esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Intervals  
For a geotechnical log, intervals should relate to the geotechnical properties of the core rather than simply the start and end of a core run or a lithological interval. The same principle applies to face mapping. Sometimes, a significant geotechnical boundary may be present within a core run or along the slope face and should be included as a separate interval, as illustrated in Figure 3.  
Figure 3: Logging within geotechnical boundaries.  
The top of the interval is recorded in meters to two decimal places in the \"From (m)\" field as a down-hole depth from the collar when drill core logging or along a traverse from the starting point when face mapping. Similarly, the bottom of the interval is recorded in the \"To (m)\" field.  
The \"Interval Length (m)\" is calculated from the data captured in the \"From (m)\" and \"To (m)\" fields (i.e., Interval Length is equal to To minus From). The core run or traverse map interval should be no greater than perhaps 3000 mm (3 m) to provide a statistically admissible dataset. Each interval should also be at least 100 mm (0.1 m) in length.  
The majority of the core logging is carried out by monitoring the joints at every meter of the core. As seen in Figures 4 and 5, this kind of logging quickly generates measurement inaccuracies if there are alternating portions with lower and higher densities of joints [11]. The variance in jointing along the core is equalized by the measurement of joint density per meter, as Table 3 and Figure 5 demonstrate.  
It is important to note that the three-dimensional spatial fractures of the rock mass are not taken into consideration by the one-dimensional rock quality designation (RQD) derived from the cores.
## 2 Materials and Methods
### Physicochemical Evaluation of Spiramycin  
#### 2.5.1 Fourier Transform Infrared Spectroscopy  
The unprocessed and processed spiramycin were mixed with KBr powder at a ratio of 1%, and the mixture was then pressed into self-supporting disks. The FTIR spectrum was detected by a MAGNA-IR560 E.S.P. (Nicolet, Madison, WI, USA) in the wavenumber range of 4000-500 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\).  
\begin{table}
\begin{tabular}{r r r r r r r r r r r r} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**No**}} & \multicolumn{4}{c}{**BBD Experiments**} & \multicolumn{4}{c}{**ANOVA**} \\ \cline{2-13}  & \(X_{1}\)* & \(X_{2}\) & \(X_{3}\) & \(Y_{1}\) & \(Y_{2}\) & **Source** & **Sum of Squares** & **Degree of Freedom** & **Mean Square** & **F** & \(P\) \\ \hline
1 & 10 (–1) & 3 (–1) & 50 (0) & 294 & 297 & Model & 36,570 & 9 & 36,570 & 113 & \(<\)0.0001 \({}^{\rm c}\) \\
2 & 30 (–1) & 3 (–1) & 50 (0) & 349 & 346 & X\({}_{1}\) & 903 & 1 & 903 & 25 & 0.0016 \({}^{\rm c}\) \\
3 & 10 (–1) & 7 (+1) & 50 (0) & 290 & 283 & X\({}_{2}\) & 3445 & 1 & 3445 & 96 & \(<\)0.0001 \({}^{\rm c}\) \\
4 & 30 (–1) & 7 (+1) & 50 (0) & 280 & 277 & X\({}_{2}\) & 13,695 & 1 & 13,695 & 380 & -0.0001 \({}^{\rm c}\) \\
5 & 10 (–1) & 5 (0) & 40 (–1) & 279 & 274 & X\({}_{1}\) & 756 & 1 & 756 & 21 & 0.0025 \({}^{\rm c}\) \\
6 & 30 (+1) & 5 (0) & 40 (–1) & 306 & 306 & X\({}_{1}\) & 121 & 1 & 121 & 3 & 0.1097 \({}^{\rm c}\) \\
7 & 10 (–1) & 5 (0) & 60 (+1) & 368 & 367 & X\({}_{2}\) & 930 & 1 & 930 & 26 & 0.0014 \({}^{\rm c}\) \\
8 & 30 (+1) & 5 (0) & 60 (+1) & 372 & 378 & X\({}_{2}^{\rm c}\) & 9584 & 1 & 5984 & 166 & -0.0001 \({}^{\rm c}\) \\
9 & 20 (0) & 3 (–1) & 40 (–1) & 300 & 302 & X\({}_{2}^{\rm c}\) & 879 & 1 & 879 & 24 & 0.0017 \({}^{\rm c}\) \\
10 & 20 (0) & 7 (+1) & 40 (–1) & 228 & 230 & X\({}_{2}^{\rm c}\) & 8413 & 1 & 8413 & 233 & -0.0001 \({}^{\rm c}\) \\
11 & 20 (0) & 3 (–1) & 60 (+1) & 377 & 355 & Residual & 252 & 7 & 36 & \\
12 & 20 (0) & 7 (+1) & 60 (+1) & 346 & 344 & Lack of fit & 123 & 3 & 41 & 1 & 0.3970 \\
13 & 20 (0) & 5 (0) & 50 (0) & 240 & 249 & Pure error & 129 & 4 & 32 & \\
14 & 20 (0) & 5 (0) & 50 (0) & 240 & 249 & Corrected total & 36,822 & 16 & \\
15 & 20 (0) & 5 (0) & 50 (0) & 255 & 249 & & & & & \\
16 & 20 (0) & 5 (0) & 50 (0) & 249 & 249 & Standard deviation & Mean & \(CV\) (\%) & \(R^{2}\) & Adjust \(R^{2}\) & Predicted \(R^{2}\) & Adequacy precision \\
17 & 20 (0) & 5 (0) & 50 (0) & 252 & 249 & 6.01 & 294.18 & 2.04 & 0.9931 & 0.9643 & 0.9410 & 31.94 \\ \hline \end{tabular}  
* The results were obtained with Design Expert 8.0 software; \({}^{\rm b}\) X\({}_{1}\) is the spiramycin concentration (mg/mL), \(X_{2}\) is the volume ratio of antisolvent to solvent (mL/mL), \(X_{3}\) is the dripping speed of the solvent adding into the antisolvent (mL/h), \(Y_{1}\) is the actual MPs of the spiramycin microparticles (nm), \(Y_{2}\) is the predicted MPS of the spiramycin microparticles (nm), \(Y_{3}\)’s significant at \(p<0.05\).  
\end{table}
Table 1: Experimental design matrix to screen for variables that determine the MPS of spiramycin microparticles and ANOVA results \({}^{\rm a}\).
## 3 Proposed Methodology
### Adaptive Power Control with Adaptive Clustering Routing Model
Some clustering protocols, on the other hand, have only been developed and studied in two-dimensional (2D) deployments, while UWSN network scenarios necessitate three-dimensional deployments (3D).
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 4 Experiments  
### Simulation Environment Configuration  
The simulation of quantum networks is implemented by integrating QKDsim into Mininet. Using a scripting language based on Python Version 2.7, the operating system is Ubuntu 18.04, Open vSwitch Version 2.5.7 is used as the virtual switch, and OpenFlow Version 1.3 is used as the southbound interface protocol, where the simulation simulates for a fixed time of 300 s. The number of services arriving during this period ranges from 50 to 200. In the experiments, assume that the bandwidth is set to 50 Mbps, the initial size of the key pool is 100 KB, the initial amount of keys contained in the key pool is 50 key volumes, and the number of successfully negotiated link keys is random, ranging from a few tens to a few hundred, depending on the given service. The experimental simulation topology is shown in Figure 6.
# Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey
## 3 Current Uses of Microalgae and Cyanobacteria in Industry
### Microalgae and Cyanobacteria as Food, Dietary Supplements, and Feed
Nannostar, based on the microalgae _Microchloropsis gaditana_ (Eustigmatophyceae), is produced by AlgaSpring (Almere, The Netherlands) for aquaculture purposes. It is rich in EPA (20:5 omega-3) and other fatty acids such as C14:0, C16:0, C16:1, C18:1, and C18:2. It contains \(\beta\)-carotene, violaxanthin, tocopherols, various sterols, and valuable minerals. Some industries use a combination of microalgae, as in the case of Easybooster (Fitoplancton marino, Cadiz, Spain), commercialized as a liquid phytoplankton extract combining 33% of _Isochrysis_ (T-ISO), 31% of _Nannochloropsis_, 18% of _Tetraselmis_, and 18% _Phaodactylum_ (Table 6). Microalgae are an important food for some marine bivalve mollusks, the larval stages of abalone and conch gastropods, the larvae of several marine fish species, penaeid shrimp, and zooplankton [102]. For adult fish, the use of microalgae is limited as fishmeal is still cheaper [101]. [PERSON] estimated the cost of live microalgae production for feed in aquaculture hatcheries as 300-600 EUR-kg\({}^{-1}\) biomass. This cost could be reduced using larger-scale production [103]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Industry** & **Product Name** & **Microalgae Strain** & **Description** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **Food** \\ **
## 6 Experimental Results and Discussion
### Influence of Fusion of Different Measuring Points
#### 6.5.1 Influence of the Number of Different Measuring Points  
The influence of the number of different test points was analyzed by comparing the accuracy of the model when the number of measuring points was 4, 5, 6, 7, and 8 respectively, corresponding to the above five kinds of training data and the deleted channels, which are shown in Table 6.  
As the number of channels decreases, the parameters that need to be trained in the model also decrease. Thus, the amount of calculation for each layer also decreases and the length of training time and testing time decreases. The number of training parameters required for the five models and the average duration required for each training set are shown in Table 6. It can be seen from Table 6 that the reduction of the number of measuring points will significantly reduce the model training time. Among them, when the number of measurement points is 4, training time is only 139.8 s, less than half of the training time when the number of measurement points is 8. Therefore, selecting the appropriate measuring points as the input of the model can speed up the model training.  
The test results of the five models on the test set are shown in Figure 16. When the number of measuring points is 7, the model has the highest diagnostic accuracy and is superior to the 1-D CNN single-measuring point fault diagnosis model in the previous section. This shows that the key signal of the channel 8 is not suitable for adding the fusion signal as the input of the model. However, as the number of measurement points continues to decrease, the diagnostic accuracy of the model also decreases. Hence, this phenomenon indicates that the data of other channels contains useful feature information. Thus, if these measurement points are deleted, it will reduce the accuracy of fault diagnosis.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 4 Results
### Compliance with Contingency Procedures  
The collected data during the simulations demonstrate that, most of the time, the RPAS could adequately execute the contingency procedures as initially agreed with the ATC. Q3 indicates that, in most cases, the contingency procedures were carried out as initially planned; they were close to 4 points in all the scenarios. Strangely, the lost-link contingency scenarios incorporating flight intent show more variability than when no intent was available. This could be attributed to the complexity of the scenario (RPAS crossing multiple altitude layers) and the unsatisfied expectation by ATCO (flight intents not providing intermediate altitude references). No such unsatisfied expectancy occurred in the scenarios in which no flight intent was available.  
Q13 offers a clear view of the capacity of the RPAS to stick to the predefined contingency trajectories, both in case of a loss of engine or a lost link, with high-rank values close to 5. On the contrary, both Q15 and Q16 indicate that the flight intent was a key factor in managing the contingency for the MQ-9 scenario but not for the RQ-4A scenario. As previously discussed, the RQ-4A scenario highlighted that better altitude prediction information should be attached to the intent information, as this is critical for efficiently managing altitude conflicts between aircrafts.  
Q17 provides an alternative vision of the problem, as it can be observed that the absence of flight intent produced a significant perceived impact on the way ATCs managed the traffic flow. In particular, for the MQ-9 mission, the traffic efficiency was impacted when no radio communication nor intent was available. The MQ-9 traversed two highly occupied arrival and departure routes in that particular scenario. Such interference required the ATC to fully reroute all the traffic for safety reasons, as the controllers did not have access to any relevant information about the immediate RPAS intentions. Either voice communication by the PiC or the availability of the flight intent would alleviate that situation, allowing ATCs to perform a more efficient rerouting of the commercial traffic around the RPAS.
## 4 Discussion
The study was conducted on Swiss albino mice infected with _Mycobacterium tuberculosis_ (Mtb), with unknown vitamin D3 status; moreover, none of the investigated parameters were common to our examination. Nevertheless, apart from the concept of the therapeutic use of vitamin D3 inhalation, our attention was attracted by the investigation of the chronic (once daily, 5 days a week for 28 days) administration of calcitriol at the concentration of 5 pg/g (potentially therapeutic dose in the light of our studies). Although Mtb-mice chronic exposure to calcitriol did not result in the expected effect, in particular, reduction in bacterial burden and increase in the production of cathelcidin (an immune peptide with bactericidal properties), the performed studies revealed significant improvement in histological parameters, such as inflammation and signs of necrosis and fibrosis, which, according to the authors, suggested a pro-healing impact of the tested compound on lung tissue damaged by infection [20]. Consequently, the presented study also supports the use of vitamin D3 metabolites administered by inhalation in the treatment of lung diseases, as proposed by our team. Finally, we would also like to mention the studies by [PERSON] et al., who investigated the impact of the single, direct administration into the respiratory tract of calcitriol in the dose of 200 pg/g on LPS-induced acute lung inflammation in mice on standard as well as a vitamin D deficient diets [21]. Studies have been conducted on 8- to 9-week-old male murine strain C57 BL/6 JolaH. Unfortunately, there is a lack of information about the amount of vitamin D in the applied diets; at the same time, the description of vitamin D status in the research groups was limited to very confusing information about the calcidiol serum level, which in vitamin-D-deficient mice was lower than 2 ng/mL, while in mice on a standard diet was seven- to nine-fold higher. It should also be emphasized that in contrast to previously described studies, including our research, calcitriol was not administered via typical nebulization but by using an aerosolizer syringe, which may explain the placing of mice under mild anesthesia with isoflurane. Unfortunately, apart from the use of the same strain of mice in the research and the concept of administering calcitriol directly to the respiratory tract of mice on diets with different vitamin D3 content, we were not able to find common points with this study, which are necessary to discuss and compare our observations. Nevertheless, it is worth mentioning that [PERSON]'s team revealed that murine nebulization with calcitriol reduced acute inflammation caused by LPS, independent of vitamin D3 status and, moreover, in the case of vitamin deficiencies prevented epithelial barrier leakage and lung damage [21]. Thus, the cited study is another argument in favor of the use of vitamin D3 metabolite inhalation in respiratory disorders.
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
## 3 Analysis
### Model Validation  
Per the ASHRAE Guideline 14-2014, a normalized mean bias error (NMBE) of 5% or better and a coefficient of variation of the root mean square error (CVRMSE) of 15% or better are the minimum requirements for an energy model to be considered as a calibrated model. The NMBE and CVRMSE are defined as follows:  
\[NMBE=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})}{(n-1)\times\overline{y}}\times 100 \tag{5}\]  
\[CVRMSE=\frac{\sqrt{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})/(n-1)}}{\overline{y}} \times 100 \tag{6}\]  
Figure 8 shows the measured vs. predicted monthly energy consumption for the case building. The simulated energy consumption agreed with the measured energy consumption well and both the NMBE and the CVRMSE met the ASHRAE guideline requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inputs** & **Actual** & **Design** \\ \hline
**Indoor temperature (\({}^{\circ}\)C)** & 29.0 & 25.0 \\
**Occupant density (m\({}^{2}\)/person)** & 10.0 & 10.0 \\
**Lighting power density (W/m\({}^{2}\))** & 1.1 & 9.0 \\
**Plug load power density (W/m\({}^{2}\))** & 3.2 & 15.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Inputs for internal loads used in the reconciled models.  
Figure 7: Schedules of internal load and indoor temperature. (**a**) occupant density, (**b**) lighting power density, (**c**) plug load density, (**d**) indoor temperature.
## 5 Mechanical Properties of the Natural Fibers Reinforced Epoxy Composites
### Tensile Behaviour
Plasma treatment has improved interfacial adhesion by enhancing hydrophobicity at fiber surfaces and increasing fiber surface roughness [106]. The hardness of untreated bagasse composites and treated fibers with 10% NaOH indicated that the tensile strength of the treated composites increased, but their hardness dropped [107]. Alkali treatment, silane treatment, and isocyanate treatment have been shown to increase the tensile strength of flax/epoxy composites [108]. The 5% NaOH-treated fibers showed a higher tensile strength, attributed to a better surface smoothness and chemical interaction between matrix and fiber. However, further NaOH concentration increases decreased tensile strength as the fibers start to degrade [71]. [PERSON] and [PERSON] [69] investigated the use of epoxy-based NRFCs (coir, banana, and sisal) in vehicle seat shell manufacturing applications. [PERSON] et al. [109] investigated the effect of hybridization of the jute and banana fibers (100/0, 75/25, 50/50, 25/75, and 0/100 wt.%). The hybrid composite with 50/50 wt.% jute and banana fibers showed the best tensile strength due to better fibers and epoxy adhesion.
## 3 Vision, Key Features and Challenges of 6G
### Vision and Key Features of 6G
#### 3.1.4 Superfast Network  
With reference to the data analysis shown in Figure 1, the ever-increasing demand for high data rate and seamless connectivity to such ultra-dense networks can be provided by integrating terahertz (THz) (ranges from 0.1-10 THz) communication in 6G networks [168; 177; 178]. A vast amount of unused radio spectra which can be efficiently used to increase network capacity is available in the THz band. THz is additionally reasonable for high data rate transmission and short-range communication by empowering the ultra-high bandwidth and uLLC paradigms. An extensive review of THz communication with its future scope and challenges is presented in [194].
## 1 Introduction
The Copernicus Sentinel-5P mission launched in October 2017 with the Tropospheric Monitoring Instrument (TROPOMI). Possessing higher spatiotemporal resolution, it provides daily global continuous spatial sampling with a minimum pixel size of 3.5 \(\times\) 7.5 km\({}^{2}\) (reduced to 3.5 \(\times\) 5.5 km\({}^{2}\) since August 2019), nearly covering the entire globe on a daily basis, and surpassing previous satellite-based SIF products [28; 29]. However, the SIF products derived from these data amplify instantaneous SIF data under clear-sky conditions to daily averages by considering variations in the solar zenith angle, thereby neglecting the influence of weather variations on SIF. The recently introduced eSIF product addresses this limitation by accounting for all weather variations, improving upon the TROPOMI SIF product [27]. Nevertheless, these data do not represent raw SIF data, and the data reconstruction process may lead to the loss of true values. Therefore, in this study, to obtain more accurate, high-resolution SIF data, both the original TROPOMI SIF product and the eSIF product were simultaneously considered. Machine learning algorithms were used to downscale and generate high-resolution SIF products (0.0005\({}^{\circ}\)) and investigate the relationship between SIF products of different resolutions and GPP data derived from EC flux towers. The objectives of this study were (1) to produce high-resolution SIF prod ucts suitable for tracking seasonal changes in GPP; (2) to compare whether matching SIF with flux footprints enhances the correlation between SIF and GPP; and (3) to assess the relationship between SIF and GPP at different observational scales.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 2 Acoustic Imaging Concepts  
Acoustic imaging seeks to form a spatial map of sound sources in a scene analogous to standard cameras that produce visual images using projected light patterns. Conventional optics passively focus rays along physical lines of sight to reconstruct perspectives. In contrast, acoustic imaging relies on digital sampling, processing, and interpreting acoustic fields using microphone array receivers and beamforming algorithms [15; 16]. We provide an overview of fundamental principles including angular resolution, aliasing, array geometry considerations, and beamforming basics.
## 2 Original SIFT Algorithm
### Scale Space Extreme Value Detection  
Potentially sensitive points that are invariant to scale and rotation are identified using differential Gaussian functions. The scale space of the image is mainly obtained by convolving the Gaussian differential function and the original image, as shown in Equations (1) and (2) [12]:\[G(x,y,z)=\frac{1}{2\pi\delta^{2}}e^{\frac{(x-\frac{\alpha}{\beta})^{2}+(y-\frac{ \alpha}{\beta})^{2}}{2\delta^{2}}} \tag{1}\]  
\[L(x,y,\delta)=G(x,y,z)\times I(x,y) \tag{2}\]  
where is \(L(x,y,\delta)\) is the scale space function, and \(G(x,y,z)\) is the Gaussian blur function. \(I(x,y)\) is the original image function, where \((x,y)\) are the image pixel coordinates; \(\delta\) is the spatial scale; and \((m,n)\) is the image dimension.  
The difference of Gaussian (DoG) detection was used to find the space extremum detection, as shown in Equation (3):  
\[D(x,y,\delta)=(G(x,y,k\delta)-G(x,y,\delta))\times I(x,y)=L(x,y,k\delta)-L(x,y,\delta) \tag{3}\]  
In the equation, \(k\) is the multiplication factor, and \(S\) is the integer number [36]. The value of \(k\) is shown in Formula (4):  
\[k=2^{\frac{1}{\delta}} \tag{4}\]  
The key points composed of local extreme points in the DoG space are initially detected by comparing the images of two adjacent layers of each DoG space in the same group. In order to find the extreme point in DoG space, each pixel is compared with all its neighbors to see if it is larger or smaller than its neighbors in the image domain and scale domain. As shown in Figure 1, the detection point in the middle (the \"X\" in the picture) is compared with its 8 adjacent points of the same scale and the 9 \(\times\) 2 points corresponding to the upper and lower adjacent scales, a total of 26 points, to ensure that extreme points are detected in both scale space and 2D image space.
## 3 Results and Discussions
### Vickers Micro-Hardness  
The mean values of Vickers micro-hardness were evaluated as a result of 6 measurements in 6 different areas, with a 300 g applied force and 10 s dwelling time. The 300 g applied force was chosen so that the penetration depth (h) satisfied the relationship h \(\leq\) d/7 (where d is the mean size of the indentation diagonal had a maximal value of 59 \(\upmu\)m).  
Mean values of the Vickers micro-hardness are presented in Table 7 and show higher micro-hardness values proportional with the plasma electrolytic oxidation process time.  
It is believed that this improved hardness is a result of time-related reduced porosity and an increased ratio of crystalline material in the coating, in our case MgAl\({}_{2}\)O\({}_{4}\) spinel [39].  
The AZ63_20m coating reached a Vickers micro-hardness of 4.4 GPa, which means that the coating processed for 20 min was five times harder than the AZ63 magnesium alloy substrate, with similar values reported in [40].