esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analyses of SPECTRAL indices of Vegetation  
#### 3.4.1 Water-Content Index: The Ratio R975/R900  
High reflectance in water absorption bands is associated with low water content. Therefore, the lower the R975/R900 ratio, the higher the water content of the Gorse companion plant. The two-way ANOVA showed a significant effect of the assemblage and water treatment. An interaction remained between these two factors (F = 5.709; \(p<0.001\)). We thus compared the two water treatments (control and exclusion) for each assemblage. For the three species in the monospecific stand, the decrease in precipitation had no significant effects on the water contents compared to the controls (Figure 7). In the bispecific stands, Up improved the water-conserving abilities of Qc, while the opposite effect was observed for Ca. The Ro water contents were not affected by Up. However, when Qc was in a tri-specific stand with Ro or Ca, Up no longer improved the water content of Qc, nor did it even reduce it (QcRoUp). The water content of Ro in the trispecific stand was improved in the presence of Ca but reduced in the presence of Qc. In the tetraspecific stand, each of the three species had a better water capacity.  
Figure 6: Spectral reflectance of assemblages in control (**c**) and exclusion (**e**). (**a–d**) _Quercus cocifera_ (Qc); (**e–h**), _Cistus albidus_ (Ca); (**i–l**) _Rosmarius officinalis_ (Ro); (**m–p**), _Ulex parriforus_ (Up) in monospecific. (**a,e,i,m**) Bispecific (**b,f,j,n**), trispecific (**c,g,k,o**) and tetraspecific (**d,h,l,p**). The first two letters of each assemblage indicate the measured species. Means of \(n=27\).
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
###### Abstract
Inspired by spatio-temporal sensitive ENF contained in multimedia signals, this paper proposes _EconLedger_, a novel _Proof-of-ENF_ (PoENF) consensus algorithm based lightweight DLT for small scale IoVT networks. Compared to PoW or PoRs, which require high computation or storage resources in mining process, our novel PoENF consensus requires each validator to use extracted ENF variations from simultaneous multimedia recordings as proofs during current consensus round. The validator that presents a valid ENF proof with minimal squared-distance-based score is qualified to generate a new block. Thus, the PoENF consensus mechanism not only achieves efficiency without high demand of mining resource or hardware platform support but it also enhances security by mitigating mining centralization. In contrast to existing solutions that directly collect ENF fluctuations from power grids and stores audio/video recordings in a centralized location-dependent ENF database [10; 11], EconLedger uses _Swarm_[12], which is a decentralized database (DDB) technology, to archive raw ENF-containing multimedia proofs and transactions over IoVT networks. Only hashed references of data are recorded on an immutable and auditable distributed ledger. Thus, it reduces the ever-increasing data storage overhead on the public ledger. The EconLedger ensures correctness, availability, and provenance of data sharing among untrusted devices under a distributed network environment. Moreover, a network with permission ensures that only authorized nodes can access raw data on DDB such that privacy preservation is guaranteed. In summary, this paper makes the following contributions:  
1. A secure-by-design EconLedger architecture is introduced along with detailed explanation of the key components and work flows;
2. A novel PoENF consensus mechanism is proposed, which improves resource efficiency and achieves a higher throughput than PoW-based blockchains;3. A finalized on-chain ledger is coupled with a decentralized off-chain storage to resolve storage burden, and it guarantees security and robustness of data sharing and cooperation in IoVT networks;
4. A proof-of-concept prototype is implemented and tested on a small scale IoVT network, and experimental results verified that the EconLedger is feasible and affordable with respect to the IoVT devices deployed at edge networks. The remainder of this paper is organized as follows: Section 2 briefly discusses background knowledge of ENF, then reviews existing consensus algorithms and state-of-the-art research on IoT Blockchains. Section 3 introduces the rationale and architecture of EconLedger, as well as core features and security guarantees. A novel PoENF consensus mechanism is explained in Section 4. Section 5 presents prototype implementation and numerical results and discusses performance improvements and security insurances. Finally, a summary is presented in Section 6.
## 3 Photonic Technologies
### Lasers  
#### 3.3.1 Working Principles  
The working principles of lasers in biological systems results from the interaction of laser energy with them. When laser energy reaches the tissue surface, it can be reflected, scattered, absorbed or transmitted to the surrounding tissues, absorption being the most important one for the performance of the laser. The level of absorption is related to the depth of penetration and depends on the laser wavelength. According to laser wavelength, they can be classified as: deep, such as neodymium-doped yttrium-aluminum-garnet (Nd:YAG) and diode lasers; superficial, such as carbon dioxide (CO\({}_{2}\)), Er:YAG, and Er,Cr:YSGG lasers. In Figure 4 is displayed a schematic representation of this classification, showing the principal effects of lasers in biological tissues. Lasers can also be grouped in four types: CO\({}_{2}\) laser; Erbium laser; Diode laser; Neodymium:Yttrium-Aluminum-garnet laser. The CO\({}_{2}\) laser wavelength is highly absorbed by water, as are the erbium laser wavelengths. The CO\({}_{2}\) laser wavelength is highly absorbed by water and, therefore, they are highly efficient when they are used on soft tissue. For the Erbium lasers, two wavelengths are available and those wavelengths are highly absorbed by the water molecules in both soft and hard tissues. Thus, Erbium lasers can cut soft tissue, but with much less hemostatic ability than other soft tissue lasers. One disadvantage is that these wavelengths, similar to all lasers, cannot remove gold or metal crowns, vitreous porcelain, or amalgam restorations. Diode lasers are used strictly for soft tissue procedures and penetrate 2 to 3 mm or more into soft tissue, depending on the wavelength and tissue biotype. Diode laser wavelengths are absorbed by pigmented structures, making them ideal for cutting melanotic or highly-vascularized soft tissues and providing hemostasis. The advantages of the Nd:YAG laser include a relatively bloodless surgical field, minimal swelling, reduced surgical time, excellent coagulation, and, in most cases, reduced or no postoperative pain. The main disadvantage of the Nd:YAG laser is the greater depth of penetration into the target tissue [73].  
More recently, lasers were also classified according to their hazardous effects, ranging from class I to IV. All lasers used in oral surgery, or for whitening or cavity preparation are class IV and can provoke damage to the skin and to the eyes [75].
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 6 The Implication of Future Research
### Ethereum + Ganache  
Ethereum's greatest advantage is its widespread use and prebuilt network. Ethereum is heavily used already as a cryptocurrency and Ganache is a simulator that is used to simulate Ethereum networks. Ethereum is proof based and is great for those trying to run simulations that have networks similar to Ethereum and for those who do not want to set up and run self-hosted nodes. Ethereum is very versatile and circumvents the need to set up one's own network as it is all built and in use. People running simulations who want to have networks similar to Ethereum should consider it, as it can save considerable time that is needed to build the blockchain network. In addition, people should use Ganache rather than the Ethereum network directly as it has easier interfaces and is more user-friendly. This simulator should not be used if the simulations are not planned for an Ethereum-based network.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Simulators** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{JABS} & consists of peer-to-peer connection modules to eliminate central sources of failure and bottlenecks and increase the fault tolerance and system scalability in IoT & Still new and in its testing phase \\ \hline \multirow{3}{*}{CBlockSim} & Being written in C++ reduces run time by an order of magnitude and boosts scalability by an order of magnitude when compared to other simulators & Is a proposed simulator. Therefore, it hasn’t been tested in real-world cases \\ \hline \multirow{3}{*}{Cardano} & Dedication to creating a sustainable cryptocurrency environment can help IoT reduce our carbon footprint & Unable to help IoT in interoperability issues because it lacks mechanisms for IoT devices to communicate properly with each other \\ \hline \hline \end{tabular}
\end{table}
Table 7: Advantages and limitations of potential blockchain simulators for IoT environments.
## 4 Implementation and Evaluation
### Functional Evaluation
#### 4.2.2 Reliability and Energy Consumption
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \multirow{2}{*}{**Evaluated Node**} & \multicolumn{2}{c|}{**Evaluation**} & \multirow{2}{*}{**PDR (\%)**} & \multirow{2}{*}{**Throughput (bps)**} \\  & **Time (hh:mm:ss)** & & \\ \hline
0x2100 & \multirow{2}{*}{22:03:02-22:06:18} & 99.2634 & 638.00 \\ \hline
0x2200 & & 99.2634 & 638.00 \\ \hline
0x2100 & \multirow{2}{*}{02:47:56–09:29:12} & 99.4214 & 644.05 \\ \hline
0x2200 & & 99.4630 & 644.32 \\ \hline
0x2100 & \multirow{2}{*}{10:13:00–10:55:39} & 99.1822 & 642.96 \\ \hline
0x2200 & & 99.0541 & 642.16 \\ \hline
0x2100 & \multirow{2}{*}{11:34:42-14:17:49} & 99.9926 & 638.16 \\ \hline
0x2200 & & 99.9926 & 638.16 \\ \hline \end{tabular}
\end{table} 
Table 4: End-to-End packet delivery ratio.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 6 Validation of Environmental Measurements  
The prototype measurements are initially cross-checked with the industrial reference devices to ensure the data quality. The main purpose of this step is to identify the errors linked with the current prototype and to eliminate them in the future. This section compares five modules, as shown in Table 2. The reference devices manufactured by Testo, Li-COR, and Radionova are mentioned as \"reference\" for simplicity in the graphs and explanations.  
The test was carried out on September 20, 2018, in the courtyard of Technical University of Munich. The devices were fixed to take readings at 4 pm. A computer was connected to the prototype to configure the run-time interims before the test. It was also used to monitor the prototype measurements during the validation. The readings were recorded in ground level and later on the rooftop of the campus. The collection consisted of 97 samples, each taken at 10 s interval. The observers remained approximately 5 m away from the site to minimize the human influence on the data. The setting of the test is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Prototype** & **Accuracy** & **Reference Sensors** & **Accuracy** \\ \hline Temperature (T\({}_{\text{g}}\)) & DS18B20 & \(\pm\)0.5 \({}^{\circ}\)C & TESTO 06020743 & \(\pm\)1.0 \({}^{\circ}\)C \\ Temperature (T\({}_{\text{a}}\)) & SHT20 & \(\pm\)0.3 \({}^{\circ}\)C & TESTO Probe 06280143 & \(\pm\)0.5 \({}^{\circ}\)C \\ Humidity (RH) & SHT20 & \(\pm\)3\% & TESTO Humidity Probe 06369743 & \(\pm\)1.0\% \\ Radiation & TCS34725 & \(-\) & LI-COR LI-200R & 0.183 W/m\({}^{2}\) \\ Wind (v) & SEN0170 & \(\pm\)3\% & TESTO Air Flow Probe 06280143 & \(\pm\)0.03 m/s \\ GPS & Adafruit Ultimate & \textgreater{}3 m & RADIONOVA (RF) Antenna & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of components (prototype and reference sensors).  
Figure 10: Validation setup with prototype and the test reference.
## 3 Results
### Performance of Different Network Types  
Obtained results were compared with three types of neural networks: Gated Recurrent Units (GRU), Elman Recurrent Neural Network (RNN), and Feedforward neural network. An example of differences in training of two hidden layers architecture is shown in the first plot in Figure 7. RNN obtained the worst results, with declining accuracy as the network becomes more complex. This is caused by Vanishing Gradient problem: For long time-series data, RNN tends to lower the gradient to the value where parameter updates become insignificant and have no real impact on the learning. The solution is to use LSTM and GRU architectures. LSTM obtained slightly better results than GRU. For GRU, LSTM, and Feedforward networks the accuracy is almost linear with peaks for different range of neurons. The second plot in Figure 7 shows how networks behave for a declining number of gestures. LSTM and GRU exceeds 96% accuracy for 10 gestures. RNN and Feedforward networks reach a satisfying level (above 94%) for six and eight gestures, respectively.  
Figure 6: (**A**) Normalized to percentage confusion matrix for 12 gestures. (**B**) Normalized to percentage confusion matrix for 10 gestures, without gesture G5 (hand down) and G6 (hand up).  
Figure 7: (**A**) Comparison of accuracy vs. number of neurons between LSTM, GRU, RNN, and Feedforward architecture for two hidden layers. (**B**) Comparison of accuracy vs. number of gestures between LSTM, GRU, RNN, and Feedforward architecture.
## 4 Discussion
### Spatiotemporal Consistency of Winter Wheat Cropping Areas from the CDL and NASS Datasets  
Crop planted area and its spatial distribution are the first data layer for crop production monitoring and forecasting. A few studies compared the planted area derived from the CDL dataset and NASS statistics over a few years or over a few states [15; 36]. The CDL pixel-based planted area estimates generally showed a satisfying accuracy for major crops (85-95% for maize, soybean, and wheat), but had a tendency of underestimation compared with NASS planted area. The pixel counting estimation of crop acreage from the CDL may underestimate crop acreages for certain counties and states but may yield overestimation for other states and counties. It should be indicated that the R\({}^{2}\) value does not indicate that the estimation is biased up or down. In this study, the winter wheat planted area estimates in the CONUS for the CDL dataset showed a strong spatiotemporal consistency (R\({}^{2}\) = 0.98) with those from the NASS dataset from 2008-2018 at the county scale (Figure 3). This high spatiotemporal consistency can be largely attributed to the accuracy of the CDL and NASS statistical data, especially for major crop types (winter wheat, soybean, and maize) [15] in the major crop production areas. The high accuracy and robustness of the CDL dataset for winter wheat planted area estimation makes it reliable for crop monitoring.  
Crop harvested area and its spatial distribution were directly related to crop production because some crop fields could not be harvested due to damage, failure, and other factors. However, there is still a lack of pixel-based harvested area at a regional scale. Thus, it is important to calculate the difference between planted and harvested area for monitoring crop production. In our study, we found that the spatiotemporal consistency of winter wheat planted and harvested area in the CONUS varied substantially, especially in the Southern Great Plains and in some spring drought years (2011, 2013-2014), with a difference of more than 20% (Figures 2 and 3). The Southern Great Plains are one of the key regions for the nation's wheat and beef production. In many counties of the Southern Great Plains region, winter wheat is used as part of a dual purpose graze-grain system, serving as both a grain crop and a forage crop to supply beef cattle [35; 53]. Drought is an important factor that can lead to the loss of grain yield and abandonment of winter wheat acreage in the Southern Great Plains. Winter wheat crops in this area normally break from dormancy in late February or early to mid-March. A lack of precipitation or snow before the dormancy period could significantly reduce the growth of winter wheat crops [54]. These factors could have led to the significant differences between planted and harvested area, especially over the Southern Great Plains.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 3 Challenges of eHealth and AI Applications in Healthcare
### Accessibility and Affordability  
While artificial intelligence can provide revolutionary insights in ways human-based data analysis cannot, for many low-income countries, the cost and availability of such technologies can be a real obstacle. Studies such as [60] provide a counterargument, showing the role and the level of efficiency machine learning algorithms can have in predicting the mortality of children under five years old in low-to-middle income countries.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Overall Aerodynamic Characteristics
#### 4.3.1 Drag  
In the free-molecular limit (\(Kt_{\infty}\to\infty\)), an exact analytical solution can be derived for the rotating sphere flow problem, and the drag coefficient [18] reads  
\[C_{D}=\frac{4S^{4}+4S^{2}-1}{2S^{4}}\text{erf}(S)+\frac{2S^{2}+1}{\sqrt{\pi}S ^{3}}\exp\Bigl{(}-S^{2}\Bigr{)}+\frac{2\alpha\sqrt{\pi}}{3S}\sqrt{\frac{T_{w} }{T_{\infty}}} \tag{4}\]  
Figure 10: The DSMC results of pressure coefficient \(C_{p}\) on the sphere for different rotation parameters \(W=\omega R/V_{\infty}\). Subfigures (**a**–**e**) correspond to \(W=0.00\), \(0.25\), \(0.50\), \(0.75\), and \(1.00\), respectively.  
where \(S\) is the freestream speed ratio, which differs from \(M_{\infty}\) only by a constant factor  
\[S=\sqrt{\frac{\gamma}{2}}M_{\infty} \tag{5}\]  
and \(\gamma=1.4\) is the specific heat ratio of the freestream air. Under the flow conditions in this study, the free-molecular solution (4) becomes \(C_{D}=2.2036\), which is independent of \(W\).  
Figure 11: The DSMC results for the heat transfer coefficient \(C_{h}\) on the sphere for different rotation parameters \(W=\omega R/V_{\infty}\). Subfigures (**a–e**) correspond to \(W=0.00\), \(0.25\), \(0.50\), \(0.75\), and \(1.00\), respectively.  
Figure 12 shows the drag coefficients computed through the DSMC method for different values of \(W\), along with the theoretical solution under the free-molecular assumption. In comparison with the free-molecular solution, the simulation data are lower. This discrepancy is reasonable, because the flow Knudsen number \(Kn_{\infty}\) considered in this paper is only 0.51, while the commonly-used criteria for the free-molecular regime is \(Kn_{\infty}>10\). However, the variation tendency of \(C_{D}\) with the growth of \(W\) is similar to the free-molecular solution. As \(W\) increases, the drag of the sphere stays almost constant.
# Analysis of Score-Level Fusion Rules for Deepfake Detection
## 3 Ensemble Methods for Deepfake Detection  
Ensemble methods and multi-modal or uni-modal fusion are constantly being exploited in many fields of pattern recognition to improve the ability to generalize and deal with intra-class variations and inter-class similarity. Fusion can be performed at various levels of a classification system, including at the sensor level, feature level, score level and decision level [27; 28; 29].  
Within the context of deepfake detection, [PERSON]. [30; 31; 32] explored the fusion of features of different nature, i.e., spatial and in the frequency domain or statistical and semantic, exploiting their complementarity. In [8], the authors proposed two different fusion techniques: the feature-level fusion of face patches from different facial regions and score-level fusion of three different classifiers. The simple average was used. In [26], the authors designed an ensemble of different neural networks by averaging the output score from each model. These previous works proposed a fusion approach as part of a more complex system. In this paper, we want to systematically evaluate which score-level fusion methods are best suited for the task.  
Thus, our focus is on the fusion rules. In particular, we investigate fusion at the score level, as this allows exploiting the complementarity without increasing the complexity of the system as is usually done in feature-level fusion or relying only on the binary outcome as in decision-level fusion [27; 33; 34; 35; 36]. Moreover, we explored various fusion approaches, represented in Figure 2, which can be categorized into non-parametric and parametric methods [27; 37]. The proposed parametric methods can be further divided into two categories: methods based on weighted averages and those based on classification models.
# Finite-Horizon Kinetic Energy Optimization of a Redundant Space Manipulator
## 2 Materials and Methods
### Mathematical Formulation
The integral cost function is used to obtain a velocity vector \(\dot{\mathbf{q}}_{prediction}\) pointing to the direction in which the kinetic energy integral is minimum for the chosen trajectory. This velocity vector is used as an extra (secondary) task added to the manipulator motion through the Jacobian null space as per (2). The steps of the proposed method, called Predictive Minimization of Kinetic Energy (PMKE) method, are presented in Table 1.