esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection
#### 2.2.4 Terrestrial Laser Scanning (TLS) Data  
The TLS data were collected on 20 March 2020, a day after the UAV\({}_{\text{image}}\) data collection; therefore, with the same phenological conditions. For the TLS data acquisition, a Faro FOCUS S70 (FARO Technologies Inc., Lake Marry, FL, USA) scanner with a multi-scan approach was used. The Faro FOCUS S70 scanner has 0.3 mrad beam divergence, \(\pm\)1 mm ranging accuracy and a maximum ranging distance of 70 m. Six reference spheres with a 14.5 cm radius were set-up on wooden stakes whose coordinates were determined in the 2 nd survey, and six scans per plot were collected (one central and five radial). The scan resolution was set to 1/4 and quality to 4\(\times\). Each scan lasted 5 min and the total time to scan the entire plot was approximately 30 min. The geometry of the multi-scan setup was primarily designed to record trees, not the terrain, which was intended to be a secondary product.  
The collected scans were preprocessed in FARO SCENE (version 2020.0.5.5516) software. Reference spheres were marked manually and correspondences were found automatically, achieving a mean registration error of 1.1 mm. Scans were georeferenced in the software CloudCompare (version 2.11.3) using six parameter transformations (three rotations and three translations, average RMSE = 1.5 cm) based on the total station survey (second survey) of the sphere placement stakes. Five to six points were used for georeferencing each TLS point cloud, depending on the stake data availability. Each TLS point cloud consisted of more than 120 million points and was subsampled to 2 cm spacing to ease data manipulation and further processing, which resulted in fewer than 35 million points per plot (\(\sim\)28,000 points\(\cdot\)m\({}^{-2}\)).
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 3 CDR Products and Applications
### CDRs and Scientific Applications
#### 3.1.2 ESA Climate Change Initiative
The legend counts 22 classes defined using the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS); (b) three global climatological seven-day time series over the 1998-2012 period representing typical seasonal dynamics of the land surface at the pixel level: the vegetation greenness as described by the Normalized Vegetation Index, the snow occurrence and the Burnt Areas (BA) distribution. They are compiled from existing global datasets; and (c) Global surface reflectance (SR) time series of the whole MERIS Full and Reduced Resolution archive (2002-2012) provided in seven-day composites.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### Features  
In this section, the one-dimensional CAV based features are described. The CAV has been used as an earthquake intensity measure [40] where it was shown that ground motion CAV has a better correlation to damage than other intensity measures, e.g., PGA, spectral acceleration, or effective duration. However, until recently, CAV of floor accelerations has not been used in SHM to assess damage. [PERSON] and [PERSON] [13] introduced CAV as a damage feature showing that CAV time series, its normalized version (NCAV), and other features extracted from it had distinct patterns for damaged structures which can be used to identify and locate damage. In this paper, four CAV based damage features, discussed below, were studied. These are one-dimensional features making them appropriate for ML computing environment with limited-size datasets.
# A Metal Detector for Clip Location Tracking of Stomach and Colon Cancer during Laparoscopic Surgery
## 2 Physical Analysis and Design Planning  
### Tumor Localization and Extraction Process in Laparoscopic Surgery  
In laparoscopic surgery, it is difficult to locate malignant tumors for removal in the abdominal cavity [2]. In gastric cancer, malignant tumors exist in the visceral folds, as shown in Figure 1, and tumors in the colon exist in the inner mucosa, that is, in the circular folds. In laparoscopic surgery, it is difficult to locate the tumor surrounded by mucosal tissue in the cavity using a surgical instrument inserted using a trocar.  
Therefore, when removing a tumor, medical accidents may occur due to incorrect excision [2]. In the case of gastric cancer, if a tumor existing in the visceral layer is found in the cavity, it becomes difficult to locate the tumor because of the parietal peritoneum. Furthermore, in the case of colorectal cancer, if a tumor existing in circular folds is found in the cavity, it becomes difficult to locate the tumor because of the cecum. Therefore, it is difficult to identify the extent of the incision in the parietal peritoneum or cecum for removing the tumor. To overcome these problems, the method of using a clip-detector during laparoscopic surgery is suitable.  
In laparoscopic surgery, to extract a tumor using a clip-detector, a clip is installed using a stomach scope or colonoscope as shown in Figure 1. As shown in Figure 2, the clip is installed around the tumor, the endoscope is removed again, and laparoscopic surgery is initiated [2]. At this time, the sensor is inserted into the cavity using the trocar to locate the clip [2]. The sensor moves around a specific location in the parietal peritoneum or  
Figure 1: Schematic of the use of clip-detector.  
ecum. The sensor locates the clip installed around the tumor of visceral or circular folds and excises the tumor by making an incision at the location.  
The underlying reason behind the ability of the detector in locating the clip installed around the tumor is that the magnetic coil is coupled around the clip and an induced magnetic field is generated against the eddy current. Furthermore, the source magnetic field is generated to induce a current, and the magnetic coil is wound at the end of the detector. When the source magnetic field and the induced magnetic field undergo magnetic coupling (as shown in Figure 3) and the resonant frequency is matched, an alarm is generated from the speaker connected to the detector. In this manner, the clip-detector can identify the location of the tumor.  
Figure 3: Coupling between clip and detector.  
Figure 2: Flowchart of clip detection and tumor localization.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 5 Results
### Experimental Results Analysis  
#### 5.2.1 Different Cache Spaces  
This section will examine the effects of buffer size from 10 M to 100 M on delivery success rate, average latency, routing overhead and a number of packet losses because cache space size has a significant impact on network performance.  
The following Figure 3 demonstrates that in contrast to other protocols, cache space size has a small impact on the delivery success rate of MPCM. Rather than sending messages blindly, MPCM makes every effort to deliver messages to nodes that may come together by predicting those nodes' locations based on their movement patterns. Hence, MPCM performs better in networks with constrained cache space. The one routing protocol most impacted by cache size is Epidemic, which employs a flooding technique to message delivery with massive message copies throughout the network. Under various cache spaces, the MPCM message latency will vary significantly, but this effect can be disregarded. Significantly fewer messages are discarded in Epidemic as cache capacity increases, the messages will be stored there for a long period and latency will rise noticeably. MPCM intra-group message forwarding and forwarding based on transmission probability may effectively regulate the number of message copies in the network, and the influence of cache size on MPCM network overhead is likewise minimal. In conclusion, the routing overhead falls dramatically, the number of messages successfully delivered by each routing algorithm rises as the cache size grows, and MPCM always maintains a low overhead level.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline dataset & haggle6-infocom6 \\ simulation time/h & 72 \\ simulation area/m\({}^{2}\) & 4500 \(\times\) 3400 \\ number of nodes & 98 \\ message generation interval/s & 100 \\ message size/kb & 50 k–5000 k \\ message TTL/h & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
###### Abstract  
When single-phase three-level neutral-point-clamped (NPC) converters operate, there are two main control objectives that need to be met for correct operation. First, the ac source current must be controlled to be sinusoidal. Second, the dc capacitor voltages must be balanced. In original model predictive control (MPC) methods for NPC converters, an optimization process involving an empirical weighting factor design is required to meet both of these objectives simultaneously. This study proposes an MPC approach developed for single-phase three-level NPC converters to meet these objectives using a single reference voltage consisting of a difference-mode term and a common-mode term in each phase. The difference-mode term and the common-mode term are responsible for sinusoidal ac source current synthesis and dc capacitor voltage balancing, respectively. Then, a single cost function compares the adjusted reference voltage with possible voltage candidates to select an optimal switching state, resulting in the smallest cost function value. Different from the conventional MPC method, the proposed approach avoids the selection of weighting factors and the attendance of various control objectives. Thanks to the deterministic approach, the proposed MPC method is straightforward to implement and maintain fast transient performance while guaranteeing the control objectives. Finally, the effectiveness and feasibility of the proposed approach for single-phase three-level NPC are verified through comprehensive experimental results.  
**Keywords:** neutral point clamped converter; predictive control method; deterministic approach +
Footnote †: journal: Applied Sciences
## 4 Evaluation of Industrial Applicability
### Case Study Results  
Figure 13 shows the average part classification accuracy obtained in the case study. Here, each data group represents a single build job split into the top-one (blue) and top-three (red) accuracies. The overall top-one and top-three classification accuracies, including all the build jobs, are represented by the vertical lines. It was found that average part recognition accuracies of 99.04% (top three) and 90.37% (top one) were achieved for all the build jobs combined. In addition, little variation in the average accuracy was observed between build jobs, i.e., the standard deviation was 3.78% for the top-one accuracy and 1.20% for the top-three accuracy. Note that 100% top-one accuracy was not achieved. However, 100% top-three accuracy was obtained for three different build jobs.  
Misclassifications were primarily observed with mirrored parts, parts that were too large to fit in the sensing area (37 cm \(\times\) 40 cm), comparatively small parts and parts that only differed in scale from other parts. Here, small parts were defined as parts with a footprint smaller than 2 cm \(\times\) 2 cm. In total, 5 out of 519 parts could not be recognized by the proposed workflow.  
Figure 12: Proposed system for the automated recognition of AM parts during the case study. A worker is placing parts in the sensing area (left) and receives feedback about each recognized part via the GUI (right) in order to sort them.  
The sorting times were 24 min and 50 s for manual sorting of 100 parts and 7 min and 47 s for sorting with the automated part recognition process. Thus, the duration of the manual sorting process was approximately 3.19 times longer than that of the automated sorting process.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 4 Discussion
### Model Performances under Drought and Non-Drought Conditions  
GPPMOD could have different performances under drought conditions. We used the Standardized Precipitation Index (SPI) to identify the periods of drought. The SPI is designed to be a spatially invariant indicator of drought, which recognizes the importance of time scale in the analysis of water use [63]. In this study, the precipitation from the FluxNet sites was used to calculate SPI for each month to determine the status of drought [64]. If SPI was less than \(-0.5\), it was defined as drought [65]. We analyzed the relationships between monthly GPPMOD and GPPEC in both non-drought and drought conditions. The results showed that GPPMOD had higher \(R^{2}\) (0.72 (0.66-0.79)) and lower \(RMSE\) (2.33 (2.05-2.60) g Cm\({}^{-2}\) day\({}^{-1}\)) in drought conditions than in non-drought conditions with lower \(R^{2}\) (0.64 (0.61-0.68)) and higher \(RMSE\) (2.48 (2.34-2.62) g Cm\({}^{-2}\) day\({}^{-1}\)), which indicates that GPPMOD had better explanatory capabilities for GPPEC variances under drought conditions than under non-drought conditions (Table A1).  
Although several studies have reported that the standard MODIS GPP products did not accurately estimate carbon uptake during drought conditions [66; 24; 67]. The role of VPD in determining GPP has been examined [68]. For example, [PERSON] et al. [46] pointed out that a high VPD scalar could reduce GPP values during a dry period. Another study also found that VPD had a weak positive correlation with GPP. Furthermore, VPD sometimes failed to capture drought events [69]. In this study, however, it is notable that GPPMOD had a better performance in drought conditions than in non-drought conditions for grasslands.  
We analyzed the relationships between the VPD-based water scalar and GPPMOD and found a higher \(R^{2}\) between GPP and VPD-based water scaler in drought conditions (Figure 7) even though they had weak correlations in both drought and non-drought conditions (26% vs. 8%, Figure 7b,c). The MOD17A2H GPP products of grassland have better performance in drought conditions, which could be attributed to the Wscalar. However, there could be some uncertainty in the drought identification since SPI calculation was based on a short-term precipitation record in some sites. More studies are still needed in the future for a thorough analyses. The weak correlation between Wscalar and GPP suggest that the water scalar could be further improved in the grasslands.
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 3 Results
### Economic Optimization
Generally, the optimizer finds solutions that require smaller power to charge or discharge batteries, which is positive from the durability of the batteries but does not use the full capacity of the batteries when it is possible to charge from the PVs. Figure 19: Results for the economic optimization method—data for 27 July: (**a**) grid power balance, (**b**) energy usage and production (input data), (**c**) batteries power, (**d**) batteries state of charge. Figure 18: Results for the economic optimization method—data for the month of July, aggregated per day: (**a**) grid energy balance, (**b**) energy usage and production (input data), (**c**) activity of the energy storage, (**d**) cost of purchased energy and profit for sold energy. During winter (Figure 21), the behavior of the batteries shows the typical schedule of the HESS operation used for price arbitrage. Batteries are being charged during off-peak hours, even if it means importing extra amounts of energy from the grid. During the morning peak, the HESS power remains close to zero, while in the evening peak the highest priced batteries are being discharged. Both the VRFB and the LFP battery follow similar patterns. This behavior is consistent with our assumptions, based on the difference in prices between the price zones in the tariff. The economic factors clearly show that the economic algorithm that reduces the purchase of energy is the highest tariff (Figure 22a), at the same time increasing the use of energy from the lowest tariff, especially in winter months. The general costs of the system are smaller when compared to the situation without any energy storage and also lower than the benchmark. The arbitrage in winter is decreasing the overall cost of operation of the whole facility. The experiments revealed a problem in which the batteries do not charge fully in case of a surplus of energy (this situation is visible on Figure 19b). This is caused by the fact that there is no value for the optimizer to keep a higher state of charge of the batteries at the end of the day. To solve the issue, the obvious action would be to run the optimization for a longer period (e.g., a week, a month) but then the number of changing variables would be significantly increased, which would bring two problems: the problem with convergence and the extended time of computations. Figure 21: Results for the economic optimization method—data for 6 February: (**a**) grid power balance, (**b**) energy usage and production (input data), (**c**) batteries power, (**d**) batteries state of charge. Figure 20: Results for the economic optimization method—data for 4 October: (**a**) grid power balance, (**b**) energy usage and production (input data), (**c**) batteries power, (**d**) batteries state of charge.
## 3 Identification of Position Error of Rotary Axis
### Identification of Average Position Error of A-Axis
\begin{array}{l}\Delta x_{2}+\Delta y_{2}\cdot\varepsilon_{\gamma A}- \Delta z_{2}\cdot\varepsilon_{\beta A}=0\\ \varepsilon_{\gamma A}\cdot(z_{2}-m_{z}+y_{2}^{\prime}-m_{y})=\delta_{yA}- \delta_{zA}+C_{3}\\ \varepsilon_{\alpha A}\cdot(z_{2}^{\prime}-m_{z}-y_{2}+m_{y})=\delta_{yA}+ \delta_{zA}+C_{4}\end{array} \right. \tag{17}\]  
where \(S_{2}=\left[ \begin{array}{cccccc}x_{2}&y_{2}&z_{2}&1\end{array} \right]^{T}\),\(S_{2}^{\prime}=\left[ \begin{array}{cccccc}x_{2}^{\prime}&y_{2}^{\prime}&z_{2}^{ \prime}&1\end{array} \right]^{T}\),\(\Delta x_{2}=x_{2}^{\prime}-x_{2}\), \(\Delta y_{2}=y_{2}^{\prime}-y_{2}\), and \(\Delta z_{2}=z_{2}^{\prime}-z_{2}\). By combining Equations (15) and (16), the expression of the geometric error for A-axis position was obtained, as expressed in Equation (17):  
\[\left\{ \begin{array}{l}\varepsilon_{\beta A}=(\Delta y_{1}\Delta x_{2}- \Delta x_{1}\Delta y_{2})/(\Delta y_{1}\Delta z_{2}-\Delta z_{1}\Delta y_{2}) \\ \varepsilon_{\gamma A}=(\Delta z_{1}\Delta x_{2}-\Delta x_{1}\Delta z_{2})/( \Delta y_{1}\Delta z_{2}-\Delta z_{1}\Delta y_{2})\\ \varepsilon_{\alpha A}=(z_{1}-z_{2}+y_{1}^{\prime}-y_{2}^{\prime})/(C_{1}-C_{ 3})\\ \delta_{yA}=(z_{1}+z_{1}^{\prime}-2m_{z}+\Delta y_{1})(z_{1}-z_{2}+y_{1}^{ \prime}-y_{2}^{\prime})/(2C_{1}-2C_{3})-(C_{1}+C_{2})/2\\ \delta_{zA}=(y_{1}+y_{1}^{\prime}-2m_{y}-\Delta z_{1})(z_{1}-z_{2}+y_{1}^{ \prime}-y_{2}^{\prime})/(2C_{3}-2C_{1})-(C_{2}-C_{1})/2\end{array} \right. \tag{18}\]  
The constant terms \(C_{1}\)\(\sim\)\(C_{4}\) consisted of error parameter \(\varepsilon_{\beta A}\),\(\varepsilon_{\gamma A}\) obtained in the first step, and the spatial coordinates of the two sets of spherical points, which were used to further simplify obtained position error equations, as shown in Equation (18):  
\[\left\{ \begin{array}{l}C_{1}=(x_{1}^{\prime}-m_{x})\cdot\varepsilon_{\beta A }+(x_{1}-m_{x})\cdot\varepsilon_{\gamma A}-y_{1}+z_{1}^{\prime}+m_{y}-m_{z}\\ C_{2}=(m_{x}-x_{1})\cdot\varepsilon_{\beta A}+(x_{1}^{\prime}-m_{x})\cdot \varepsilon_{\gamma A}-y_{1}^{\prime}-z_{1}+m_{y}+m_{z}\\ C_{3}=(x_{2}^{\prime}-m_{x})\cdot\varepsilon_{\beta A}+(x_{2}-m_{x})\cdot \varepsilon_{\gamma A}-y_{2}+z_{2}^{\prime}+m_{y}-m_{z}\\ C_{4}=(m_{x}-x_{2})\cdot\varepsilon_{\beta A}+(x_{2}^{\prime}-m_{x})\cdot \varepsilon_{\gamma A}-y_{2}^{\prime}-z_{2}+m_{y}+m_{z}\end{array} \right. \tag{19}\]  
Position error \(\delta_{xA}\) was obtained by substituting the obtained 5 position error elements in Equation (14).
## 3 Theoretical Background
### Optical Constants  
Optical properties [viz., refractive index n(\(\omega\)), extinction coefficient \(\kappa(\omega)\), reflectivity R(\(\omega\)), absorption coefficient \(\alpha(\omega)\), etc.] of polar semiconductors are strongly associated with the complex dielectric function \(\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)\) [or \(\widetilde{\overset{\sim\text{}}{\text{n}}}(\omega)\)]. Numerically, one can obtain \(\widetilde{\overset{\sim\text{}}{\text{n}}}(\omega)\) by using \(\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)\) and expressing it as follows: [38]  
\[\widetilde{\overset{\sim\text{}}{\text{n}}}(\omega)=\text{n}(\omega)+\text{i} \kappa(\omega)=\sqrt{\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)}, \tag{2}\]  
where the two constants n(\(\omega\)) and \(\kappa(\omega)\) have real, positive numbers and are usually estimated from different experimental measurements such as SE, IR, etc. [22; 23; 38]. The values of these constants can be evaluated by exploiting: [38]  
\[\text{n}(\omega)=\left[\frac{\left(\varepsilon_{1}^{2}+\varepsilon_{2}^{2} \right)^{1/2}+\varepsilon_{1}}{2}\right]^{1/2}, \tag{3a}\] \[\kappa(\omega)=\left[\frac{\left(\varepsilon_{1}^{2}+\varepsilon_{2}^{2} \right)^{1/2}-\varepsilon_{1}}{2}\right]^{1/2}. \tag{3b}\]  
Again, by incorporating them, it is possible to evaluate the reflectivity R(\(\omega\)) and absorption coefficient \(\alpha(\omega):\)[38]  
\[\text{R}(\omega)=\frac{\left[\left(\text{n}(\omega)-1\right)^{2}+\kappa(\omega )^{2}\right]}{\left[\left(\text{n}(\omega)+1\right)^{2}+\kappa(\omega)^{2} \right]}, \tag{4a}\] \[\alpha(\omega)=\frac{4\pi}{\lambda}\kappa(\omega), \tag{4b}\]  
where \(\lambda\) is the wavelength of light in vacuum. The real \(\varepsilon_{1}(\omega)\) and imaginary \(\varepsilon_{2}(\omega)\), parts of the dielectric function \(\widetilde{\overset{\sim\text{}}{\epsilon}}(\omega)\) of materials are also associated with the Kramers-Kronig relations via the critical point (CP) energies of their electronic energy-band structures E\({}_{\text{j}}\)\(\left(\overset{\rightarrow}{\text{q}}\right):\)[38]  
\[\varepsilon_{1}(\omega)-1=\frac{2}{\pi}{\int_{0}^{\infty}}\frac{ \omega^{\prime}\varepsilon_{2}(\omega^{\prime})}{\left(\omega^{\prime}\right) ^{2}-\omega^{2}}\text{d}\omega^{\prime}, \tag{5a}\] \[\varepsilon_{2}(\omega)=-\frac{2\omega}{\pi}{\int_{0}^{\infty}}\frac{ \varepsilon_{1}(\omega^{\prime})}{\left(\omega^{\prime}\right)^{2}-\omega^{2} }\text{d}\omega^{\prime}. \tag{5b}\]  
One must note that the optical joint density of states for the semiconductor materials becomes large in the vicinity of CP energy transitions in the Brillouin zone (BZ) [28].