esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Source and Methods
### Data Source
The fundamental principle behind retrieving the column-averaged dry-air mole fraction of methane (XCH\({}_{4}\)) using Sentinel-2 is based on the absorption of solar radiation by methane present in the atmosphere, which is primarily centered around wavelengths of 1600 and 2300 nm, corresponding to Sentinel-2's bands 11 and 12. Figure 2 illustrates the methane spectral transmittance based on the high-resolution HITRAN2020 Sentinel-2B [30; 31]. The MultiSpectral Instruments (MSIs) on Sentinel-2A and Sentinel-2B have slightly different positions and widths for the spectral transmittance windows. Band 11 encompasses a set of weaker methane absorption lines near 1650 nm, the molecular absorption database, and the spectral response functions of Sentinel-2A, while band 12 contains stronger absorption lines within the range of 2200-2300 nm. The average methane transmittance for band 12 is 2 to 3 times lower than that of band 11. Multispectral sensors (such as Sentinel-2) operate based on imaging spectrometry techniques. They split the incident full-spectrum or broad-band light signals into several narrow-band beams and then image each of them onto the corresponding detectors. These detectors are typically arrays of pixels, with each pixel corresponding to a specific spectral band [17]. When the light signal strikes the surface of an object, the object reflects or emits light in specific bands, which is captured by the sensor and converted into electrical signals. Through data processing and image analysis, the characteristic information of the target substance in different spectral bands can be extracted. Hyperspectral sensors (such as GF5-02-AHSI and EnMAP) work on a principle similar to that of multispectral sensors but can obtain more and narrower spectral band information. Taking EnMAP as an example, its sensor, with its finer spectral resolution, can capture the subtle spectral characteristics of objects in different bands, thereby providing richer information about the objects. For instance, in methane emission detection, hyperspectral sensors can more accurately identify and quantify methane emissions by utilizing the absorption characteristics of methane in specific bands. To identify methane emission hotspots in China and the United States and monitor them frequently, this study initially employs a match filter algorithm applied to EnMAP/AHSI data for the detection of these areas. Subsequently, for the identified hotspots, we utilize the Google Earth Engine (GEE) platform [33] to process Sentinel-2 Level 1C (L1C) data, specifically selecting the top-of-atmosphere reflectance in bands 11 and 12 for individual plumes. The data are filtered based on a quality assurance (QA) threshold greater than 0.5 to conduct remote sensing identification and the quantification of abnormal point source emissions within these methane hotspot regions.
## 3 Results and Discussion
### Adsorption Kinetics  
Besides adsorption equilibrium, the adsorption kinetics is another important aspect for parameter evaluation of the adsorption process, which is used to describe the solute uptake rate. Herein, the experimental data were analyzed by the pseudo-first- and the pseudo-second-order kinetic models. The nonlinear form is considered a better way to obtain the kinetic parameters [42], shown as Equations (9) and (10):  
\[q_{t}=\ q_{e}(1-e^{-k_{1}t}) \tag{9}\]  
\[q_{t}=\frac{k_{2}q_{e}^{2}t}{1+k_{2}q_{e}t} \tag{10}\]  
where \(q_{t}\) denotes the amount of ILs adsorbed on the resins at any time, mg/g; \(q_{e}\) is the equilibrium adsorption capacity, mg/g; \(k_{1}\) represents the pseudo-first-order rate constant, min\({}^{-1}\); \(t\) refers to the contact time, min; \(k_{2}\) stands for the pseudo-second-order rate constant, g/(mg\(\cdot\)min); \(h=k_{2}q_{e}^{2}\) is defined as the initial adsorption rate constant.  
The fitting parameters are summarized in Table 5, including uptakes of ILs at equilibrium state, adsorption rate constants, and correlation coefficients for the pseudo-first- and pseudo-second-order models. By comparing the values of R\({}^{2}\), it could be found that the pseudo-second-order model provided a better fit for two ILs' adsorption. The initial sorption rate, \(h\), followed the order of [Bmim][BF4] > [Emim][BF4], which was in accordance with the affinity order observed in the equilibrium isotherm experiments [43]. In addition, Figure 6 shows the adsorbed amount of ILs as a function of contact time. The uptake of ILs increased quickly to over 70% of equilibrium adsorption capacity within 1 min. These findings suggested that the adsorption of ionic liquids on the resin is a fast process. It also indicated that the rate-determining step may be a chemical adsorption involving valence forces through exchanging of electrons between ILs cation and the resin [42].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ionic**} & \multicolumn{3}{c}{**Pseudo-First-Order Model**} & \multicolumn{3}{c}{**Pseudo-Second-Order Model**} \\ \cline{2-7}  & \(q_{e,cal}\) **(mg/g)** & \(k_{1}\) **(min\({}^{-1}\))** & **R\({}^{2}\)** & \(q_{e,cal}\) **(mg/g)** & \(k_{2}\) **(10\({}^{-3}\)g/(mg\(\cdot\)min))** & \(h\)** & **R\({}^{2}\)** \\ \hline
[Bmim][BF4] & 943.3 & 3.05 & 0.921 & 969.0 & 2.90 & 2722.9 & 0.984 \\
[Emim][BF4] & 895.8 & 3.07 & 0.945 & 919.3 & 3.14 & 2653.7 & 0.972 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fitting parameters of kinetics (pseudo-first- and pseudo-second-order models) for adsorption of ionic liquids ([Bmim][BF4] and [Emim][BF4]) onto ion-exchange resin (Amberlite IR 120 Na).
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 2 Missile Dynamics and Environment Modeling
### Problem Formulation
#### 2.1.1 Scenario A: 2D Obstacle Avoidance  
This scenario focuses on the anti-ship missile with sea-skimming and waypoint navigation maneuvers. It considers the possible existence of friendly objects that should not be hit or specific objects that can be utilized to increase the survivability and success rate of the mission [20]. Previously proposed guidance laws have obvious limitations because they need all the placement and map information of the objects in the battlefield. In this paper, we propose a two-dimensional anti-ship missile guidance law that hits the target while avoiding obstacles without any prior information of their placement and map in the battlefield. The proposed guidance law, however, needs an extra onboard seeker that will be explained later.
# Low-Profile Harmonic Transponder for IoT Applications
## 4 Experimental Results
### Transponder RCS Measurements  
The transponder antennas are directly matched to the diode in this model, due to which the direct reflection coefficient measurement are not suitable. Alternatively, for verifying the design of transponder antennas, RCS measurement without the diode inclusion are conducted inside the anechoic chamber, where the transponder is placed on a foam substrate at a distance of \(30\,\mathrm{cm}\) from a double ridged horn antenna that is connected to the  
Figure 9: Utilized diode I–V curve.  
VNA (Vector Network Analyzer). Afterwards, a calibration procedure as presented in [20] is applied. The measured RCS frequency response is a good match with the simulated one as shown in Figure 10, and so verifies the designed transponder antennas. Moreover, the RCS is simulated considering the diode impedance at fundamental and harmonic frequencies, where the two resonance peaks are clearly observable at the design frequencies as shown in Figure 10.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 4 Methodology
### Generative Adversarial Network (GAN) Model
#### 4.4.1 LSTM and CNN  
* LSTM neural networks are emerged to add long-term memory function, which enhanced Recurrent Neural Networks' ability to deal with more complicated issues, including prediction and classification [29]. In the LSTM network, the input vector sequence X would be mapped to an output vector sequence y by i iterations. Figure 13 depicts the architecture of an LSTM cell.  
Figure 12: Feature Concatenation.  
Figure 13: The architecture of a traditional LSTM network.  
An LSTM cell contains three layers: an input layer, an output layer, and a memory block layer. The memory block includes three types of gates, including the input gate, the output gate \(o_{t}\), and the forget gate \(f_{t}\). Besides, \(c_{t}\) and \(c_{t}\) as memory cell vectors and the candidate value, respectively. The notation \(t\) represents a random time-step. During the training process, the related information can be updated as the following formula [30]:  
\[i_{t}=\sigma(W_{i}[h_{t-1},\{X\}_{t}]+b_{i}) \tag{2}\]  
\[f_{t}=\sigma(W_{f}[h_{t-1},\{X\}_{t}]+b_{f}) \tag{3}\]  
\[c=\tanh(W_{c}[h_{t-1},\{X\}_{t}]+b_{c}) \tag{4}\]  
\[c_{t}=f_{t}\times c_{t-1}+i_{t}\times c_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},\{X\}_{t}]+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}\times\tanh(c_{t}) \tag{7}\]  
where function \(\sigma(.)\) is a sigmoid function can be computed as follows:  
\[\sigma(x)=\frac{1}{1+\exp(-x)} \tag{8}\]  
\(W_{i}\), \(W_{f}\), and \(W_{c}\) are the weight and the of the input gate, input gate, and input gate, respectively, whereas the function \(\tanh(.)\) is a tangent function that is computed as follows:  
\[\tanh(x)=\frac{\exp(x)-\exp(-x)}{\exp(x)+\exp(-x)} \tag{9}\]  
* CNN  
In general, Convolutional Neural Networks (CNNs) are composed of a convolutional cell group, pooling layers, and a set of fully connected layers, as depicted in Figure 14.  
A convolutional layer i includes a group of filters \(W_{i}=\in\)R\({}^{S\times D\times N}\) that is convolved with an input tensor, S denotes the filters' number, D is a filter's size, and N represents the input channels' number [31]. A pooling layer can pool the output of a convolutional layer. Both convolutional and pooling layers are adopted to obtain the temporal patterns and to correlate temporally distant features. A set of fully connected layers follows the last convolutional/pooling layer to classify the input time series. The network output showed the dataset's classification result and provided one of the two labels (real, predicted) for each time step.  
\[\sigma(x)=\frac{1}{1+\exp(-x)} \tag{10}\]  
Figure 14: The architecture of traditional Convolutional Neural Network.
# Parameterized Algorithms in Bioinformatics: An Overview
## 5 Phylogenetics
### Combining and Comparing Phylogenies
#### 5.2.1 Consensus by Removing Trees  
When reconstructing a species tree from gene trees, we may hope that the gene trees of most of the sampled gene families actually agree with the species phylogeny and only few such families describe outliers that developed nonconformingly. In this case, we can hope to recover the true phylogeny by removing a small number of gene trees.  
Rooted Triplet Inconsistency (RTI) [noitemsep] **Input:** triplets \(T_{1},T_{2},\ldots\) on \(\mathcal{X}_{1},\mathcal{X}_{2},\ldots\), respectively, and some \(k\in\mathbb{N}\)  
**Question:** Is there a tree \(T^{*}\) on \(\mathcal{X}:=\bigcup_{i}\mathcal{X}_{i}\) such that \(T^{*}|_{\mathcal{X}_{i}}\
eq T_{i}\) for at most \(k\) of the \(T_{i}\)?  
**Results.** Rooted Triplet Inconsistency is NP-hard [156; 157; 158], even on \"dense\" triplet sets [159] (a triplet set \(\mathcal{T}\) is called _dense_ or _complete_ if for each leaf-triple \(\{a,b,c\}\), \(\mathcal{T}\) contains exactly one of \(ab|c\), \(ac|b\) and \(bc|a\)). While the general problem is W[2]-hard for \(k\)[159], the dense version admits parameterized algorithms. Indeed, [PERSON] and [PERSON] [160] showed parameterized algorithms running in \(O(4^{k}n^{3})\) and in \(O^{*}(2^{O(k^{1/3}\log k)})\) time, as well as an \(O(n^{4})\)-time computable, sunflower-based kernel containing \(O(k^{2})\) taxa (see [161] for details on the sunflower kernelization technique). Their result has recently been improved to linear size by [PERSON] et al. [162].  
**Notes.** Generalizing RTI to ask for a level-\(\ell\)_network_ displaying the input trees yields a somewhat harder problem, which can be solved for dense inputs in \(O(|T|^{\ell+1}n^{4\ell/3+1})\) time [163] and in \(O(|T|^{\ell+1})\) time for a particular class of networks [164].  
The unrooted-tree version of dense RTI (where the input consists of quartets) is known to be solvable in \(O(4^{k}n+n^{4})\) time [165].
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 4 Experiment
### Result
In the MARS dataset, the accuracy of Rank1 was improved by 0.8% relative to N = 4, and mAP was improved by 0.9% relative to N = 4. Such a result is also expected because for both the NLAM and NLTP, we inserted a non-local mechanism. Hence, a more extended sequence is more helpful for our model to extract long-range dependencies and obtain a more robust video-level feature descriptor. Comparison with state-of-the-art methods: We compared our method with the state-of-the-art method [15; 22; 28; 33; 35; 36; 37; 38] in the MARS and DukeMTMCVideoReID datasets. The results are shown in Table 7. Our final model selection was tested on the basis of N = 8 using SE-ResNet50 with the NLAM module added after the fourth and fifth CNN layers as the feature extractor and NLTP as the temporal aggregation layer. It is observed that our proposed model achieved a good performance. Especially in the MARS dataset, our method improved by 2.3% on CMC Rank-1 and nearly 1.8% on mAP compared to our baseline (Table 1). Compared with the state-of-the-art method [29], our method also improved the CMC Rank-1 by 1.4%. Our model also achieved impressive results in the DukeMTMCVideoReID dataset. Compared to the baseline (Table 1), our network improved by 0.9% and 1.3% on mAP and CMC Rank-1, respectively. We attribute this improvement to NLAM in frame-level feature extraction and NLTP in temporal feature aggregation to better obtain global information, resulting in a more robust feature representation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Sequence**} & \multicolumn{2}{c}{**MARS**} \\ \cline{2-4}
**Length** & **mAP** & **R1** & **R5** \\ \hline N = 2 & 77.1 & 83.6 & 94.2 \\ N = 4 & 78.9 & 85.5 & 95.3 \\ N = 8 & **79.8** & **86.3** & **95.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the effect of different sequence lengths N on the MARS dataset on our best model (SE-ResNet50 + NLAM (4,5) + NLTP). NLAM (4,5) means to add the NLAM layer after the fourth and fifth CNN blocks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Temporal**} & \multicolumn{2}{c}{**MARS**} & \multicolumn{2}{c}{**Duke**} \\ \cline{2-7}
**Aggregation** & **mAP** & **R1** & **R5** & **mAP** & **R1** & **R5** \\ \hline TP & 79.6 & 85.8 & 95.7 & 94.1 & 94.9 & 99.1 \\ NLTP (ours) & **79.8** & **86.3** & **95.8** & **94.5** & **95.0** & **99.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different time feature extraction methods NLTP and TP are compared on the MARS and DukeMTMC-VideoReID datasets. NLTP = non-local temporal pooling, TP = temporal pooling.
## 5 Experiments
### Results and Discussion
#### 5.2.3 Respiration Detection in Scenario 2  
The results of the second experiment conducted in scenario 2 are shown in Figures 10 and 11 and Table 3. The reference RR and RR extracted using our method are equal to 0.4329 Hz and 0.4482 Hz, respectively. Figures 10d and 11d suggest that the background residual method loses efficacy in the scenario with external environmental interference, which is based on the theory that the platform motion can be separated by the signal return from static ground in the range profile. When there is grass movement caused by UAV rotor rotation, the echo signal of ground no longer represents the platform motion; instead, the grass motion will be introduced into the detection results. This is the reason why the background residual method fails in scenario 2. Our method estimates the respiratory signal based on the non-Gaussian qualities and independence of the source signals, and the results does not depend on the quality of the obtained platform motion signal, so it is also effective in the scenario with external environmental interference.  
According to visual inspection and quantitative evaluation, this proposed method has a better effect in the aspects of reserving the details and SNR, making it more suitable for practical application. In terms of operability, our method does not need to manually extract the range bin where the ground echo signal is located, and it can better meet the requirements of real-time respiration detection of post-disaster ground injured people.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario 2**} & \multicolumn{2}{c}{**RR (Hz)**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**SNR (dB)**} \\ \cline{2-9}  & **Reference** & **Our Method** & **BGR** & **Our Method** & **BGR** & **Our Method** & **BGR** \\ \hline Subject 1 & 0.4329 & 0.4482 & 0.1268 & 96.47 & 29.29 & 6.69 & 5.48 \\ Subject 2 & 0.2930 & 0.2988 & 0.3682 & 98.02 & 74.33 & 7.38 & 5.95 \\ Subject 3 & 0.2500 & 0.2656 & 0.4016 & 93.76 & 39.36. & 6.74 & 4.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of respiratory rate in scenario 2. The data length was 30 s for each experiment.  
Figure 9: Frequency spectrum of the signals in Figure 8. (**a**) FFT of raw radar signal. (**b**) FFT of respiration from respiratory belt. (**c**) FFT of respiration extracted by our proposed method. (**d**) FFT of respiration extracted using background residual method.  
Figure 11: _Cont._  
Figure 10: Results of subject 1 in scenario 2. (**a**) Raw radar echo signal of subject 1 obtained using maximum energy method. (**b**) Reference respiration from respiratory belt. (**c**) Respiration extracted using our proposed method. (**d**) Respiration extracted using background residual method.
## 3 Methods
### Improved RF Models
#### 3.5.1 Improved Weights Based on the Accuracy  
First, let \(\{\textit{Tree}_{1}(x)\), \(\textit{Tress}_{2}(x)\), \(\textit{Tree}_{3}(x)\), \(\cdots\), \(\textit{Tree}_{t}(x)\}\) represent the set of decision trees. For the regular RF model, the final predicted values can be obtained through Equation (27), which indicates that the weight of every single decision tree is equal to \(1/t\).  
\[y_{pre}=\sum_{i=1}^{t}\frac{1}{t}\times\textit{Tree}_{i}(x) \tag{27}\]  
where \(t\) is the number of decision trees in a forest.  
For the improved weights based on R\({}^{2}\), the weight of every single decision is obtained through Equation (28), and the final predicted values can be obtained using Equation (29). The key step of this approach is to compute the weights of decision trees. After constructing the optimal RF model based on the training sets, the accuracy (i.e., R\({}^{2}\)) of each decision tree can be obtained. Then, according to Equation (28), all decision trees will be assigned a normalized weight, and the larger the term R\({}^{2}\), the larger the weight assigned, which means that these decision trees with large weights will have a greater role in the final prediction. Equations (28) and (29) are:  
\[\text{weight\_R}^{2}_{i}=\frac{\text{R}_{i}^{2}}{\sum_{i=1}^{t}\text{R}_{i}^{ 2}} \tag{28}\]  
\[y_{pre}=\sum_{i=1}^{t}\text{weight\_R}^{2}_{i}\times\textit{Tree}_{i}(x) \tag{29}\]  
Similarly, for the improved weights based on the RMSE, the weight of every single decision is obtained through Equation (30), and the final predicted values can be obtained using Equation (31). The weights of the decision trees are obtained by first taking the inverse of the RMSE and then performing a normalized calculation, which means that the larger the RMSE of a decision tree, the smaller the weight it is assigned to, and vice versa. Equations (30) and (31) are:  
\[\text{weight\_RMSE}_{i}=\frac{\frac{1}{\text{RMSE}_{i}}}{\sum_{i=1}^{t}\left( \frac{1}{\text{RMSE}_{i}}\right)} \tag{30}\]  
\[y_{pre}=\sum_{i=1}^{t}\text{weight\_RMSE}_{i}\times Tree_{i}(x) \tag{31}\]
# Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set
###### Abstract  
Due to technical aspects of Cone Beam Computed Tomography (CBCT), the automatic methods for bone segmentation are not widely used in the clinical practice of endodontics, orthodontics, oral and maxillofacial surgery. The aim of this study was to evaluate method's accuracy for bone segmentation in CBCT data sets. The sliding three dimensional (3D) window, histogram filter and [PERSON]'s method were used to implement the automatic segmentation. The results of automatic segmentation were compared with the results of segmentation performed by an experienced oral and maxillofacial surgeon. Twenty patients and their forty CBCT data sets were used in this study (20 preoperative and 20 postoperative). Intraclass Correlation Coefficients (ICC) were calculated to prove the reliability of surgeon segmentations. ICC was 0.958 with 95% confidence interval [0.896... 0.983] in preoperative data sets and 0.931 with 95% confidence interval [0.836... 0.972] in postoperative data sets. Three basic metrics were used in order to evaluate the accuracy of the automatic method--Dice Similarity Coefficient (DSC), Root Mean Square (RMS), Average Distance Error (ADE) of surfaces mismatch and additional metric in order to evaluate computation time of segmentation was used. The mean value of preoperative DSC was 0.921, postoperative--0.911, the mean value of preoperative RMS was 0.559 mm, postoperative--0.647 mm, the ADE value of preoperative cases was 0.043 mm, postoperative--0.057 mm, the mean computational time to perform the segmentation was 46 s. The automatic method showed clinically acceptable accuracy results and thus can be used as a new tool for automatic bone segmentation in CBCT data. It can be applied in oral and maxillofacial surgery for performance of 3D Virtual Surgical Plan (VSP) or for postoperative follow-up.  
cone beam computed tomography; automatic segmentation; sliding window; 3D virtual surgical plan; [PERSON]'s method Article