esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality
### Instrument Configuration and Signal Detection
#### 2.1.2 Receiver Subsystem
In contrast, analog mode silicon APDs have been used for the 1064 nm channels where high sensitivity in the near-IR is needed. The GLAS instrument in ICESat (2003) and CALIPSO's CALIOP (2006) employed silicon APDs for their 1064 nm return [51; 60]. Regarding PMTs, the Atlas instrument from the ICESat-2 mission utilized an array of PMTs with 16 individual photon multiplier channels for a single-photon counting detection [61]. The key high-level parameters to describe the receiver subsystem can include [53; 62; 63; 64]:  
1. Field of view (FOV) [\(\mu\)rad]: The FOV is the angular range over which the LiDAR system can detect backscattered light and is typically in the range of (100-1000) \(\mu\)rad. A wider FOV can capture more scattered light but may also increase background noise, affecting the signal-to-noise ratio (SNR). 2. Quantum efficiency (QE) [%]: Probability that an incident photon generates a photoelectron. 3. Photon detection efficiency (PDE) [%] is a variable that describes the probability that a photon will be detected and is mainly dependent on the quantum efficiency of the detector semiconductor material and the arrangement of the sensors. 4. Dead time [ns] is the period immediately after detecting a photon during which the detector cannot register another photon. A short dead time allows the detector to be ready to detect another photon more quickly, enhancing the counting rate and efficiency. 5. Timing jitter [ps]: A low jitter is essential for applications requiring precise timing measurements, such as time-correlated single photon counting (TCSPC). Jitter refers to the variability in timing accuracy when detecting photons. Reducing jitter improves the temporal resolution of measurements, which is crucial for accurately determining the time of arrival of photons. 6. Dark count rate [counts per second]: The dark count rate measures the number of false counts detected by the sensor, essentially background noise. Minimizing this rate leads to achieving high signal-to-noise ratios in sensitive applications, allowing for detecting very low levels of light without significant interference from the detector itself.
## 4 Results
### RT Models as a Precursor for Response Surface Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Ultimate Static Load (kg) Quantiles**} & \multicolumn{2}{c}{**Dryer Inlet Temperature (\({}^{\circ}\)C) Quantiles**} \\
100.0\% & maximum & 283.04 & 100.0\% & maximum & 145.00 \\
99.5\% & & 283.04 & 99.5\% & & 145.00 \\
97.5\% & & 269.20 & 97.5\% & & 133.33 \\
90.0\% & & 250.84 & 90.0\% & & 127.22 \\
75.0\% & quartile & 232.35 & 75.0\% & quartile & 125.00 \\
50.0\% & median & 209.11 & 50.0\% & median & 120.28 \\
25.0\% & quartile & 189.71 & 25.0\% & quartile & 112.22 \\
10.0\% & & 174.22 & 10.0\% & & 103.33 \\
2.5\% & & 168.53 & 2.5\% & & 96.67 \\
0.5\% & & 147.87 & 0.5\% & & 93.33 \\
0.0\% & minimum & 147.87 & 0.0\% & minimum & 93.33 \\ \multicolumn{2}{c}{**Summary Statistics**} \\ Mean & 211.96 & Mean & 117.92 \\ Std Dev & 28.23 & Std Dev & 8.36 \\ Std Err Mean & 2.31 & Std Err Mean & 1.38 \\ Upper 95\% Mean & 216.52 & Upper 95\% Mean & 119.44 \\ Lower 95\% Mean & 207.41 & Lower 95\% Mean & 116.40 \\ N & 150 & N & 150 \\ \multicolumn{2}{c}{**Wood Weight (kg) Quantiles**} & \multicolumn{2}{c}{**Wet Bin Speed (m/min) Quantiles**} \\
100.0\% & maximum & 19.66 & 100.0\% & maximum & 9.75 \\
99.5\% & & 19.66 & 99.5\% & & 9.75 \\
97.5\% & & 19.66 & 97.5\% & & 9.45 \\
90.0\% & & 5.68 & 90.0\% & & 9.14 \\
75.0\% & quartile & 4.38 & 75.0\% & quartile & 9.14 \\
50.0\% & median & 3.20 & 50.0\% & median & 8.53 \\
25.0\% & quartile & 2.19 & 25.0\% & quartile & 7.92 \\
10.0\% & & 0.10 & 10.0\% & & 7.32 \\
2.5\% & & 0.10 & 2.5\% & & 6.40 \\
0.5\% & & 0.10 & 0.5\% & & 6.10 \\
0.0\% & minimum & 0.10 & 0.0\% & minimum & 6.10 \\ \multicolumn{2}{c}{**Summary Statistics**} \\ Mean & 3.54 & Mean & 8.45 \\ Std Dev & 4.19 & Std Dev & 0.84 \\ Std Err Mean & 0.34 & Std Err Mean & 0.07 \\ Upper 95\% Mean & 4.21 & Upper 95\% Mean & 8.59 \\ Lower 95\% Mean & 2.86 & Lower 95\% Mean & 8.32 \\ N & 150 & N & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics for the dependent and independent variables in the RT model for OSB. Figure 3: _Cont_. Figure 2: Regression tree for OSB ultimate static load derived from boosted tree and random forest models.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 5 Discussion
### Comparison to ERA5-Land Soil and Air Temperatures  
The qualitative analysis between the detection results using our algorithm and ERA5-Land's air and soil temperatures supported our previous findings. In effect, our algorithm is capable of detecting the same freezing episodes detected using ERA5-Land. However, while both product performances (our algorithm and ERA5-Land) were similar, the qualitative precision of our frozen maps appears to be higher. Using our algorithm with the high spatial resolution S1 images allowed us to better differentiate the local variations in freezing episodes at the plot scale in comparison to ERA5-Land, which is unable to detect them due to its low spatial resolution (9 km). For example, on 27 December 2018 and 25 February 2019, in site A, the reported soil temperature by ERA5-Land was, respectively, \(-2.7\) and \(2.6\ ^{\circ}\)C, which suggests that two freezing events with different intensities occurred. However, Figure 15a,b, show that our product is more capable of describing the intensities of these freezing episodes and discerning which plots were affected. Similarly, over site B, on 22 January 2018 (t = \(-5.5\ ^{\circ}\)C) and 25 January 2019 (t = \(0.9\ ^{\circ}\)C), we observe that for the lowest temperatures, our product detected most of the plots as frozen, with almost half detected as being severely frozen (Figure 15c). Alternatively, on 22 January 2019, with a reported temperature of \(0.9\ ^{\circ}\)C, our algorithm did not detect any severely frozen plots, and only slightly more than half of the plots were detected as being mild-to-moderately frozen (Figure 15d).
## 3 The Principle of Arbitrary Baseline Algorithm  
### The Model of the Incident Signal  
The direction finding of spatial Cartesian coordinate system is shown in Figure 3. \(\overrightarrow{OS}\) is the incident signal of far-field. And \(\overrightarrow{S^{\prime}O}\) is its projection in XOY plane. \(\alpha\) and \(\beta\) stand for azimuth angle and elevation angle, respectively. Likewise, \(\theta\) and \(\varphi\) represent course angle and pitching angle, respectively. According to the geometric, the relationship between \(\alpha\), \(\beta\) and \(\theta\), \(\varphi\) are expressed as:  
\[\tan\theta=\cot\beta\sin\alpha \tag{2}\]  
\[\tan\varphi=\cot\beta\cos\alpha \tag{3}\]  
As shown in Figure 3, \(\theta\) and \(\varphi\) are generally used to control direction finding system in practice. According to Equations (4) and (5), if \(\alpha\) and \(\beta\) are obtained by any algorithm, then \(\theta\) and \(\varphi\) could be obtained easily.  
The arbitrary baseline model is shown in Figure 4. A, B and C are three antenna elements placed in the space.  
Assuming that the phase difference ambiguity does not exist and using simple geometric mathematic, the phase differences of the incident signal impinging on the antennas A, B, C can be represented as:  
\[\phi_{\rm AB}=\frac{2\pi}{\lambda}[(x_{\rm B}-x_{\rm A})\cdot\cos\beta\cdot \cos\alpha+(y_{\rm B}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha+(z_{\rm B}-z_{ \rm A})\cdot\sin\beta] \tag{4}\]  
\[\phi_{\rm AC}=\frac{2\pi}{\lambda}[(x_{\rm C}-x_{\rm A})\cdot\cos\beta\cdot \cos\alpha+(y_{\rm C}-y_{\rm A})\cdot\cos\beta\cdot\sin\alpha+(z_{\rm C}-z_{ \rm A})\cdot\sin\beta] \tag{5}\]  
\[\phi_{\rm BC}=\frac{2\pi}{\lambda}[(x_{\rm C}-x_{\rm B})\cdot\cos\beta\cdot \cos\alpha+(y_{\rm C}-y_{\rm B})\cdot\cos\beta\cdot\sin\alpha+(z_{\rm C}-z_{ \rm B})\cdot\sin\beta] \tag{6}\]  
Figure 3: Model of incident signal.  
where \(\phi_{\rm AB},\,\phi_{\rm AC},\phi_{\rm BC}\) stand for the phase differences respectively. In theory, the 2D-DOA could be acquired by solving any two of Equations (4-6).
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.5 YOLO (You Only Look Once)
The model integrates a novel attention mechanism through Selective Kernel (SK) modules, enhances feature representation using Spatial Pyramid Pooling (SPP), and promotes effective feature fusion across multiple scales with a Feature Fusion (FF) module. Additionally, it employs the Complete Intersection over Union (CIoU) loss function for improved localization accuracy. Also, [PERSON] et al. [77] evaluate the YOLOv8 deep learning model for detecting and classifying face mask-wearing conditions using the Face Mask Detector dataset. By employing transfer learning techniques, YOLOv8 demonstrates high accuracy in distinguishing between correctly worn mask, incorrectly worn mask, and no mask scenarios, outperforming the previous model, YOLOv5. The research highlights YOLOv8's enhancements in real-time object detection, making it suitable for applications requiring quick and reliable mask detection.
## 5 Results and Discussion
### Critical Discussion
#### 5.8.1 Relationships between the Proposed Framework and Security Methods  
The proposed framework holistically integrates the security methods for network-based manufacturing systems. To clarify the relationships:  
* Physical security: Pertains directly to the CNC machines and mobile industrial robots, ensuring that they are free from physical tampering or unauthorized access.
* Application security: It focuses on the software running on all the devices of NBMS to ensure that the applications governing their operations are free from threats.
* Network security: It focuses on secure data transmission within the NBMS while communicating remotely. Device security: It ensures that the communications of all devices of NBMS are authenticated and that data integrity is maintained.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Proposed UAS Data Ferrying Designs
#### 2.1.4 Decision Matrix
The LoRa and ZigBee wireless protocols (allocated 2 points) were low-cost setup systems in comparison to a GPRS wireless protocol (1 point). The fixed-wing UAS is an expensive UAS (allocated 1 point) in comparison to the multi-rotor UAS (2 points), in general. Tethering power from the UAS would be economical (allocated 1 point) in comparison to mounting an external battery source (0 points) on the UAS. Constraint 2 (adoptability): Adoptability of the design system was determined based on the application of the system by a producer/crop consultant for agronomic decision-making. Points ranging from 1-5 were given to the system based on the adoptability of the system with low points allocated to the design with low adoptability. 'Design B' (LoRa wireless protocol, multi-rotor UAS, external power source, memory storage over the UAS) (Table 1) was selected based on the matrix total points and was subsequently investigated as a suitable UAS data ferrying design for operations with a WSN installed in a typical US mid-west maize field.
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 4 Results
### Performance Analysis  
The proposed algorithm's effectiveness is assessed using two performance measures. The initial metric gauges the distortion caused by the original image due to incorporating the spatial domain watermark quantified through the Peak Signal-to-Noise Ratio. The second metric assesses the maximum potential capacity for embedding within the domain, measured in bits per second (pps).
## 3 Results and Discussion
### Effect of Ferrofluid Volume Fraction  
The effect of ferrofluid volume fraction on the Nusselt number is depicted in Figure 13. The cooling performance of ferrofluid can be expressed as its ability to absorb the heat and transfer it away from traction inverter. The Nusselt number indicates the heat removal rate from traction inverter to ferrofluid using an MHD pump-based cooling system. The nanoparticles are able to absorb more heat and transfer it away from the source more efficiently as their concentrations increase, therefore, the higher volume fraction of nanoparticles in the ferrofluid shows the enhanced cooling performance. Furthermore, the heat dissipation performance of ferrofluid improves with the rise in volume fraction because the thermophysical properties of ferrofluid improve with an increase in the volume fraction of nanoparticles. Therefore, it has been observed that ferrofluid with a volume fraction of 10% shows a superior cooling performance for traction inverters. The Nusselt numbers of 2.76, 5.52, 7.45, 8.76, and 9.92 are evaluated for ferrofluid volume fractions of 2%, 4%, 6%, 8%, and 10%, respectively.  
The present work demonstrates the feasibility of MHD pump-based cooling systems to maintain the desired cooling performance of traction inverters by varying key influencing parameters for small capacity E-mobility applications (Small-capacity EVs, electric tricycle or motorcycle, etc.). It is important to note that the cooling performance of an MHD pump-based cooling system for a traction inverter can also be influenced by other factors, such as the size and shape of nanoparticles, type of nanoparticles, magnetic field patterns, temperature, and flow rate of the coolant. These factors can affect the ability  
Figure 12: Nusselt number for ferrofluid cooling with change in magnetic field intensity and voltage.  
of coolant to absorb and transfer heat and should be considered when designing and optimizing a cooling system for traction inverters in EVs. The future work will be focused on evaluating the cooling performance of MHD pump-based cooling systems, considering aforementioned parameters for the thermal management of traction inverters under actual operating conditions of EVs with higher power capacity.
# Dry Sintered Metal Coating of Halloysite Nanotubes
## 1 Introduction  
Halloysite nanotubes (HNTs) are naturally-occurring aluminosilicates that measure microns in length and tens of nanometers in diameter [1; 2; 3; 4]. Bonding defects between the alumina and silica lead to net negative and positive charges on the exterior and interior lumen, respectively [5; 6]. HNTs are capable of enhancing the material properties of many polymer matrices [7; 8; 9; 10]. HNTs have been widely used as a nanostructured reinforcement material for polymers that exhibit low mechanical strengths [7; 8]. Modification of these nanotubes through surface coatings and other preparations can further enhance the strength and dispersion of the HNTs within the polymer [9; 10]. HNTs also enable the sustained release of loaded chemicals, drugs, and growth factors [11; 12; 13; 14]. Due to the net charges, HNTs can also be readily coated with polyelectrolytes, polymers, etc. using the net negative chargeof the exterior surface as the basis for assembly [15; 16]. Through added layers deposited onto the HNTs, the pattern of released substances can be further controlled, enabling delayed or inhibited release [13; 14; 15; 16].  
For coating of the HNT surface, a layering method is commonly used to achieve the desired coatings onto the exterior surface, using the inherent charge differential between the layers of the nanotubes as the basis for these coatings [16; 17]. Due to the aforementioned polarity of the nanotubes, solvents used are limited, and the nature of the method requires a much longer and more drawn out process that demands full drying of the prepared HNTs between successively added layers. In particular, the demands of preparing samples with cobalt to achieve a catalytic sheath require manufacture in a vacuum after sample preparation via double solvent impregnation of the cobalt salt [18]. Similarly, the HNT coating in polyamide requires the use of fully-hydrolyzed silane to enable full coating of the polyamide onto the nanotube surface [19; 20].  
In this study, we show a method of coating these nanotubes with metals through dry sintering of the HNTs with metal acetylacetonates--compounds primarily used in the synthesis of nanoparticles, metal catalysts, and as NMR shift reagents. We found that this method was capable of thermally decaying the metal acetylacetonate molecules, resulting in a free positively-charged metal ion that readily bonds to the negatively-charged exterior of the halloysite. Our results show that through sintering, metallic coatings can be achieved on these nanotubes of the desired metal using the associated metal acetylacetonate compound. We anticipate that our method of coating these tubes through a dry sintering process will enable compatibility with a wider range of materials to be coated onto these nanotubes as necessitated by the application without requiring the use of exotic chemical processes.
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 3 Mapping Items Flow Mechanism
### Multi-Homing Supporting with Multi-Mapping Item Mechanism  
When an endpoint gets services by accessing two or more access routers at the same time, it creates problems for mapping item management where the different AMSs assigns different RIDs, and each RID is effective. As we consider the case that AMS supports only services for one access router, there are not two valid RIDs corresponding to an AID in the AMS. Therefore, we only consider the NLMT and IMS. We focus on verifying mapping item validity and adding it to the mapping table.  
Figure 4: Mapping item query.  
Suppose the AMS/NMS storing a routing identifier \(\mathrm{RID_{m}}\) reports a mapping item (AID, \(\mathrm{RID_{0}}\)). After the query in the NLMT/IMT, the corresponding item is (AID, \(\mathrm{RID_{a}}\)/(\(\mathrm{RID_{m}}\))). The multi-mapping item mechanism consists of two steps. In order to describe them clearly, we divided them into two cases:  
_Case 1_ AMS reports to NMS, and the corresponding item queried in NLMT is (AID, \(\mathrm{RID}\), \(\mathrm{RID_{a}}\)).  
_Step 1_: Checks \(\mathrm{RID_{m}}\) (corresponding to an AMS) whether the RID is under its management. If so, it indicates the queried item in NLMT is invalid, replaces the RID with \(\mathrm{RID_{0}}\) in (AID, \(\mathrm{RID}\), \(\mathrm{RID_{a}}\)), sends a mapping update message to IMS, and exits;  
_Step 2_: Sends RID a message and checks if the RID of the corresponding AID in (AID, \(\mathrm{RID}\)) is valid. If valid, showing that it is a multi-homing endpoint, it forms a new item (AID, \(\mathrm{RID_{0}}\).), stores it locally, and reports this item to IMS; if invalid, it deletes the item.  
_Case 2_: NMS reports to IMS, and the corresponding item queried in IMT is (AID, \(\mathrm{RID}\), \(\mathrm{RID_{m}}\)).  
_Step 1_: Checks \(\mathrm{RID_{m}}\) (corresponding to an NMS) and whether its an RID under its management. If so, showing that it is a multi-homing endpoint, forms a new item (AID, \(\mathrm{RID_{0}}\), \([\mathrm{RID_{m}}]\)...), stores it locally, and sends a mapping update message to (\(\mathrm{RID_{m}}\)), and then exits;  
_Step 2_: Sends RID a message, and checks if the RID of the corresponding AID in (AID, \(\mathrm{RID_{m}}\), \(\mathrm{RID_{m}}\)) is valid, If valid, showing that it is a multi-homing endpoint, forms a new item (AID, \(\mathrm{RID_{0}}\), \([\mathrm{RID_{m}}]\)...), stores it locally, and sends a mapping update message to \([\mathrm{RID_{m}}]\); if invalid, indicating the original item has expired, and it is not a multi-homing endpoint, it deletes the item.  
The above mechanism can be used to ensure that there are many mapping items for one endpoint at the same time, instead of the latter item replacing previous items, which supports multi-homing.
## Materials and Methods
### Bioactive Characterisation
#### 2.6.2 Antioxidant Activity  
The free and bound phenolic extracts were used to evaluate the radical scavenging capacity of crude olive pomace, liquid and pulp fraction samples according to 3 methods: DPPH [32], ABTS [33] and Oxygen Radical Absorbance Capacity (ORAC) [29]. In the DPPH method, 1.75 mL of a 60 \(\upmu\)M DPPH methanolic solution was added to 250 \(\upmu\)L of sample and incubated at 30 \({}^{\circ}\)C for 1 h. The results were obtained by absorbance measurement at 515 nm in a spectrophotometer. ABTS was generatedthrough a chemical oxidation reaction with potassium persulfate [33]. The concentration of ABTS radical was adjusted with methanol to an initial absorbance of 0.700 \(\pm\) 0.020 at 734 nm. To 200 \(\mu\)L of this solution of ABTS, 20 \(\mu\)L of the sample, Trolox or solvent were added, using a 96-well plate. The mixture was incubated (30 \({}^{\circ}\)C) for 5 min, and the absorbance at 734 nm was measured with a microplate reader. In the ORAC assay, the reaction was carried out at 40 \({}^{\circ}\)C in 75 mM phosphate buffer (pH 7.4), and the final assay mixture (200 mL) contained fluorescein (70 nM), AAPH (14 mM) and antioxidant (Trolox or sample, at different concentrations). The fluorescence was recorded during 137 min (104 cycles) in a FLUO star OPTIMA plate reader (BMG Labtech, Offenburg, Germany) with 485 nm excitation and 520 nm emission filters. The equipment was controlled by the FLUO star Control software version (1.32 R2) for fluorescence measurement. Black polystyrene 96-well microplates (Nunc, Denmark) were used. AAPH, Trolox and fluorescein solutions were diluted in 75 mM phosphate buffer (pH 7.4). Trolox was used as a standard, and a blank control was performed using a sample solvent for all the methods. All values of antioxidant activity were performed in triplicate and expressed in \(\mu\)M Trolox-equivalents (TE)/g DW.