudden convective storm case tracked by the SWIPE model at 07:00 UTC on 23 April 2018 in the Hainan province of China. The sub-figures in the first row represent the results of SWIPE index and the grayscale 10.4 μm BT image with coast line (yellow solid line). The colorful sub-figures at the second row represent the 10.4 μm BT images. The sub-figures in the third row are the accumulated precipitation in the past one hour (mm/h) measured by the ground rainfall gauge stations. The sub-figures in the first and second columns signify the two contiguous results or scenarios when this convective storm case in Hainan province is tracked by the SWIPE model at the first time. The sub-figures in the third and fourth columns represent the results or scenarios with the first rainfall measurement and the maximum rain rate, respectively.  
rainfall first occurs at 03:51 UTC at the central part of Shandong Province. The sub-figures at the last column of Figure 6 exhibit the maximum rain rate of 51.3 mm/h (significantly larger than 16 mm/h) observed at 04:36 UTC. Therefore, in this case, the SWIPE model can capture sudden convective storm systems 56 minutes earlier than the occurrence of their maximum rain rate, whereas one hour ahead is completely adequate [50]. However, unfortunately, the SWIPE model underestimates the rank of this sudden convective storm system which should be a severe convection sample. This underestimation is likely to be induced by the relatively high FAR (0.91) of medium case using the Scenario-1 RF classification model.
## 3 Controller Design
### Trajectory Tracking of Speed, Position, and Torque  
According to the last section, the controller depends on the values of the states of the system and the reference trajectories; this implies that this trajectories must be known. To define the references, \(\omega^{*}\) is proposed as a free parameter. Then, the remaining trajectories can be determined from (23), and the \(i_{q}^{*}\) reference is given by (56)  
\[i_{q}^{*}=\frac{J}{K_{m}}\frac{d\omega^{*}}{dt}-\frac{1}{K_{m}}\tau_{L}. \tag{56}\]  
Finally  
where \(\xi=R_{s}/L_{s}\).  
Until now, the controller design ensures the trajectory tracking of the vector (18) which implies that the tracking of speed trajectories is achieved. Nevertheless, it could be desirable to track position and torque trajectories. For a position trajectory tracking \(\theta^{*}\), Equation (60) can be used:  
\[\omega^{*}=\frac{d\theta^{*}}{dt}. \tag{60}\]  
In this sense, adding a block with a derivative operator before the input, we are able to use the controller (26) to perform the tracking of position trajectories.  
Finally, for the tracking of torque trajectories, we consider that the torque generated by PMSM is given by  
\[\tau^{*}=K_{m}i_{q}^{*}. \tag{61}\]  
Therefore, when the tracking of \(i_{q}^{*}\) trajectories is achieved, the tracking of torque trajectories is also attained. Moreover, these trajectories are proportional to the acceleration of the rotor. A summary of these schemes is described below in Figure 1.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 6 Conclusions  
In this paper, a bi-level optimization method is presented. The purpose of this method is to improve both the working condition of the DN and MGs. The first level is to minimize power loss and voltage offset in the DN based on the PSO algorithm containing discrete variables, considering reconfiguration of the DN and the power interaction between DN and MGs. The second level solves a MILP problem to control the operation modes of devices in CCHP-based MGs to minimize the total cost of MGs. The simulation results in all cases demonstrate that:  
1. The power loss, voltage offset, and power interaction on tie lines have noticeable reductions based on the proposed method, reflecting that it is necessary to consider both the benefit of DN and MGs.
2. Compared with [25], the consideration of the structure of the DN can keep the voltage of partial nodes away from extreme conditions, while further reducing DN power loss and operating costs of the MGs.
3. The MGs provide support for the DN, and in the appropriate cases, some of the benefits can be sacrificed to meet the dispatch requirements under the framework proposed in this paper.
4. This paper only considers the electrical distribution network. Further research can focus on the effect of taking gas networks and thermal networks into consideration.  
Conceptualization, S.C. and Q.X.; methodology, S.C. and Y.Y.; software, S.C.; validation, Q.X., Y.Y. and J.Z.; formal analysis, Q.X.; investigation, S.C. and Y.Y.; resources, Q.X.; data curation, J.Z.; writing--original draft preparation, S.C.; writing--review and editing, Q.X., Y.Y. and J.Z.  
This research was funded by The National Natural Science Foundation of China Under Grant, grant number 51577028.  
The authors declare no conflict of interest.
## 5 Result and Discussion
### Performance Evaluation  
For the experiment, we divided the collected data into training (60%), validation (20%), and test data (20%) as shown in Table 2. Target data were distributed to each data about 25.23%. The training data was used to train the model, the validation data was used to select the best performing model in the training process, and the test set was used to evaluate the performance of the finally selected model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Split Data** & **Count** & **Non RepSTDoc** & **RepSTDoc** & **Ratio** \\ \hline Training & 4440 & 3319 & 1121 & 25.25\% \\ Validation & 1480 & 1107 & 373 & 25.20\% \\ Test & 1480 & 1107 & 373 & 25.20\% \\ \hline Total & 7400 & 5533 & 1867 & 25.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of training, validation, and test data.  
Figure 2: A comparison of DocClass_ConvNet [22] and RepSTDoc ConvNet.
## 5 Robustness Results and Analysis  
In the previous sections, the results were studied to aid the development of the BiRep algorithm. This section has as the objective to test the reputation system against several different scenarios in order to determine BiRep's robustness. To evaluate the results, only metrics for good nodes will be judged once they are most relevant to evaluate the whole scheme's usefulness.  
As the most important factor for the success of a reputation system is the number of messages relayed between nodes, the first group of scenarios uses different message creation intervals and transmission rates to see how these differences influence the results. The second group of scenarios that are tested has a major focus on node density.
## 5 Illustrative Example
### Results
#### 5.3.3 Cruise Ship  
In ice regime 1, the cruise ship received a modified RIO of 5 ('normal operations'). In ice regime 2 the cruise ship received a modified RIO of \(-15\) ('operations subject to special consideration'), reflecting the severe life-safety consequences should an incident occur in this ice regime with 2500 passengers onboard. Due to the cruise ship's low spill consequence value, there was no additional consequence severity for operating in an environmentally sensitive zone. However, the modified RIO remained at \(-15\) due to its high life-safety category in ice regime 2. Route C was the only option that allowed for 'normal operations' for a cruise ship of this size.  
It is noted that the operational restrictions for the cruise ship would be much less severe if it were a smaller expedition cruise vessel with fewer than 500 people on board and/or if the vessel were built to a higher ice class.
## 3 Description of the Study Area  
### Description of the Watershed  
Douala is the most populous city in Cameroon, with about 3 million people distributed across the 12 watersheds. Among the watersheds, the Tongo-Bassa watershed has been chosen for this study because it has been recorded as the place where enormous human and infrastructural damage has been caused by flooding each year. The Tongo Bassa watershed is located between longitude \(9^{\circ}46^{\prime}00^{\prime\prime}\) E and latitude \(4^{\circ}4^{\prime}00^{\prime\prime}\) N (WGS 84 UTM Zone 32N) and its area is estimated at 4161.6 ha (Figure 2). [42] estimated in 2009 that the watershed constituted 27 districts, with a population density ranging from 25 to more than 350 inhabitants per hectare. The watershed has a river called the \"Tongo Bassa River\", which flows into the major Wouri River in the region and then joins the Atlantic Ocean [43]. The soil of the area is essentially made up of sedimentary rocks, with colors that vary from brown to black and soils that change from sandy ferritic to deep sandy clayey soils [44].  
Figure 2: Location of the Tongo Bassa watershed in Cameroon: (**A**) (Location of Littoral Region on the Cameroon map), (**B**) (Location of Wouri Division on the Littoral Region), (**C**) (Location of Field study in subdivisions of Wouri) and Tongo Bassa Watershed.  
The watershed is composed of valleys where the altitudes vary from about 5 to 60 m above sea level, on average. This area is dominated by two main seasons: dry (December to February) and wet (March to November). However, it should be noted that these seasons do not affect the monthly temperatures. Particularly in August, the temperature is 26 \({}^{\circ}\)C and reaches 28.6 \({}^{\circ}\)C in February, with an annual temperature average estimated at 27 \({}^{\circ}\)C [45]. They found that the lowest amount of precipitation is recorded in December (the monthly average is 35 mm) and the maximum is recorded in July and August (an average of 754 mm). The land cover of the watershed is occupied by vegetated soil at 6.43% (267.61 ha), bare soil at 5.03% (209.17 ha), farming at 26.23% (1091.46 ha), urban areas at 62.25% (2590.44 ha) and water at 0.07% (2.92 ha) (Figure 3).
## 3 PAPR Reduction with Clipping and Modified Iterative Detection Scheme
### Clipping Noise Reconstruction  
For dealing with the clipping noise, another approach is to directly reconstruct the clipping noise in the iterative detection and correct the received signal by the reconstructed clipping noise, as shown in Figure 3b.  
Here again, the signal after clipping is modeled as (13). In the iterative detection process at the receiver side, the following algorithm is introduced.  
1. The first iteration yields a preliminary demodulation result \(\hat{U}=[u_{0},u_{1},u_{2},\ldots,u_{M}\hat{-1}]^{T}\), with \(u_{n}\in\{0,1\}\) by SIC detection and decoding.
2. Perform the same modulation process on the detection results as on the transmitter to reconstruct the transmit signal estimate \(\hat{\mathbf{x}}\).
3. Reconstruct the clipping noise according to \[\hat{\mathbf{d}}=c(\hat{\mathbf{x}})-\alpha\hat{\mathbf{x}},\] (19) which is then subtracted from the received signal as \(\hat{\mathbf{y}}=\mathbf{y}-\hat{\mathbf{d}}\), and replace \(\mathbf{y}\) by \(\hat{\mathbf{y}}\) in the next round of iterative detection.  
With such above-mentioned iterative detection scheme, the clipping noise is estimated more accurately with multiple iterations, resulting in more accurate decoding results.  
To evaluate the complexity, we assume that the signal length is \(N\). The proposed scheme involves several operations in each iteration, including \(N\) times numerical integration, one \(N\)-point inverse fast Fourier transform (IFFT), one \(N\)-point fast Fourier transform (FFT), and one \(N\)-dimensional vector addition.  
The time complexity of the \(N\) times numerical integration is \(\mathcal{O}(N)\). The \(N\)-point IFFT and \(N\)-point FFT have a time complexity of \(\mathcal{O}(N\log(N))\) each. Meanwhile, the  
Figure 3: Block diagram of the iterative detection algorithm for clipped OvTDM. (**a**) Block diagram of iterative soft compensation algorithm for OvTDM. (**b**) Block diagram of clipping noise reconstruction for OvTDM.  
-dimensional vector addition takes \(\mathcal{O}(N)\) time. Therefore, the total time complexity is given by \(\mathcal{O}(N\log(N))+\mathcal{O}(N)+\mathcal{O}(N)\), which simplifies to \(\mathcal{O}(N\log(N))\).  
In each iteration of the existing serial interference cancellation framework, the introduced iterative approach includes a corrective measure involving amplitude-limited noise. As a result, the computational complexity of the modified scheme shows a moderate increase when compared to the original approach. This indicates a smooth integration of the proposed solution with the existing framework, resulting in a negligible rise in computational overhead. Essentially, the incremental complexity introduced by the proposed scheme is small, affirming its compatibility and efficiency within the established paradigm.