esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 2 Related Works
### Noise Robust Loss Functions  
A loss function is considered noise-robust if the classifier achieves the same classification accuracy on both noisy and noise-free data [24]. Compared to other robust methods such as relabelling [25] and sample importance weighting [26], robust loss is a simpler and more general method. Currently, symmetric loss is one of the mainstream robust loss functions [27; 28], such as MAE [13], RCE [15] and so on. To make any loss symmetric, [PERSON] et al. [17] proposed normalised loss functions using a simple normalisation operation. However, this operation actually changes the form of loss functions, which leads to difficulties in optimisation. Consequently, the fitting ability of the symmetric loss function is limited by the symmetry condition [29], and the model underfitting problem is prone to occur. Inspired by the advantages of symmetric [15] or complementary learning [30], the APL [17] framework was proposed for robust and sufficient learning, which is combined with active loss and passive loss to mutually reinforces each other and solve the model underfitting problem. Although APL has shown excellent performance among many robust loss functions, it has two hyperparameters (\(\alpha\) and \(\beta\)), which cannot maintain optimal performance on any dataset without tuning. Recently, asymmetric loss functions have been proposed as a new class of robust loss functions by [PERSON] et al. [31]. Meanwhile, several asymmetric loss functions such as asymmetric unhinged loss (AUL) and asymmetric exponential loss (AEL) have also been proposed by [PERSON] et al. [31]. However, this type of asymmetric loss function requires that each sample in the training dataset has a higher probability of being labelled with a true semantic label than any other class of labels during image classification. This means that it is ineffective for noisy label types that are easily confused. Therefore, in this paper, the active passive loss function (APL) is still chosen as an improved benchmark for remote sensing image retrieval with noisy labels.
## 3 Experiments and Discussions  
### Experimental Area and Data  
In order to verify the performance of this research method, a Google S2 coding-based search method for marine vessel rescue was implemented in this study using the Python programming language. The experiment takes the East China Sea (23\({}^{\circ}\)00\({}^{\prime}\) N-33\({}^{\circ}\)10\({}^{\prime}\) N, 117\({}^{\circ}\)11\({}^{\prime}\) E-131\({}^{\circ}\)00\({}^{\prime}\) E) as the spatial range, and generates different magnitudes of ship position datasets (I \(\times\) 10\({}^{2}\), 1\(\times\) 10\({}^{3}\), 1\(\times\) 10\({}^{4}\), 1\(\times\) 10\({}^{5}\), and 1\(\times\) 10\({}^{6}\)) by simulating the scenario in which the AIS or VMS systems collect the ship's location information at minute intervals. The effective number of bits is retained at 8. The hardware and software environment used is shown in Table 5.
## 2 Materials and Methods
### Characterization  
#### 2.3.1. SEM  
The morphology and distribution of chitosan/sodium phytate/Al(OH)\({}_{3}\) loaded on the wood surface were observed by scanning electron microscopy (SEM, FEG, Quanta 400) with a scanning voltage of 20 kV under a vacuum atmosphere. The sample was cut into wood pieces at a size of 1 \(\times\) 1 \(\times\) 1 mm, and then an Au film of 5-10 nm thickness was coated under vacuum conditions.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 2 Secure and Resilient Smart Grid Frameworks
### Resilient Framework: NIAC  
The National Infrastructure Advisory Council (NIAC) classified the four stages in the resilience framework with more detailed high-level concepts when the system encounters incidents: robustness, resourcefulness, rapid recovery, and adaptability [6; 7], which are illustrated in Figure 2.  
Robustness denotes the continuity ability of the smart grid when facing incidents, which refers to the infrastructure hardening and substitute/redundant system designed to support the grid to keep operating during an incident. Resourcefulness denotes the response prioritisation ability when incidents are happening, which relies more on skillful and experienced people to mitigate damage. Rapid recovery denotes the recovery ability of the smart grid after incidents, which requires detailed continuity plans and emergency operations to set the grid back to its normal state. Adaptability denotes the knowledge and experience gained from previous incidents, which involves revising the system operations, patching vulnerabilities, and applying new techniques to improve the system's robustness, resourcefulness, and rapid recovery capabilities.
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 3 Results and Discussions  
### Surface Characterization of Pure Levocetrizzine Samples  
To identify the active ingredient on the surface of the carrier, both forms of levocetrizzine (dihydrochloride and free base) were analyzed by XPS before the immobilization experiments. The two forms can be distinguished by the chemical states of nitrogen (C-N 399.4 eV, C-N+ 401.7 eV) and chlorine (Cl-C 200.7 eV, Cl- 197.6 eV). The surface compositions for the resolved chemical states are shown in Suppl. Table S1. Both molecule forms are on the surface, indicated by the chemical states of nitrogen and chlorine. The quantity of Cl- is relatively low compared to N+. The moderate oxygen surplus may suggest that some of the Cl- were replaced by OH- groups. The concentrations of the carboxyl groups are also lower than expected.
# Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping
## 5 Synthesis Time Reduction  
Reducing the time required for synthesizing reversible circuits is essential to reduce the turnaround time for each reversible chip. Most works related to reversible circuit BDD-based synthesis are related to minimizing the quantum cost for a circuit through reversible circuit size minimization; only a minority of works considered the time consumed in the synthesis process [15]. However, the size of the BDD and the size of its resulting circuit depends mainly on the ordering of the corresponding BDD nodes. However, a smaller size of the reversible circuit is not guaranteed by a smaller size of the BDD. Therefore, BDD-based synthesis algorithms for reversible circuits sometimes include the mapping phase in the optimization process for BDD reordering (as shown in Figure 5) which abruptly increases the overall synthesis time [28]. Thus, it is necessary to minimize the time consumed in this mapping phase.  
The one-to-one mapping between BDD nodes and their corresponding reversible gates is a time-consuming process, especially in the case of large reversible functions [27]. Because the algorithm has to traverse all nodes in the reduced BDD, the process of constructing the corresponding reversible circuit consumes a large amount of time. Instead of nodal-based mapping, our idea is to consider an entire subtree to be converted directly to a predefined set of reversible gates in the process of reversible circuit synthesis in an attempt to minimize the overall synthesis time. To develop this idea, a new look-up table has to be constructed that maps all possible subtrees to their corresponding reversible gates. This also includes altering the structure of the BDD nodes that form the parents for the considered subtrees by adding a special code to the node that represents the type of the subtree under consideration during the reduction process of the BDD. To do this, and depending on the possible nodes which appear in Table 2, all possible formations of subtrees that could be formed are studied and a new mapping table is constructed. During the traversal of a given BDD, and after studying its nodes, all subtrees are mapped to their corresponding reversible gates as they appear in the new look-up table. The following illustrates the process in detail.  
Figure 5: BDD-based synthesis for reversible circuits, integrating the mapping phase in the BDD optimization process.
## 2 Materials and Methods
### Methods  
Proximate, ultimate, and surface analysis were run by UK Biochar Research Centre (School of Geosciences, University of Edinburgh, Kings Buildings, Edinburgh, EH9 3 JN Scotland, UK) and reported in the datasheets of the biochars [15]. Analysis showed the high reproducibility of the materials.  
The morphology of biochars was investigated using a Field Emission Scanning Electron Microscope (FE-SEM, Zeis SupraTM 40).  
Biochars were also analyzed in the range from 500 cm\({}^{-1}\) to 4000 cm\({}^{-1}\) using a Fourier transformed infrared (FT-IR) spectrometer (Nicolet 5700, Thermoscientific, Waltham, MA, US) operated in attenuated total reflectance (ATR) equipped with a diamond window (Smartorbit, Thermoscientific, Waltham, MA, US)).  
Raman spectroscopically analysis was carried out using Renishaw(r) Ramanscope InVia (H43662 model, Renishaw, Gloucestershire, UK).  
To prepare composites, biochars were mechanically pulverized and subsequently dispersed into the epoxy monomer using a tip ultrasonicator apparatus (Sonics Vibra-cell) for 15 min.  
Particle size distribution was evaluated using a laser granulometry (Fritsch Analysette 22, Idar-Oberstein, Germany) after dispersion in ethanol and sonication in an ultrasonic bath for 10 min.  
Ultrasonication has been proven as a very effective method to realize well dispersed suspensions of CNTs in water [17] or polymeric media [18]. Despite the good performances, ultrasonication could induce disruptive phenomena due to ultracavitation effect with localized hot spot up to a thousand \({}^{\circ}\)C [19]. In order to avoid the instantaneous temperature rise, ultrasounds were pulsed with cycles of 20 s alternating to pause of 10 s to allow a better heat diffusion. After the addition of the curing agent, the mixture was further ultrasonicated for 2 min and left into the moulds for 16 h at room temperature. A final thermal curing was performed using a ventilated oven (I.S.C.O. Srl \"The scientific manufacturer\") at 70 \({}^{\circ}\)C for 6 h.  
Biochar containing composites were produced in dog-bone shape according with ASTM 638 procedure. Samples were tested using a MTS machine (MTS Q-test10). Data were analyzed using a homemade software compiled using Matlab(r). Average values of at least three samples were reported.
## 3 Elastographic Methods
### Strain Imaging (Real-Time Elastography (RTE))  
Real-time elastography (RTE) has been used mainly for the evaluation of the pancreas [83; 84; 85; 86], the thyroid [87; 88; 89; 90; 91; 92; 93; 94], the prostate [95], the breast [5,96] but also for the liver [1,2,7,97]. Published evidence in children is scarce and contradictory [98; 99; 100].
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 3 Problem Definition
### Assumptions and Definitions  
At first, we need to make some assumptions about the posed enemy avoidance problem.  
**Assumption 1**.: _The enemy UAV has constant desired velocity and desired heading angle, while the actual velocity and heading angle of the enemy UAV oscillate a little around the desired ones._  
**Assumption 2**.: _The decision making UAV can change its attitude and attack the enemy UAV during its flight according to the action decided by the UAV agent._  
**Assumption 3**.: _The heights of the UAVs are not considered. Therefore, all the distances in the problem are simply computed by two dimensions._  
**Assumption 4**.: _Each UAV obtains its own position and attitude (velocity and heading angle) with its on-board sensor system. The decision making UAV can obtain information about all the UAVs through the ground station._  
**Assumption 5**.: _There are no other UAVs in the region except the decision making UAV and the enemy UAV, as well as no obstacles in the region._  
In these assumptions, Assumptions 1 and 3 are used to simplify the enemy avoidance problem, so that we can focus more on other more important factors in the enemy avoidance problem. The researched results then can be used as the basis of more practical problems. With Assumption 5, this paper can focus on the collision avoidance of the enemy UAV, and does not need to consider collision with other UAVs and obstacles.  
Furthermore, we give some definitions that will be used in solving the enemy avoidance problem.  
**Definition 1**.: _(Distance). The distance between two points \(a=(x_{a},y_{a})\) and \(b=(x_{b},y_{b})\) is calculated with the following equation:_  
\[d_{ab}= \sqrt{(x_{a}-x_{b})^{2}+(y_{a}-y_{b})^{2}} \tag{1}\]  
**Definition 2**.: _(Reaching Goal). A UAV \(f\) is regarded as having reached a goal \(G\) when the following condition is satisfied:_  
\[d_{fg}<r_{g} \tag{2}\]  
_where \(d_{fg}\) denotes the distance between the UAV \(f\) and the goal point \(G\), and \(r_{g}\) is the specified goal radius._  
**Definition 3**.: _(Collision). A UAV \(f\) is regarded as having collided with the enemy UAV \(e\) when the following condition is satisfied:_  
\[d_{fe}<r_{c} \tag{3}\]  
_where \(d_{fe}\) denotes the distance between the UAV \(f\) and the enemy UAV \(e\), and \(r_{c}\) is the specified collision radius._  
Figure 2: The decision making framework.  
**Definition 4**: _(Attacking Probability). The success of an attacking action is defined by the attacking probability \(P\), which is specified by the following equation:_  
\[P=e^{1-\frac{d_{fc}}{2\pi}} \tag{4}\]
## 7 Results and Discussion
### Validation Lising Simulation  
The comparisons of the sensitivity of PW, LES and TES of retracked and non-retracked waveform to wind speed are presented in Figure 7. From the figure, it is clear that the PW, LES and TES of retracked waveform have more sensitive to wind speed than the ones of non-retracked waveform. In addition, retracking method proposed above could recover PW and LES better than TES. To explore the reason, the mean square errors of leading edge, peak and trailing edge between Z-V waveform and the corresponding analytical Model (20) are computed. The results are given in Figure 12 which shows that the fitting error of the TES is much larger than the ones of the PW and LES. In other word,  
Figure 11: Delay waveform with the tracking refresh period of 1 ms, (**a**) 1 s, (**b**) 3 s; and corresponding waveform after retracking using Equation (19) for UK-TDS-1.  
Figure 10: Delay waveform with the tracking refresh period of 1 ms, (**a**) 1 s, (**b**) 3 s; and corresponding waveform after retracking using Equation (21) for UK-DMC.  
the larger fitting error of the TES causes the poorer retracking result compared to the ones of the PW and LES.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Error Probability
Now, the closed-form expression for \(P_{div}(e)\) is obtained by putting Equation (26) in Equation (25) and performing integration. We need to integrate from 0 to \(\gamma_{0}\) for case \(\gamma_{d}\leq\gamma_{0}\). \[ \begin{array}{rcl}P_{div}(e)&=&\frac{d}{(\gamma_{R_{1}}-\gamma_{SD})(1-e^{ \frac{-\gamma_{0}}{\gamma_{SD}}})}\times\\ &&\left[\frac{\gamma_{R_{1}}-\gamma_{R_{1}}\sqrt{b\gamma_{R_{1}}(1+b\gamma_{R_ {1}})}}{erf(\sqrt{\gamma_{0}(b+1/\gamma_{R_{1}})})-\gamma_{R_{1}}e^{\frac{- \gamma_{0}}{\gamma_{R_{1}}}}}{erfc(\sqrt{\gamma_{0}b})}\right]-\\ &&\frac{d}{(-\gamma_{R_{1}}+\gamma_{SD})(-1+e^{\frac{-\gamma_{0}}{\gamma_{SD}} })}\times\\ &&\gamma_{SD}\left[-e^{\frac{-\gamma_{0}}{\gamma_{SD}}}+\sqrt{b\gamma_{SD}(1+b \gamma_{SD})}e^{\frac{-\gamma_{0}}{\gamma_{SD}}}{erf(\sqrt{\gamma_{0}(b+1/ \gamma_{SD})})})+erfc(\sqrt{\gamma_{0}b})\right]\end{array} \] (27)
**Case 2**: \(:[\left(\gamma_{SD}+\gamma_{R_{1}}\right)\leq\gamma_{0}]\)  
In this case, relays, \(R_{1}\) and \(R_{2}\), perform retransmission given that first retransmission was not successful. Here, \(\gamma_{d}=\gamma_{SD}+\gamma_{R_{1}}+\gamma_{R_{2}}\), where \(\gamma_{d}\) represents output SNR at D and \(\gamma_{R_{i}}\) is given in Equation (B2). \(P_{div}(e)\) can be written as:  
\[P_{div}(e)=\int_{0}^{\infty}a\times erfc(\sqrt{bz})f_{\gamma_{d}}(z|(\gamma_ {SD}+\gamma_{R_{1}})\leq\gamma_{0})\;dz \tag{28}\]  
The PDF of random variable, \(\gamma_{d}\), will be the convolution of the PDFs of \(\gamma_{SD}\), \(\gamma_{R_{1}}\) and \(\gamma_{R_{2}}\). Hence, \(f_{\gamma_{d}}(z|(\gamma_{SD}+\gamma_{R_{1}})\leq\gamma_{0})  
where,  
\[u=\frac{\bar{\gamma}_{SD}b}{1+\bar{\gamma}_{SD}b} \tag{31}\]  
\[v=\frac{\bar{\gamma}_{R_{1}}b}{1+\bar{\gamma}_{R_{1}}b} \tag{32}\]  
\[w=\frac{\bar{\gamma}_{R_{2}}b}{1+\bar{\gamma}_{R_{2}}b} \tag{33}\]
**Case 3**: \(\left[(\gamma_{SD}+\gamma_{R_{1}}+\gamma_{R_{2}})\leq\gamma_{0}\right]\)  
For this case, relays, \(R_{1},R_{2}\) and \(R_{3}\), perform retransmission given that second retransmission was not successful and third retransmission is required from \(R_{3}\). Here, \(\gamma_{d}=\gamma_{SD}+\gamma_{R_{1}}+\gamma_{R_{2}}~{}+~{}\gamma_{R_{2}}\), where \(\gamma_{d}\) represents output SNR at D and \(\gamma_{R_{i}}\) is from Equation (B2). \(P_{div}(e)\)  
The results also show that in low SNR, system benefits from the diversity gain, however, at high SNR, both m = 2 and m = 3 tend to get parallel with the direct transmission. This is because, at high SNR destination rarely need assistance from relay to perform retransmission.
## 2 Instrumentation and Methods
### Instruments and Measurements  
The EC system including a 3-D sonic anemometer (Model CSAT3, Campbell Scientific Inc., Logan, UT, USA), open-path CO\({}_{2}\)/H\({}_{2}\)O analyzer (Model LI-7500, Li- COR Inc., Lincoln, NE, USA) and CR5000 datalogger (Campbell Scientific Inc.) was located at 23 and 39 m above the ground to measure CO\({}_{2}\) flux at Qianyanzhou subtropical coniferous plantation [46], and the observational data (NEE, Re and GPP) at 23 m during 1 January 2013 to 31 December 2016 were used in this study.  
Solar global radiation (Q), direct radiation (D), and PAR were measured using radiometers (model TBQ-4-1 and TBS-2, 322 Institute of Jinzhou, China; LI-190 SA Quantum Sensor, LI-COR, Inc., Lincoln, NE, USA,) and a solar radiation system with a frequency of 1 Hz at the Qianyanzhou Experimental Station (CAS), 800 m away from the flux tower [47]. The diffuse radiation (S) is derived from Q-D\(\times\)cosZ, \(Z\) is the solar zenith angle. Q, D, and PAR were measured routinely, except from 1 January to 21 May 2013, when Q and PAR were obtained from the weather station at the Qianyanzhou Experimental Station. All solar radiation sensors were cleaned every morning and when they are needed. Meteorological parameters, temperature (\({}^{\circ}\)C), relative humidity (RH, %), and water vapor pressure (E, hPa) were measured using a HOBO weather station (Model H21, Onset Company, Bourne, MA, USA) [47].
## 3 System Model
### Block Data Transmission Model
#### 3.3.2 R2R Transmission Latency  
For R2R communication, where RSUs are connected via wired links, the transmission latency incurred when RSU \(r_{j}\) transmits a block \(b_{k}\) of size \(s_{k}\) to RSU \(r_{i}\) can be expressed as follows:  
\[TD(r_{j},r_{i},b_{k})=\frac{s_{k}}{B_{ij}} \tag{7}\]  
where \(B_{ij}\) denotes the available transmission bandwidth between RSU \(r_{j}\) and RSU \(r_{i}\).
## 3 Results and Discussion  
### Compressive Strength  
Figure 4 plots graphs for the compressive strength of concrete according to the C/S molar ratio set by adjusting the quantities of silica fume and quartz powder. The highest compressive strength (\(\geq\)200 MPa) was exhibited by the specimens with a C/S molar ratio ranging from 0.7 to 0.9. At the C/S molar ratio ranges of \(\leq\)0.6 and \(\geq\)0.95, both silica fume and quartz powder showed lower compressive strength, with the mix ratio adjusted by silica fume showing even lower compressive strength. These results are slightly different from those of previous studies that found the optimal C/S molar ratio range was 0.85 to 1.0 in normal strength concrete and foamed concrete with a high water/binder ratio and a more spacious concrete matrix for the formation of crystalline hydrates [21,22]. These disparate results are presumably ascribable to differences due to the residual unreacted silica in the inorganic high-strength concrete specimens of fine masonry. In addition, the low compressive strength exhibited by specimen SF0.6 with high silica fume content is considered to be associated with the generation of micropores due to an increase in specific surface area.  
Figure 4: Compressive Strength of concrete depending on C/S molar ratio.
## 3 Methodology
### Derivation and Upscaling of Landsat 8-Based Indices
#### 3.1.4 Upscaling of Landsat 8 Indices  
Landsat 8 data-derived indicators were upscaled to 1 km resolution by means of a simple aggregation approach. First, MODIS 8-day 1 km ET product (MOD16A2) was re-projected with the same projection system as Landsat 8 data. For each indicator, pixels within each MODIS 1 km grid were averaged to generate Landsat 1 km indicators. If a considerable amount of 30 m pixels (>30%) were covered with cloud/cirrus/cloud shadow within a 1 km grid, the grid was excluded from modeling. [PERSON] _et al._[27] has shown that the simple aggregation method produced good consistency  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multicolumn{8}{c}{**Band Number**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{5-8}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline Landsat 8 OLI & 0.130 & 0.115 & 0.143 & 0.180 & 0.281 & 0.108 & 0.042 & 1.00 \\ Landsat 7 ETM+ & 0.254 & 0.149 & 0.147 & 0.311 & 0.103 & \(-\) & 0.036 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Weighting coefficients based on at-surface solar radiation derived from SMARTS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Landsat 8 OLI**} & \multicolumn{3}{c}{**Landsat 7 ETM+**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Band** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Band Limits** \\ **(\(\upmu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **UPb and** \\ **LOb Bound** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Band** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Band Limits** \\ **(\(\upmu\)m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **UPb and** \\ **LOb Bound** \\ \end{tabular} } \\ \hline
1 & 0.43–0.45 & 0.30–0.450 & 1 & 0.45–0.52 & 0.30–0.520 \\
2 & 0.45–0.51 & 0.450–0.520 & 2 & 0.52–0.60 & 0.520–0.615 \\
3 & 0.53–0.59 & 0.520–0.615 & 3 & 0.63–0.69 & 0.615–0.725 \\
4 & 0.64–0.67 & 0.615–0.760 & 4 & 0.77–0.90 & 0.725–1.225 \\
5 & 0.85–0.88 & 0.760–1.225 & 5 & 1.55–1.75 & 1.225–1.915 \\
6 & 1.57–1.65 & 1.225–1.880 & 6 & 10.40–12.50 & Thermal \\
7 & 2.11–2.29 & 1.880–4.000 & 7 & 2.09–2.35 & 1.915–4.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Landsat 8 OLI and Landsat 7 ETM+ band width and \(UP_{b}\) and \(LO_{b}\) in Equation (5).  
between Landsat 8 derived NDVI and MODIS NDVI at MODIS resolution. MOD16A2 QA masks were also used to remove grids with low quality caused by cloud/aerosol states or algorithm failure [33].