esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency of Machine Sanding of Wood
## 4 Discussion
Based on the mathematical analyses, it was found that the sanding belts in the second stage of blunting behave similarly. The belts of grit number (P60) dull faster than belts with higher grit numbers (P180). At the same time, differences were found between softwood and hardwood in terms of the efficiency of the sanding process. The graph in Figure 12 shows the average sanding times with belts of different grit numbers in the individual sample sets. The graph also shows the Brinell hardness of individual wood species (the macro-hardness determination method was selected from two common hardness measurement methods [30]). The average sanding times for sample sets of two wood species are interesting. In the case of ash, the shortest sanding time was obtained for the P120 belt, while in the case of the other tested materials, the shortest sanding time was usually for the P60 belt (in the case of walnut, these times were more or less equal). This can be explained by the fact that it is the hardest species of wood and in this case, the optimal abrasive belt grit from the point of view of sanding efficiency fell on the belt of medium grain (according to the grain size effect described in the publication [PERSON] et al. 1979 [31] caused by the influence of the elasticity of wood [32]). Another interesting species of wood is spruce. In the case of samples made of this material, the greatest effect of the grit number of the sanding belt on the average time for complete sanding of the sample set was observed. The sanding time with the P120 belt increased by as much as 120% compared to the sanding time with the P60 belt. For other grits, these times were either slightly shorter (by 15% for ash and 5% for walnuts) or greater (from 13 to 42%). Figure 12: Average sanding time for sample sets sanded with different grit sizes. The times of sanding with belts of different grit numbers of the individual sets of samples do not seem to correlate with the Brinell hardness of the tested wood species. For example, walnut, which is twice as hard as alder, shows an average sanding time similar to that of alder. Among the softwood species, average sanding times of pine wood are roughly half of that of spruce, although the hardness of both species is similar. The test results show that hardness is not the only factor affecting sanding efficiency. The influence may be caused by other tribological properties, e.g., the instantaneous coefficient of friction, which depends both on the type of wood, hardness, as well as the roughness and temperature, which are time-varying during sanding and dependent on the grit of the sanding belt [33].
## 3 Results and Discussion
### Sensory Evaluation
#### 3.3.2 Electronic Tongue  
The electronic tongue was used to evaluate the basic tastes, including umami, saltiness, acidity, bitterness, and astringency, while further observing the richness, astringent aftertaste (aftertaste-A), and bitter aftertaste (aftertaste-B) [23].  
As shown in Figure 7, the response values of the microcapsule (astringency 1.88 \(\pm\) 0.16 and aftertaste-A 1.77 \(\pm\) 0.04) and mixture (astringency 7.13 \(\pm\) 0.10 and aftertaste-A 4.07 \(\pm\) 0.05) showed a difference in astringency and aftertaste-A. This indicates that the astringency of RJSR was well-masked. However, the difference between the response values of bitter and aftertaste-B was not as significant (microcapsule bitterness 3.58 \(\pm\) 0.03 and aftertaste-B 1.77 \(\pm\) 0.02; mixture bitterness 5.70 \(\pm\) 0.09 and aftertaste-B 3.16 \(\pm\) 0.12), indicating that the bitter substances in RJSR might have relatively good water solubility, leading to their location on the surfaces of the microcapsules. The difference between sour and salty might have been caused by the acid and salt introduced during microencapsulation. As shown in Figure 8a, astringency, aftertaste-A, bitterness, and aftertaste-B contributed greatly to both LA1 and LA2 (both \(>\)0.3), while the contribution rates of the other indicators were relatively small (one item or both \(<\)0). The total contribution variances of PC1 and PC2 for direct electronic nose measurement (Figure 8b) were 60.6 and 39.1%, adding up to 99.7%. This also means that the first two PCs were sufficient to explain the total variance of the datasets. The greater the degree of dispersion was between the data points, the greater the differences among the samples [37]. In addition, as shown in Figure 8b, there was no overlap between the Mix. and the microcapsules. This selective microencapsulation phenomenon was also observed by [PERSON] et al. [21], demonstrating that these two samples had differences in taste. The above results confirm that the unpleasant flavor of RJSR was successfully altered.  
Figure 7: Radar image of electronic tongue results.
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 2 Adaptive Environment Partitioning Invariant Learning Framework
### The K-Means Clustering Algorithm  
This method divides all samples into \(k\) classes according to the distance between the sample points, to ensure that each sample is as close as possible to the class center [26]. In this work, the Euclidean distance serves as the criterion for measuring the proximity between samples, the basic process is shown in Figure 3 [27].  
The convergence criterion function \(E\) is shown in Formula (7).  
\[E=\sum_{i=1}^{k}\sum_{x\in C_{i}}|x-\overline{x}_{i}| \tag{7}\]  
where \(\overline{x}_{i}\) is the mean of the samples in the cluster \(C_{i}\). As one of the most widely used clustering algorithms, K-means clustering features low computational complexity, fast convergence speed, and excellent clustering effect. Therefore, this paper uses the K-means  
Figure 3: The schematic representation of the K-means clustering algorithm.  
clustering method to cluster the image and environment, validating the effectiveness of environment classification based on spurious features.
## 3 Results  
### Sensor Validation with Chemical Standards  
In this study, the sensor's response to seven common VOCs found in food products was investigated. These VOCs have different sensory attributes, which could be pleasant or unpleasant depending on the type of food in which they are found (Figure 3). The sensor demonstrated cross-reactiveness, as each receptor could react with multiple VOCs.  
In general, receptor Nos. 1-12 were more sensitive to acids, while receptor Nos. 13 and 14 were more sensitive to aldehydes. Receptor Nos. 13 and 14 were formulated with chemicals that could react specifically with aldehydes. Receptor No. 13 was based on the reaction between aldehydes and hydroxylamine sulfate; the acid produced by this reaction could be easily detected using a suitable pH indicator [29]. Notice that receptor No. 13 could undergo a slight color change in response to acids, as a trace amount of Na\({}_{2}\)CO\({}_{3}\) was added to adjust the color of the indicator solution. Yet such color change was insignificant compared to that caused by aldehydes. Receptor No. 14 was made with [PERSON]'s reagent, which is commonly used to detect aldehydes colorimetrically. Interestingly, the receptor was found to turn pale yellow and light blue upon reaction with furfural and phenylacetaldehyde vapors, respectively, instead of the commonly observed pink color caused by other aldehydes. Among the acid-sensitive receptors, receptor Nos. 1, 6-9, 11, and 12 were also found to undergo color change upon exposure to certain aldehydes, especially No. 9, which could change color in response to pentanal, heptanal, and furfural. This could be attributed to the chemical properties of the indicators. Receptor No. 1 was loaded with Pararosaniline base, which has aryl amine groups that can react with carbonyl groups [37; 38]. Receptor Nos. 6-9 contained pH indicators mixed with Na\({}_{2}\)CO\({}_{3}\) or KOH. The color change could be due to the reaction between aldehydes and bases [27]. In addition, receptor Nos. 11 and 12 were solvatochromic dyes that were introduced for non-specific detection of polar VOCs. Since the receptors possessed different affinity to the VOCs, the results suggested that the color fingerprint might be used to estimate the types of VOCs present in a given sample.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 6 Discussion  
### Performance Analysis  
Let the modulation order be denoted as \(M\) and the channel coding rate as \(R_{c}\). The transmission rate for a single GOCDM block is, then, given by \(R_{s}=\frac{NR_{c}\log_{3}(M)T_{\rm CP}}{T(T_{\rm CP}+T)}\).  
Next, the computational complexity of the GOCDM underwater acoustic communication system is analyzed. Since GOCDM modulation applies a DFnT to \(M\)N-dimensional vectors, the computational complexity at the transmitter is \(O(MN\ \log_{2}N)\), lower than OCDM and OFDM. At the receiver, channel equalization requires a \(K\)-point FFT, resulting in  
Figure 10: Channel multipath delay in the deep-sea channel: (**a**) A–B, (**b**) C–D, and (**c**) E–F.  
a computational complexity of \(O(K\,\log_{2}\,K)\). It follows that the computational complexity of GOCDM remains comparable to that OFDM and OCDM, ensuring practical feasibility in underwater acoustic communication systems.  
We then include SC-FDE in the discussion. At the receiver side, the implementation complexity of SC-FDE is identical to that of GOCDM, with both being \(O(K\,\log_{2}\,K)\). On the transmitter side, SC-FDE requires a pulse-shaping filter. Assuming the filter order is P, SC-FDE exhibits an additional complexity of \(KP-\frac{1}{2}K\,\log_{2}\,K\) compared to OFDM. Furthermore, to mitigate inter-symbol interference, SC-FDE needs to increase the symbol duration, which, consequently, reduces the transmission rate to some extent.  
The performance comparison between SC-FDE and OCDM has been thoroughly discussed in [19]. Specifically, OCDM is equivalent to SC-FDE when the cyclic prefix is sufficient, while OCDM outperforms SC-FDE when the channel multipath delay exceeds the cyclic prefix length. This advantage stems from OCDM's multicarrier nature, which distributes the localized interference across all subcarriers to mitigate its impact when the partial time-domain signals are corrupted. Similarly, GOCDM inherits this advantage as a multicarrier scheme. In contrast, SC-FDE, being a single-carrier system, lacks this capability and, consequently, suffers a significant performance degradation under excessive multipath delay conditions.
## 4 Discussion
### Differences in Findings  
The study identified 12 operational environmental risk factors that affect sleep that can potentially be monitored using SPs/SWs tandems--climatic attributes (wind velocity, temperature, humidity, precipitation, and cloud cover), physical factors (ALAN and noise), and air pollution (CO\({}_{2}\), TVOC, PM\({}_{2.5}\), and CO). Of these factors, nighttime light and noise were found to be more monitored in empirical studies (53% of the publications we analyzed), while the monitoring of precipitation, cloud cover, TVOC, PM\({}_{2.5}\), and CO was least frequent (7% each). This outcome contradicts the findings of [PERSON] et al. [12], according to which the most frequently monitored environmental risk factors affecting sleep are second-hand smoke, air pollution, and light pollution, while the least frequently used factors are noise pollution and heavy metals. The differences in findings can be attributed to the fact that the current study employs the PRISMA method, using the \"Covidence\" tool for screening and data extraction, which minimizes potential biases associated with studies due to specific research perspectives and priorities [89]. This methodological advantage distinguishes PRISMA from other survey methods, such as content analysis [90], joint citation analysis [91], and citation-based content clustering [58], which lack established study-section protocols and may thus overlook crucial bibliographic information [92].
## 4 Experiment and Discussion
### Experimental Process  
Owing to the dry surface of the joint threads, a small amount of threading dope needs to be applied for lubrication before connection. In order to minimize the influence of the thread dope on the experiment, the thread dope was evenly applied and then wiped clean with a dry cloth.  
It is important to check the sealing and leak points of the joint before performing the tensile test. The check method is to immerse the connected, gas-filled joint in a water tank and observe whether bubbles emerge. If a leak occurs, this sample will be discarded and replaced with a new sample and test the new joint again. The tensile test can be performed after the sample is successfully prepared. The experimental process is shown in Figure 20. The test procedure is as follows:  
1. Adjust the two grips of the universal testing machine to the appropriate distance and install the sample.
2. Connect the air injection port on the joint to the outlet of the air compressor and fill the cavity with compressed air. After the pressure gauge reading stabilizes at 0.6 MPa, close the valve between the air pressure gauge and the air compressor.
3. Allow the sample to stand for 10 min while continuously observing the pressure readings. If there is no significant change in the pressure gauge reading, indicating that no leak has occurred, and then proceed to the next step. However, if the pressure is significantly reduced, indicating a leak, replace the joint.
4. Set the tensile force of the universal testing machine to 500 kN, the crosshead displacement rate to 5 mm/min, and the saturation loading time to 5 min.
5. Start the universal testing machine. As the tensile force increases from 0 to 500 kN, the pressure gauge value is read every 5 s. During the loading phase, a 0.02 MPa change in the pressure gauge reading indicates the formation of the initial leakage. Therefore, the tensile force that corresponds to this stress reduction is recorded as the initial leakage tensile force. During the load saturation phase, the gauge values are read every 30 s.
6. After reaching the saturated loading time, the universal testing machine is unloaded, after which the grips are opened and the sample is replaced. Repeat steps 1 through 5 for the next set of experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Taper** & **Thread Height (mm)** & **Thread Angle (degrees)** \\ \hline Sample 1 & 1:32 & 2.24 & 30 \\ Sample 2 & 0 + 1:32 & 2.24 & 30 \\ Sample 3 & 1:32 & 2.24 & 15 \\ Sample 4 & 1:32 & 2.44 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of the test samples.  
Figure 19: Test samples.
## 5 Discussion
Proceeding to the burn severity, it is important that it is assessed also by field surveys to validate the results derived from the satellite imagery. An issue faced through the use of NBR is the misclassification of unburned areas as burned ones. This is caused by areas having similar spectral properties in the used spectral bands. In addition to the use of masks to minimize these effects, a manual selection of the burned consulting the post-fire Sentinel-2 natural and false-color images was also performed to maximize the accuracy. Continuing with the empirical part, generally, the datasets proved adequate to meet this study's purposes. The land cover retrieved from the CLC 2018 dataset gave a clear view of the affected burned areas and proved to be sufficient in in \(P\) factor estimation. However, a more detailed dataset such as Urban Atlas 2018 could further improve them. With regards to the used DEM, a high-resolution DEM like that of Hellenic Cadastre with a 5 m resolution [2] would have given higher detail in the RUSLE as well as a closer resolution to the burned areas. In this context, the difference in resolution between the 10 m of burn severity and the 25 m selected based on DEM of RUSLE soil loss creates a difference in detail. Considering the total extent of all four wildfires, a higher resolution could have caused processing volume-related problems. The meteorological dataset concerning rainfall data for the creation of \(R\) factor was based on a dense network of stations for the examined time periods. In general, dense point networks provide better outcomes in the implementation of spatial interpolation techniques. Soil-related datasets and, more specifically, the topsoil ones from ESDAC used in this study, led to the \(K\) factor estimation but their resolution is very low (500 m) thus degrading detail. In addition, quality of the ESDAC raster datasets in combination with their low resolution made it necessary to apply the kriging spatial interpolation technique in order to improve both quality and resolution. Finally, considering the RUSLE implementation, the \(C\) factor, which is based on NDVI, proved very useful in the RUSLE estimation before and after a wildfire event. However, it might present some issues in regards to distinguishing between burned and non-burned areas. Alternatively, for the \(K\) factor, the soil erodibility dataset by ESDAC would have been also suitable to be used instead of the previously mentioned ESDAC datasets as a ready-to-use solution. The RUSLE modeling validation could have included fieldwork after the fire event. Classification of the soil loss to create soil erosion risk categories was challenging and the final selection was performed after tests with other existing classification methods. The web app is maintained and updated through the ESRI's ArcGIS Online services while content updates can also be conducted if more products are added or by updating existing ones. In addition, any update to the UI or widgets can be conducted by taking into consideration end-users' feedback and when new or updated features are provided by ESRI.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Measurement Requirements
#### 3.2.1 MWIR and LWIR Band Requirements
Given that this satellite will be designed to observe Canadian wildfires during the peak burn period, the saturation point was defined in anticipation of observing the extreme intensities of Canadian boreal crown wildfires (e.g., [77]) during their peak period. This is a condition not yet achieved through LEO satellites. Therefore, a sub-pixel scene was modeled in which a 50-m deep flame front with net blackbody emission equivalent to \(\sim\)900 K crosses the pixel area diagonally, while the background area remains \(\sim\) 300 K. Under such conditions the saturation point for the MWIR and LWIR spectral bands vary in accordance with their spectral band and with respect to the sub-pixel area of the wildfire. As such, the required dynamic ranges are bound by spectral band selection and spatial resolution (Table 5; Figure 3). Ideally the dynamic ranges for these bands would be stated in spectral radiance ranges at Top of Atmosphere (TOA); however, until the Spectral Response Functions (SRF) and spatial resolutions are known, the modeling exercise in Figure 3 cannot be completed. Atmospheric transmittance modeling as performed for the VIS and NIR bands should be replicated for MWIR and LWIR when appropriate. Given the interdependency of spatial resolution and dynamic range it is not necessarily advantageous to target unnecessarily fine spatial resolution imagery in the MWIR and LWIR bands. Recent experiences in analysis of the VIIRS active-fire products have demonstrated increasing errors of commission (i.e., false positives) associated with finer scale active-fire detections [55; 78]. [PERSON] (2016) [70] found that ROS measurements could be reasonably estimated under crown wildfire conditions at spatial resolutions as coarse as 500 m. Given FMF-5, MWIR and LWIR spatial resolutions were defined with the intent to be comparable to the VIIRS I-bands (\(\sim\)375 vs. 400 m for WFS) in order to serve as a complimentary data source to the VIIRS data which is captured a few hours earlier in the day (Figure 1).
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 3 Pose Regression Methods
### Avatar Pose Regression  
In VR applications, we can obtain the position of the VR user's specific body joints in the real world. The HMD and controllers track the user's head and both hands. We then apply our pose regression model to estimate the other body joints. The input to the pose regression model is the positions \(\mathbf{p}_{1}\), \(\mathbf{p}_{2}\), and \(\mathbf{p}_{3}\), and orientations \(\mathbf{r}_{1}\), \(\mathbf{r}_{2}\), and \(\mathbf{r}_{3}\) of a HMD, left controller, and right controller, respectively. It appraises the values of \(\mathbf{p}_{4}\), \(\mathbf{p}_{5}\), \(\mathbf{p}_{6}\), \(\mathbf{p}_{7}\), \(\mathbf{p}_{8}\), and \(\mathbf{p}_{9}\) as outputs for the avatar. After estimating the positions of the joints, we calculate the orientation of the joints of the entire upper body of the VR user.  
We use the avatar's parent-child hierarchy to estimate the orientation angles of joints. We compute the range of orientation angles of the neck, shoulders, pelvis, and spine joints using the estimated positions of the joints and the known orientation angles of the head and wrist joints. The neck joint is oriented using the values of the head joint and its position. Let the directional vector from the neck and head joints be the \(y\)-axis of the neck. We then determine the orientation angles of the neck by rotating the head coordinate system to align its \(y\)-axis with that of the neck.  
Similarly, we determine the orientation angles of the shoulders, pelvis, and spine joints. For the shoulder joints, the positions of the left and right shoulders are used to define the \(x\)-axis of the shoulder coordinate system. The neck coordinate is rotated to align its \(x\)-axis with the shoulder coordinate system. The coordinates of the pelvis and spine joints are defined using both the coordinates of the neck and shoulders.  
After these steps, we calculate the orientation angles of the elbow joints. With known position and orientations of the wrists and shoulders, the orientation values of the elbow joints are calculated as follows. The \(y\)-axis is computed as the unit vector along the line from the elbow to the wrist joint locations, an \(x\)-axis is a unit normal vector to the plane defined with three points, including the elbow, wrist, and shoulder joint locations, and the \(z\)-axis is a cross product of the \(x\)- and \(y\)-axes. We point an imaginary line from the shoulder to the hand, compute the distance to determine the threshold value of the elbow using the cosine rule [44], and produce the most probable state of the elbow.