esa-sceva/satcom-chunk-collection huggingface dataset:
# A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series
## 2 Summary of Study Area and Data Sources  
### Summary of Study Area  
The Beijing-Tianjin-Hebei (Jing-Jin-Ji) region refers to a region consisting of three districts, Hebei Province and its inner rings of Beijing and Tianjin (Figure 1), which are located in the northern part of eastern China. The region's topographic elevation decreases gradually from northwest to southeast, and it comprises a mountainous and plain-dominated landscape. The Beijing-Tianjin-Hebei region is located on the east coast of the mid-latitude Eurasian continent, and it has a typical temperate semi-humul and semi-arid continental monsoon climate. The four seasons are able to be distinguished from one another, and precipitation is concentrated in significant wet and dry periods. The north-south temperature difference in this region is very large. Taking the Great Wall as the border, the average temperature up to the north of the Great Wall is less than 10 \({}^{\circ}\)C, whereas that of the northern Bashang Plateau is only \(-0.5\)\({}^{\circ}\)C. The temperature to the south of the Great Wall is higher than 10 \({}^{\circ}\)C, and the annual mean temperature of the southern plain of Handan is 14.2 \({}^{\circ}\)C. The north-south temperature difference is 14.7 \({}^{\circ}\)C. The average temperature in the coldest month, January, ranges from \(-2\)\({}^{\circ}\)C to \(-16\)\({}^{\circ}\)C, and the temperature in the hottest month, July, ranges from 20-28 \({}^{\circ}\)C, with a north-south temperature difference of 22-44 \({}^{\circ}\)C [27].  
The zoning criteria of the study area are based on the relationship between elevation and altitude. We determined the zoning of the study area using US Geological Survey (USGS) digital elevation model (DEM) data with a resolution of 30 m according to the elevation of each station and basic principles, including the average distribution of stations at a similar elevation, a combination of concurrent occurrence and the relative homogeneity of regional climate characteristics, and the continuity of the spatial distribution (Figure 1).  
Figure 1: Study area and distribution of observation sites.  
We assumed that natural conditions, including the elevation, precipitation, dryness, and topography within a zone, were generally consistent.
## 1 Introduction
Using a Bayesian classifier [20], other authors have added the possibility of correctly identifying whether the chord the student is playing is the one requested. In this approach, students are shown an image of how the fingers should be positioned to play a chord, together with audio of how it should sound when played correctly [21], and a camera is then used to detect the student's finger positions via corresponding finger markers. This combination of audio overlay and 3D visual information is extremely well suited to guitar teaching [22]. Additionally, for both the bass guitar and the electric guitar, tests have been performed that pertain to musical instruction using video games like Rocksmith [23]. The authors argue that the use of music games can expand the conceptualization of what is \"playful\" and \"serious\" as opposed to traditional teaching. Experiments have also been run using a virtual musical instrument system (using Kinect) as an initiation method for someone who does not own the real instrument [24]. Furthermore, augmented reality (AR) has been used to mitigate the limitations of the different didactic materials used to learn guitar [18; 25]. This technology has been used in education for several reasons, including reduced costs and the improvement of hardware/software, the need for more user-friendly interfaces when interacting with non-expert users, and the need to work with other teaching methodologies using active practices [26]. In addition, the use of augmented reality facilitates the visualization of elements such as finger placement [27]. Examples include HoloKeys [28] and PIANO [29], which are augmented reality tools that help users learn to play a real piano [30]; the Internet of Musical Things (IoMusT), which is a research field within the Internet of Things seeking to achieve computer-human interaction with music [31]; and a piece of research entitled Malaysia Music Augmented Reality, the goal of which is to help students to learn a musical instrument at any time [32]. It is important that augmented reality systems provide information in a way that clarifies the perceptions of the original material, while forming an integral part of the entire learning workflow [33; 34]. The literature on AR in musical instrument instruction stretches over 14 years and includes different learning proposals. The novelty of the research detailed in this paper lay in the performance of a comparative study that compared an AR-based teaching methodology using an ad-hoc AR app against a traditional teaching methodology. The question to be addressed was whether the AR methodology would allow learners to pick up the basic skills needed to play the instrument more effectively than the traditional methodology. Therefore, the purpose of this research was to determine through a flexible and feasible system for users with minimal knowledge whether it is more efficient for beginner students to learn guitar with augmented reality than via a traditional teacher-led methodology.
## 2 Materials and Methods
### Measurements
#### 2.3.2 Measurement of the Muscle Activity  
To quantify the muscle activity of the participants' UT, MT, LT, and SA, data were gathered using wireless surface electromyography (EMG) (TeleMyo DTS; Noraxon Inc, Scottsdale, AZ, USA). The sampling rate was set to 1000 Hz, and the electromyography signals were amplified and subjected to band-pass filtering within the range of 20-400 Hz. Root-mean-square (RMS) values were then calculated from the electromyography data to quantify the signal amplitude. After rectifying and smoothing the signal, the root mean square (RMS) was computed over a time constant of 50 ms for a duration of 3 s. Before electrode placement, the skin was prepared as follows: it was shaved and cleaned with 70% isopropyl alcohol in order to reduce skin impedance [21].  
For the UT, the two electrodes were positioned parallel to the muscle fibers, with one electrode placed supermodially and the other inferolateral at a point 2 cm lateral to half the distance between the C7 spinous process and the lateral tip of the acromion process. The electrode of the MT was positioned at a midpoint along the horizontal line connecting the root of the scapular spine and the third thoracic vertebrae, while the LT electrode was placed obliquely along a line connecting the spinous process of the seventh thoracic vertebrae to the junction of the medial border of the scapula and spine of the scapula. The SA electrode was placed below the axillary region along the muscle fibers, anterior to the latissimus dorsi and posterior to the pectoralis major muscle [21; 22]. The maximal voluntary isometric test (MVIC) was applied to normalize the surface EMG data. The MVIC test positions were taught to the participants by the examiner, and sufficient training was performed to obtain the test position and contraction before the measurement. The MVIC was maintained at that specific point for a duration of 5 s. Subsequently, the analysis focused on the middle 3 s interval within each 5 s trial. Each muscle test was repeated three times. Then, the mean of the three measurements was taken as the standard [21; 22]. After MVIC testing, subjects were allowed to rest for 5 min. The electromyography activity of the UT, MT, LT, and SA were assessed during movement; specifically, arm elevation in the scapular plane (30\({}^{\circ}\) anterior to the coronal plane) to the ending posture that lasted 3 s. In the resting position, recording of the electromyography data was started. The threshold of three standard deviations (3 SD) above the baseline accurately represents the time of muscle activity [23]. In this study, the 3 SD of UT was used as the starting time for recording the 3-s RMS of electromyography signals. The RMSs of electromyography signals, recording activity for all muscles, were obtained and normalized by their respective MVIC, in order to obtain muscle activity during activity in terms of MVIC% [24].
## 2 Materials and Methods  
### Materials  
The epoxidized linseed oil (ELO) was supplied by Valtris Specialty Chemicals. This product appears as a yellow liquid with a viscosity of \(\sim\)1200 Pa.s, a functionality of 5.5 epoxyides per triglyceride and an average mass of 980 g\(\cdot\)mol\({}^{-1}\). To develop the resin-based matrix, ELO was crosslinked with dodecenyl succinin anhydride (DDSA; 90%), with the reaction being initiated by an equal mixture of N,N'-dimethylbenzylamine (BDMA; initiator) and 2,4,6-tris(dimethylaminomethyl)phenol (DMP-30; accelerator). This ELO-based resin was used as the matrix for the composites' production, in which various proportions of hydrochar (HC) and spruce bark (SB) powder were used as green and sustainable fillers. Hydrochar (HC) was obtained from spruce bark powder by HTC process at 280 \({}^{\circ}\)C. HC appears as a brown powder with a particle size distribution of around 400 \(\upmu\)m. The elemental analysis of the synthesized HC and raw SB was investigated by energy-dispersive X-ray spectroscopy (EDX). The elemental content of HC is \(\sim\)71.61% C, 21.11% O, 5.20% H, 1.44% Ca, 0.38% Cu and 0.26% Zn, while that of the SB is 58.26% C, 33.15% O, 5.84%, H, 0.05% Mg, 0.01% Al, 0.01% Si, 0.04% P, 0.05% S, 0.01% Cl, 0.33% K, 1.59% Ca, 0.02% Mn, 0.38% Cu, 0.27% Zn.
# Effects of Illuminance Level of Light Source on White Appearance of a Tablet Display
## 2 Methods  
A psychophysical experiment is conducted to investigate the whiteness percentage on the 6 th generation iPad with a black boundary to understand the white appearance of an e-reading device under varying illuminance levels. Observers are asked to rate the whiteness percentage under eight ambient lighting conditions comprising two levels of correlated color temperature (CCT) and four levels of illuminance.
## 3 Results and Discussion
### Core Extractability and Microencapsulation Efficiency
Results (Table 4) indicated that at a W:C ratio of 75:25, MEE was not influenced by the WHPI/lactose ratio in the WS and ranged from 95.8 to 96.8%. At a W:C ratio of 50:50, MEE ranged from 92.2 to 95.9% and it exhibited a small, yet significant, increase with the proportion of WHPI that was included in the WS. The overall lowest MEE was exhibited by microcapsules with a W:C ratio of 25:75 that also exhibited the strongest influence of the concentration of WHPI on MEE (Table 3). At a given wall composition, MEE of microcapsules with a W:C ratio of 75:25 was similar (\(p\) > 0.05) to that of microcapsules with a W:C ratio of 50:50 (Table 4), however, MEE of microcapsules with a W:C ratio of 25:75 was significantly lower than that at a higher W:C ratio (Table 4). The overall very high level of MEE and, in most cases, the very small changes in core extractability with extraction time (Figure 3) clearly indicated that, in all cases, the lactose content of the wall matrix was effective in hindering the solvent diffusion through the wall matrices. Results indicated that, except for two cases, MEE was mainly influenced by the proportion of core that resided at- or immediately below the outer surface of the microcapsules and could thus be considered to be \"surface oil\" [59]. Results (Table 4 and Figure 3) suggested that the latter was inversely related to the concentration of the WHPI in the WS, thus reflecting the aforementioned effects of the WHPI content on the transport of core droplets to the surface of the drying microcapsules, prior to crust formation. The level of MEE that was accomplished in the present study with wall systems consisting of blends of WHPI and lactose was higher than what has been reported for wall systems consisting of blends of WHPI and maltodext or corn syrup solids [30]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**WHPI/Lactose (\%/\%, \(v\)/\(w\))**} & \multicolumn{3}{c}{**Core Retention (\%)**} \\ \cline{2-4}  & **75:25 1** & **50:50** & **25:75** \\ \hline
2.5/17.5 & 91.15 \({}^{\text{a,C}}\) & 94.62 \({}^{\text{b,B}}\) & 97.14 \({}^{\text{c,B}}\) \\
5.0/15.0 & 83.72 \({}^{\text{a,B}}\) & 90.67 \({}^{\text{b,A}}\) & 93.02 \({}^{\text{b,A}}\) \\
7.5/12.5 & 85.40 \({}^{\text{a,B}}\) & 89.55 \({}^{\text{b,A}}\) & NA \\
10.0/10.0 & 70.91 \({}^{\text{a,A}}\) & 88.17 \({}^{\text{b,A}}\) & NA \\ \hline \hline \end{tabular} \({}^{1}\) Wall-to-core ratio (%:%, \(w\)/\(w\)); \({}^{\text{a}\prec}\) for a given wall system, mean values in a given row that are followed by different letters, differ significantly (\(p\) < 0.05); \({}^{\text{a,D}}\) for a given column, mean values that are followed by different letters, differ significantly (\(p\) < 0.05).  
\end{table} 
Table 3: The effect of wall composition and wall-to-core ratio on core retention during microencapsulation.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results
### Impact of the Compliance's Physical Length  
The series of simulations and results are presented in Figure 16 and in Appendix A, considering a length \(l_{m}\) of the equivalent spring equal to 0.05 m. The physical length of the spring denotes the available space of the mechanism to compress, as shown for the indicative case in Figure 21. It is, therefore, an important design parameter for the system.  
A series of simulations were run for a different spring's length to further enhance the discussion and the valuable conclusions. The algorithm developed searches for the range of stiffness \(k_{m}\) between 0.25-1.25 N/m, for a range of space debris mass \(m_{d}\) between 1-100 kg and for a range of minimum contact time required for completion of the capturing between 5-8 s. Specifically, Figure 22 displays the range of space debris masses that can be targeted and successfully captured for a range of equivalent spring stiffnesses and for various minimum contact times required for a spring's length \(l_{m}\) equal to 0.025 m.  
Figure 21: Spring deflection for the indicative case.  
Figure 22: Range of space debris to be targeted for different contact times.  
Based on this diagram, one could observe that, for a specific minimum contact time and spring's stiffness, the range of debris masses that can be targeted is smaller than the corresponding one when the spring's length \(l_{m}\) is equal to 0.05 m. This is because, at similar stiffnesses and contact time parameters, the retracting phase on the spring reaches its limit, resulting in possible damage to the servicer CubeSat. In other words, the contact time requirement may be fulfilled while reaching the spring's length limits, which may be dangerous for the servicer satellite. Hence, the ability to tune the equivalent stiffness coefficient using an impedance controller is even more necessary. Moreover, as shown in Figure 22, the maximum contact time achieved is no more than 8 s; this is an important contact time constraint. In conclusion, for an ADR system to target a wider range of debris masses while ensuring a realistic required time of contact, the spring's length for compliance--or alternatively the space that the system has to compress--must be carefully selected to be above a minimum value.
# Plant Disease Diagnosis in the Visible Spectrum
## 3 Materials and Methods
### Color Identification and Quantification
#### 3.4.2 Color Quantification  
Once the segment belonging to the plant has been identified, the quantification of the pixels that have colors associated to the diseases is executed using the following criteria.  
* White portion \[\frac{WP}{TP},\] (19)
* Black portion \[\frac{BP}{TP},\] (20)  
Figure 5: Evolution of the segmentation process: (**1**) Original image, (**2**) First guess codified by HSV thresholds, (**3**) Initial saliency map, (**4**) Final saliency map, (**5**) Segmentation after [PERSON]’s method, (**6**) Final segmentation after opening and closure.  
Figure 6: Evolution of the segmentation process: (**1**) Original image, (**2**) First guess codified by HSV thresholds, (**3**) Initial saliency map, (**4**) Final saliency map, (**5**) Segmentation after [PERSON]’s method, (**6**) Final segmentation after opening and closure.  
* Green portion \[\frac{GP}{TP},\] (21)
* Yellow portion \[\frac{YP}{TP},\] (22)
* Brown portion: \[\frac{BRP}{TP},\] (23) where \(WP\) is the number of white pixels; \(BP\) is the number of black pixels; \(GP\) is the number of green pixels; \(YP\) is the number of yellow pixels; \(BRP\) is the number of brown pixels; \(TP\) is the total number of pixels of the segment that represents the plant.  
With the portions calculated now, it is possible to estimate the portion of the tissue of the plant that has the presence of the following diseases.  
* White spots: White.
* Necrosis: Black portion + Brown portion.
* Healthy: Green portion.
* Chlorosis: Yellow portion.
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
###### Abstract  
In this paper, we propose a novel distributed unequal error protection (UEP) rateless coding scheme (DURC) for space information networks (SIN). We consider the multimedia data transmissions in a dual-hop SIN communication scenario, where multiple disjoint source nodes need to transmit their UEP rateless coded data to a destination via a dynamic relay. We formulate the optimization problems to provide optimal degree distributions on the direct links and the dynamic relay links to satisfy the required error protection levels. The optimization methods are based on the And-Or tree analysis and can be solved by multi-objective programming. In addition, we evaluate the performance of the optimal DURC scheme, and simulation results show that the proposed DURC scheme can effectively provide UEP property under a variety of error requirements.  
distributed rateless codes; unequal error protection; space information networks; multi-objective programming +
Footnote †: journal:  
Article
# Crack Location and Degree Detection Method Based on YOLOX Model
## 4 Experimental Results and Analysis
### Test Results and Discussion

Table 4: Performance comparison of different deep learning models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{4}{c}{**AP (\%)**} \\ \cline{3-6}  & & **Super Wide Crack** & **Wide Crack** & **Normal Crack** & **Thin Crack** & **Spalling 1**  \\ \hline YOLOXs & 88.78 & 90.58 & 91.98 & 86.98 & 74.48 & **99.86** \\ YOLOXm & 89.98 & 91.67 & 91.88 & 86.04 & 80.90 & 99.43 \\ YOLOXI & 90.17 & 90.08 & **92.59** & 88.44 & 80.28 & 99.47 \\ YOLOXx & **91.05** & 89.83 & 90.04 & **91.65** & **84.75** & 99.01 \\ \hline Faster R-CNN & 69.77 & 78.86 & 81.95 & 65.21 & 33.80 & 89.01 \\ DSSD & 88.92 & 91.43 & 89.57 & 82.21 & 84.72 & 96.69 \\ YOLOv5s & 85.85 & 92.16 & 87.51 & 77.56 & 73.35 & 98.67 \\ YOLOv5m & 86.33 & 92.91 & 87.12 & 82.05 & 71.11 & 98.46 \\ YOLOv5l & 87.85 & 95.23 & 91.10 & 81.35 & 73.04 & 98.53 \\ YOLOv5x & 88.11 & **95.36** & 88.57 & 81.32 & 76.58 & 98.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results of different object detection models. Figure 11 visualizes the partially recognized results of the 10 networks from the same image. There are three crack degrees in the example image 1 (I1), which are a super wide crack, a normal, and a spalling, respectively. Example image 2 (I2) contains two thin cracks, two wide, and a spalling. Although the above models could accurately identify these cracks in the image, some false detections exist during the test process, which directly impact the mAP values. However, the proposed method can effectively avoid too many wrong identifications. Figure 12a-c describes some examples of detected errors, which are roughly divided into three categories. The first type is undetected, as shown in Figure 12a from DSSD, that is, the algorithm does not detect the crack in the image; the second type is pseudo-detection, as shown in Figure 12b from Faster R-CNN, that is, the algorithm recognizes a non-existent crack; the third type is misdetection, as shown in Figure 12c from YOLOv5, that is, the algorithm incorrectly recognizes one crack degree as another crack degree. Figure 11: Examples of detection results by different deep learning models. Figure 12: Examples of false detections. In summary, the YOLOX performs well in the detection and assessment of different types of crack. First, the great mAP values suggest that the model's ability to detect cracks with different degrees is a relatively reliable performance. Secondly, high FPS and short inference time represent that the detection speed is very rapid and can be content with real-time detection. Thirdly, the short training time indicates that the network structure is not complex. Finally, the high AP for thin crack suggests that this model is suitable for small crack detection. Therefore, the trained YOLOX model is selected as the crack location and degree detector.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 5 Performance Evaluation
### Simulation Results  
Simulation results are shown in Figure 6. As Figure 5(a) shows, our proposed protocol achieves almost full coverage under different traffic scenarios. The other protocols show significant improvements when evaluated under MEC architecture in terms of data delivery.  
In terms of dissemination overhead, our protocol outperforms the other protocols as shown in Figure 5(b). This can be explained by the utilization of traffic condition information offered by the network edge in controlling the broadcasting suppression. However, our protocol shows an increasing overhead under high traffic flow rates. This is due to the fact that a considerable percentage of messages is disseminated via V2V communication. A noticeable improvement by around 40% in the dissemination overhead is shown in TURBO-MEC compared to the original version of TURBO. This improvement is due to the role of the edge in initiating the data dissemination process on behalf of individual vehicles. This role frees the vehicles from receiving redundant data at the first hop where data is excessively disseminated.  
Figure 5(c) shows the average dissemination delay values under different flow rates. The protocols under comparison present average delay values under MEC that are very close to those presented under VANET. Therefore, it can be concluded that the employment of a MEC architecture would not negatively affect the minimized dissemination delays of VANETs, but could significantly decrease the existing dissemination delays of cloud-based infrastructures as expected.  
Figure 5: SUMO NetEdit Map.