esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In contrast, Summative had the lowest \(\beta_{M}\) value with \(\beta_{S}=0.20\), indicating that although it possesses a desirable simplicity attribute, it lacks the robustness necessary to compensate for its limited elements and operations. It is also pertinent to emphasize the relevance of elucidating the relationship between Accuracy (\(MAD_{M}\)) and Complexity (\(CP_{M}\)) that implicitly holds in our approach. The \(MAD_{M}\) values indicate that more complex methods (ST and ST-FIS) exhibited lower absolute deviations, suggesting they are more precise. Moreover, the ST-FIS scheme attained the lowest \(MAD_{ST-FIS}=0.06\), making it the most precise method among the four. The Summative method, with \(MAD_{R}=0.13\), was the least precise, suggesting it may produce more inconsistent evaluations compared to more structured methods. Table 8 reports the standard errors for \(MAD_{M}\), \(CI_{M}\) and \(\beta_{M}\) indicators for the four grading methods analyzed. \(MAD_{ST}\) resulted in the least variability, suggesting better precision of ST in that dimension. \(CI_{S}\) and \(\beta_{S}\) achieved lower standard error values. Conversely,  
the ST-FIS method showed the greatest variability across all indicators, pointing to lower statistical robustness regarding the precision of its calculated values. Nevertheless, this should be taken in a relative sense. It is worth pointing out that the reported differences in standard error values among indicators are minimal and consequently do not significantly affect the overall comparative assessment of the methods. According to the present results, a shift to STBAM is not merely a matter of notation or structure; it represents a methodological transformation through which to address the structural shortcomings of the traditional Summative and Rubric approaches. Table 9 provides a brief scheme that allows for the comparison of STABM's features with those of conventional methods. Specifically, while Summative assessment operates through the linear aggregation of teacher-assigned grades across predefined content sections, STBAM evaluates student performance through explicit binary indicators linked to core competencies--Learning (L), Procedure (P), and Attitude (A)--which are tied to specific instructions within a learning activity. Moreover, STBAM is not simply a variant of conventional grading tools but a comprehensive framework designed to address key limitations found in both the Summative and Rubric approaches. Summative assessment relies on weighted averages of teacher-assigned grades, and the Rubric method introduces structure through descriptive performance levels and weighted criteria. Both methods assume evaluative precision and still involve significant subjectivity. Furthermore, STBAM enables holistic evaluation, ensuring that students not only \"know\" (L) but can \"apply\" (P) and \"engage constructively\" (A) with the task. This is achieved by requiring that each instruction be associated with observable, assessable indicators, making the process more transparent and less susceptible to evaluator bias. STBAM
## 2 Materials and Methods
### Patch-Clamp Experiments  
The whole-cell patch-clamp method was used to record the effects of trypsin on the pacemaker potentials of ICCs. The composition of the physiological salt solution used to bathe the cultured ICC clusters was 5 mM KCl, 135 mM NaCl, 2 mM CaCl\({}_{2}\), 10 mM glucose, 1.2 mM MgCl\({}_{2}\), and 10 mM HEPES (adjusted to pH 7.4 with NaOH) [23]. Meanwhile, the composition of the pipette solution used to examine pacemaker potentials was 140 mM KCl, 5 mM MgCl\({}_{2}\), 2.7 mM K\({}_{2}\)ATP, 0.1 mM NaGTP, 2.5 mM creatine phosphate disodium, 5 mM HEPES, and 0.1 mM EGTA (adjusted to pH 7.2 with KOH). The patch-clamp experiments were performed using Axopatch I-D and Axopatch 200B amplifiers (Axon Instruments, Burlingame, CA, USA). Command pulses were applied using pClamp software (version 6.1; Axon Instruments). The data were filtered at 5 kHz with Axopatch 200B amplifiers. The results were analyzed using pClamp and Origin software version 6.0 (Microcal, Northampton, MA, USA). All experiments were performed at 30-32 \({}^{\circ}\)C.
## 2 Methods
### Trajectory Generation  
For our experiments on trajectory control, we have defined a sample trajectory for the end-effector of the UR5 robot. The trajectory is designed in the Cartesian space in the parametric form of a Lissajous curve:  
\[\begin{cases}x=x_{0}+cos\theta\cdot A_{x}\cdot cos\omega_{x}t-sin\theta\cdot A _{y}\cdot sin\omega_{y}t\\ y=y_{0}+sin\theta\cdot A_{x}\cdot cos\omega_{x}t+cos\theta\cdot A_{y}\cdot sin \omega_{y}t\\ z=z_{0}\end{cases} \tag{1}\]  
In Equation (1), \(x,y\), and \(z\) are the coordinates of the curve, \(x_{0},y_{0}\), and \(z_{0}\) are the location of the curve origin in the workspace of the robot, \(A_{x}\) and \(A_{y}\) are the major and minor axis length parameters, respectively, \(\omega_{x}\) and \(\omega_{y}\) are the angular frequency parameters, \(\theta\) is the curve tilt, and \(t=[0,\,2\pi]\) is the parameter of the curve. In our case, we have defined \(A_{x}=A_{y}=0.1\) m, \(\omega_{x}=36\) rad/s, \(\omega_{y}=72\) rad/s, \(\theta=1\) rad, and \((x_{0},\,y_{0},\,z_{0})=(0.36,\,0.24,\,0.1)\) m. The obtained curve is shown in Figure 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Joint** & **a [m]** & \(\alpha\) **[rad]** & **d [m]** & \(\theta\) **[rad]** \\ \hline
1 & 0 & \(\pi/2\) & 0.089159 & \(q_{1}\) \\
2 & \(-0.425\) & 0 & 0 & \(q_{2}\) \\
3 & \(-0.39225\) & 0 & 0 & \(q_{3}\) \\
4 & 0 & \(\pi/2\) & 0.10915 & \(q_{4}\) \\
5 & 0 & \(-\pi/2\) & 0.09465 & \(q_{5}\) \\
6 & 0 & 0 & 0.0823 & \(q_{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The D-H parameters of the UR5 robot arm.  
Figure 1: The UR5 manipulator by Universal Robots used for the experiments.  
To fix the TCP position in space we have assumed a constant TCP frame orientation expressed as \(RPY\) (\(\pi\), 0, 0). Furthermore, we have scaled the obtained trajectory in time, such that the duration of the trajectory is \(T=10\) s.  
The Cartesian space trajectory described above is subsequently expressed in the joint space of the robot. This is achieved in the following manner. First, the Cartesian trajectory is sampled at 50 points \(\tau=\) [0:0.2:10] s. The sampled Cartesian trajectory can be presented as a list of pairs \(TP_{i}=\) (\(t_{i}\), \(P_{i}\)), where \(P_{i}\) denotes the desired pose of the TCP frame at time \(t_{i}\).  
Assuming the initial robot configuration \(q_{0}=\) [3.73, \(-\)1.28, 0.99, \(-\)1.28, \(-\)1.57, 2.18], we have obtained IK solutions for all the subsequent points of the trajectory using a Jacobian IK solver. We have thus obtained a sequence of trajectory waypoints in the configuration space expressed as a list of pairs \(TQ_{i}=\) (\(t_{i}\), \(q_{i}\)), where \(q_{i}\) denotes the robot configuration achieved at time \(t_{i}\).  
Based on the trajectory waypoints \(TQ_{i}\), we have constructed cubic spline interpolators \(CS_{j}\) for each of the \(j\)-th joint of the UR5 robot. We can denote the spline interpolator \(CS_{j}\) as a function that generates the desired joint position \(q_{j}(t)\) and the desired joint velocity \(\dot{q}_{j}(t)\) given as:  
\[CS_{j}=\left\{q_{j}(t),\dot{q}_{j}(t)\right\} \tag{2}\]
## 3 Materials and Methods  
### Problem Definition  
We consider using a series of remote sensing images to predict the average yield of a certain crop (such as soybeans) in an area of interest before the harvest season. Specifically, we will use a multispectral image sequence \(\left(I^{(1)},\cdots,I^{(T)}\right)\) covering the region of interest as input. Each multispectral image \(I^{(l)}\in R^{l\times w\times d}\) corresponds to images at different time t within a year. And \(l\) and \(w\) are the number of horizontal and vertical pixels, and \(d\) is the number of bands of image. Our goal is to learn a mapping to characterize the relationship between these original images \(\left(I^{(1)},\cdots,I^{(T)}\right)\) and the average crop yield \(y\).  
\[y=F\left(I^{(1)},\cdots,I^{(T)}\right) \tag{1}\]
## 5 Discussion  
### LULC Classification Using SVM Algorithm  
The overall accuracy and Kappa hat coefficient for the thematic accuracy of the LULC map in 2009 and 2019 were 90.86 and 87.00%, and 89.59 and 85.85%, respectively. Kappa hat coefficient values of more than 80% represent a substantial agreement between the classification map and the reference data [66]; likewise, the overall accuracy of the LULC maps--higher than 85%--indicates that they can provide an acceptable result [67].  
In addition, the overall accuracy and Kappa hat coefficient of the current study were consistent with other researchers who have classified LULC data based on Landsat imagery using the SVM algorithm [68; 69; 70; 71]; however, to apply SVM for LULC classification, users must select sample points between the boundaries of LULC classes precisely, where mixed pixels are common, in order to ensure accurate classification [72]. Therefore, selecting the SVM algorithm's training points for LULC classification under the EnMap BOX software requires time and skill.
## 3 Proposed Methodology
### Text Detection
#### 3.2.2 The Process of Text Detection  
The process of identifying text in an image takes many forms. First, each entered picture is binarized to improve the visibility of the text. Binarized images are passed through CCA and LSTM, where text blobs are disjointed by additional-size blogs. For this purpose, the CCA applies the image and identifies the blob's adjusted areas (x and y), which coordinates the pixel values and forms the boundary box. Next, the pixels in each bounding box are sent to the pre-trained LSTM model with pixel values, whether the box is a text or a different shape of the block sequence. After success, the boundary box is cut into text line images complete to be inserted in the neural network to recognize the text. The process works as follows:  
* For each scanned image,
* binarize image;
* pass binarized image to CCA;
* image is segmented into blobs of objects;
* pass each blob to a pertained LSTM model;
* if blob contains text;
* crop image to new image on generated blob dimensions;
* take each newly generated image;
* store locally for text recognition;
* else;
* ignore the blob;
* break;
* move to the next image.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CSPF-TE & constraint shortest path first-traffic engineering \\ DiffServ & differentiated services \\ DP & data plane \\ DSCP & differentiated services code point \\ FEC & forwarding equivalence class \\ FIB & forwarding information base \\ FIFO & first in first out \\ FPGA & field programmable gate array \\ FSO & free-space optical \\ GEO & geostationary orbit \\ HILS & hardware-in-the-loop-simulation \\ IETF & Internet engineering task force \\ IGSO & inclined geosynchronous orbit \\ IntServ & integrated services \\ ISL & inter-satellite link \\ ISTNs & integrated satellite-terrestrial networks \\ LANs & local area networks \\ LDP & label distribution protocol \\ LEO & low earth orbit \\ LER & label edge router \\ LFIB & label forwarding information base \\ LMP & link manager protocol \\ LSDB & link-state database \\ LSP & label switching path \\ LSR & label switching router \\ LVDS & low voltage differential signaling \\ Mbps & Megabits per second \\ MEO & middle earth orbit \\ MIPS & microprocessor without interlocked piped stages \\ MP & management plane \\ MPLS & multi-protocol label switching \\ NMA & network management agent \\ NMS & network management system \\ NP-hard & non-deterministic polynomial-hard \\ OAM & operation administration and maintenance \\ OSPF & open shortest path first \\ PowerPC & Performance optimization with enhanced RISC-performance computing \\ QoS & quality of service \\ RSVP-TE & resource reservation protocol-traffic engineering \\ SCID & spacecraft identification \\ SDNs & software defined networks \\ SNMP & simple network management protocol \\ \end{tabular}
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 2 Materials and Methods
### Visualization on HoloLens 2  
For the visualization of the 3D mesh obtained using the previously described methods in an augmented reality experience, an application designed for execution on HoloLens 2 was developed using Unity 2020 software (version: 2020.3.48f1). This project was configured following Microsoft's guidelines, ensuring full compatibility with augmented reality devices. Additionally, interactive features to enhance the user experience were incorporated by the Mixed Reality Toolkit (MRKT) [47]. This means that the user has complete control over the 3D model, being able to move and rotate it using hand tracking performed by the integrated cameras in the headset.  
To manage the limited computational resources of the headset, we configured the application to provide a smoother user experience. This involved enabling the headset to establish a wireless connection with a desktop computer for handling resource-intensive calculations due to its hardware.  
Specifically, Unity3D was used to develop a software capable of visualizing a 3D object (.obj file format). Additionally, object manipulation feature was implemented. Manipulation allows the user to move, rotate, enlarge, or shrink the object through specific manual gestures that are captured by the cameras of HoloLens 2 and interpreted using MRTK scripts for gesture recognition. In particular, these gestures include: grasping the object with the fingers and moving the hand, rotating the wrist while holding the object with the fingers, and bringing the hands closer together or moving them apart in the case of grasping with both hands. Therefore, this feature allows the user to manipulate the hologram projected into real space by the AR viewer with simple and intuitive hand movements.
## 2 Materials and Methods
### Resampling Techniques
#### 2.4.4 SMOTE Combined with Tomek Links (SMOTE-Tomek)  
SMOTE combined with Tomek links (SMOTE-Tomek) is another hybrid algorithm that balances data by generating synthetic minority class instances and subsequently removing noisy and borderline instances using Tomek links, an undersampling technique. A Tomek link exists between two instances from different classes that are each other's nearest neighbors in the feature space. By removing these links, ambiguous data points are eliminated, resulting in a cleaner and more balanced dataset [35].
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
###### Abstract  
This paper discusses several cases of the Offline Nanosatellite Task Scheduling (ONTS) optimization problem, which seeks to schedule the start and finish timings of payloads on a nanosatellite. Modeled after the FloripSat-I mission, a nanosatellite, the examples were built expressly to test the performance of various solutions to the OINTS problem. Realistic input data for power harvesting calculations were used to generate the instances, and an instance creation procedure was employed to increase the instances' difficulty. The instances are made accessible to the public to facilitate a fair comparison of various solutions and to aid in establishing a baseline for the OINTS problem. Additionally, the study discusses the various orbit types and their effects on energy harvesting and mission performance.  
instance generation; nanosatellite; QoS; OINTS 2023  
[PERSON], Lio  
1 Department of Electrical Engineering, Federal University of Santa Catarina (UFSC), Florianopolis 88040-900, Brazil 1  
2 Department of Aeronautical Engineering, Sao Paulo State University (UNESP),  
Sio Joao da Boa Vista 13876-750, Brazil 2  
Graduate Program in Applied Computer Science, University of Vale do Itajat (UNIVALI), Itajat 88032-901, Brazil 2
## 3 Discussion
### Effects of QG on Peroxide Value (PV) and Acid Value (AV)  
PVs are used to detect peroxide formation in the early stages of lipid oxidation [49]. In this study, regular increases in PV as a function of the accelerated oxidation period were  
Figure 6: Peak area of various volatile fractions from soybean oil treated with different antioxidants according to HS-SPME-GC-MS.  
observed for all soybean oil samples after all time intervals, and these were attributed to the formation of primary oxidation products in the samples. Compared with that for pure soybean oil, the PV seen with QG use was significantly decreased, and the oxidation rate was also significantly decreased, which indicated that QG prolonged the initiation stage of soybean oil oxidation. Simultaneously, the antioxidant effect of QG was better than those of the positive controls BHT and TP, which may be because there are 6-OH groups in the aromatic ring of QG, while BHT has only one OH group, and TP is the general name for the polyphenols in tea. Studies have shown that quercetin, which is a nucleophile, delayed the formation of early lipid peroxidation products in oil [50]. Therefore, we speculate that QG is a polyhydroxy phenolic compound with a hydrogen-donating substituent on a benzene ring, since the antioxidant effects of phenolic compounds are related to their hydrogen-donating ability, that is, the number of ortho- and para-OH groups, the bond dissociation enthalpies (BDE) and the formation of hydrogen bonds [51]. QG provides more binding sites than quercetin, and QG can also function as a nucleophile; this was responsible for prolonging the initiation period of the oxidation reaction.  
During oxidation, hydroxyl free radicals accumulate in large quantities, and long-chain polyunsaturated fatty acids react with free radicals to form alkanes, alcohols, ketones, aldehydes and finally, acids, which leads to rancidity in oils [52]. The free fatty acids in soybean oil were determined by measuring the acid value, which is the standard parameter for oil rancidity [53]. In this study, compared with the pure soybean oil, increases in the acid values of oil with QG and the positive control were inhibited during accelerated oxidation, which was consistent with the literature report [54]. QG reduced the content of free fatty acids formed during accelerated oxidation. It may be that QG is a flavonoid compound with various aromatic ring substituents and can serve as a hydrogen-donating antioxidant to delay the rancidity of soybean oil.
## 3 Study on Surface Integrity of Milled Workpiece Surface
### Microscopic Topography of Milled Workpiece Surface
#### 3.3.2 Effect of Lubrication Parameters on the Hardness Distribution of the Milled Workpiece  
In this section, the hardness distribution of the titanium alloy surface is measured using the cross-sectional method. The milled TC21 workpiece is cut to obtain a cross-section perpendicular to the machined surface, and the cross-section is ground using sandpaper and finely polished. Figure 21 shows the hardness distribution of the surface layer for different machining methods (dry milling, MQL1 (40 mL/h), MQL2 (80 mL/h)). MQL can reduce the temperature in the machining area and effectively mitigate the thermal softening effect of the machined surface. The lubricant can effectively improve the contact condition of the milling cutter and workpiece and reduce the deformation degree of the machined surface. Meanwhile, the lubricant film attached to the workpiece surface prevents the titanium element from coming into contact with the air, thus inhibiting the formation of a dense oxide film on the workpiece surface. The reduction of surface hardening caused by the weakening of material deformation and chemical reaction is stronger than the strengthening of surface hardening caused by the weakening of thermal softening. Thus, the hardness of the machined surface decreases after using MQL compared to dry milling.  
As can be seen from Figure 21, the trend of hardness variation along the depth of the surface layer is similar, and both go through the process of surface hardening, softening, rehardening, and stabilization in the substrate hardness. The milling heat is transferred to a narrow area where the heat is enriched during the milling process. The material is thermally softened to form a material softening layer. The microhardness in this area decreases rapidly compared to the workpiece surface. Dry milling has the widest softening layer since the massive cutting heat is transferred to the surface. Meanwhile, the minimum hardness value at the softening layer is the smallest in all three lubrication environments. For MQL (80 mL/h), the softening layer is the narrowest. The minimum hardness value of the softened layer is the largest as the least amount of cutting heat is transferred to the machined surface. Due to the thermal expansion of the material, the internal material adjacent to the softened layer is squeezed by the softened layer, which leads to dislocations and increases the density of the material. Eventually, rehardening of the material is caused.  
Figure 20: The effect of milling parameters on surface microhardness.
## 2 Materials and Methods
### Review of Recent In Situ Experimental Tests  
The analytical frame presented in Section 2.1 is verified against experimental tests recently published by [PERSON] et al. [30]. That work was originally proposed to characterize the fracture toughness of silicon by using the TCD. In detail, in situ fracture tests were carried out in a transmission electron microscope (TEM). Four specimens were considered, made of single crystal silicon, and fabricated by a focused ion beam (FIB) processing system. The fabrication process and orientation are simplified in Figure 2 while the detailed procedure is provided in [30]. Geometry and example of a sample are presented in the lower-half of Figure 2, whilst Table 2 gives the geometrical details.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & \(\omega\) **(deg)** & \(\rho\) **(nm)** & \(a\) **(nm)** & \(d\) **(nm)** & \(S\) **(nm)** & \(L\) **(nm)** & \(B\) **(nm)** & \(W\) **(nm)** & \(\mathbf{P_{f}}\) **(\(\mu\)N)** \\ \hline
1 & 33 & \(\approx\)10 & 155 & 217 & 837 & 1307 & 494 & 502 & 45.33 \\
2 & 48 & \(\approx\)14 & 161 & 96 & 682 & 1019 & 529 & 544 & 84.80 \\
3 & 59 & \(\approx\)20 & 179 & 119 & 651 & 1201 & 484 & 495 & 65.11 \\
4 & 68 & \(\approx\)6 & 144 & 87 & 875 & 1252 & 454 & 456 & 30.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical parameters of the blunt V-notched nano-cantilevers (see Figure 2).  
Figure 2: Example of main steps of fabrication process, orientation, final sample (specimen 2) and geometrical details of the blunt V-notched nano-cantilevers. At first, a block is carved from bulk single crystal silicon plate; later, the block is positioned on a gold wire and the nano-cantilever is cut; the notch is finally introduced by focused ion beam (FIB).  
The samples have different notch radii \(\rho\) and opening angles \(\omega\) measured at the best of authors capability by means of scanning electron microscope (SEM). It is worth noting that the samples have an \(a/W\) ratio varying between 0.3 and 0.36, while \(\rho/a\) varies between 0.07 and 0.11. Experimental mechanical characterization at the considered scale gave an ultimate strength \(\sigma_{u}\) of 13.9 GPa, and a fracture toughness \(K_{lc}\) of approximately 1 MPa m\({}^{0.5}\). The latter is determined experimentally by employing the TCD [30], pre-cracked samples [19] and even by considering the atomic scale by means of molecular simulations [15; 22]. The fracture tests are realized by pushing the nano-cantilevers towards an indenter which is able to detect the applied load. Table 2 summarizes the load at failure, P\({}_{t}\), as obtained in [30], which should be consulted for additional details on experimental procedures and loading device.