esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Calculating Burn Severity: Composite Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Pre-Fire Imagery Season**} & \multicolumn{2}{c}{**Post-Fire Imagery Season**} \\  & **Start** & **End** & **Start** & **End** \\ \hline \multirow{2}{*}{Extended} & 1 June YBF & 30 September YBF & \multirow{2}{*}{1 June YAF} & \multirow{2}{*}{30 September YAF} \\  & 1 June YOF & Fire Start Day & & \\ \hline \multirow{2}{*}{Hybrid} & \multirow{2}{*}{20 May YBF} & \multirow{2}{*}{31 August YBF} & Day after fire ends YOF & \multirow{2}{*}{15 November YOF} \\  & & & Day after snow cover ends YAF & & 1 July YAF \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Image season date ranges for pre- and post-fire imagery used in composite imagery methods for calculating burn severity. YBF indicates “Year Before Fire”, YOF indicates “Year of Fire”, and YAF indicates “Year After Fire”. brid composite approach is to minimize the influence of rapid herbaceous and shrub cover green-up following fire; this approach is shown to increase correlation of burn severity estimates with plot validation data in the boreal forests of North America [70]. Specifically, the MODIS Aqua and Terra Thermal Anomalies and Fire Daily Global datasets are used to estimate the date when there are no longer any high-confidence active \"fire\" pixels within the fire perimeter. All Landsat or Sentinel imagery occurring after the end of fire date and before November 15 of the fire year are included in the post-fire imagery season. If the end of fire date is not able to be determined by MODIS, then the start of the imagery season defaults to 15 September (this only occurred for the two smallest fires, Chippean and Mammoth). In the following year, the MODIS Aqua and Terra Snow Cover Daily Global datasets are used to estimate the earliest date of majority snowmelt (i.e., snow cover of 5% or less for each pixel within the fire perimeter), and imagery from this date to 1 July is added to the image stack used to compute the post-fire image composite. If a majority snowmelt date is not able to be determined by MODIS, then the start of the imagery season defaults to 30 April (this did not occur for any fires analyzed).
## 3 Case Study: Analysis of Urban Pipeline Sensor Placement Optimization
### Application of the Ant Colony Algorithm in Layout Optimization
For instance, as shown in Table 5, when the distance is between 50 and 60 m, the level coefficient \(\beta\) is 0.5, indicating a medium monitoring effect; when the distance exceeds 90 m, the \(\beta\) coefficient increases to 1, suggesting that signal boosting or enhanced monitoring measures are needed to maintain effective monitoring. The system also includes an important strategy: if the distance between two sensors is less than 60 m, the network layout must be reconfigured. This strategy helps prevent sensor over-density, ensuring that each sensor's monitoring area is fully utilized and minimizing signal interference. The system also includes an important strategy: if the distance between two sensors is less than 60 m, the network layout must be reconfigured. This strategy helps prevent over-dense sensor deployment, ensuring that each sensor's monitoring area is fully utilized. By importing the geographic information of the urban pipeline, the image is processed for recognition, distance calculation, and classification. In sensor placement, a key objective is to maximize coverage. Coverage is calculated as follows:  
\[\text{Coverage Rate}=\frac{\text{Total Sensor Coverage Area}}{\text{Total Pipeline Network Coverage Area}} \tag{16}\]
## 1 Introduction  
The major difficulties for state of the art and future wireless communication systems are the requirements of higher data rate and improved quality of service [1]. Bandwidth limitation and varying nature of the wireless channel are required to be investigated during the development of a new generation wireless systems [1]. Several advancements have been introduced for 5G and future wireless communication networks such as cooperative relaying systems.  
A dual-hop cooperative relaying system is a well-known technique, used in cooperative communication systems to achieve substantial improvements in the overall system performance [2; 3; 4; 5]. Impairments of wireless communication channels are mitigated, and many communication metrics, such as the data rate, coverage range, and link stability, were improved by the deployment of this technique [2; 3; 4; 5]. Mainly, there are two dual-hop relaying techniques, which are categorized as decode-and-forward (DF) and amplify-and-forward (AF) relays. In a DF relaying method, the received signals are first decoded and re-encoded, then forwarded to the destination or end-user, whereas, in an AF relaying method, the received signals from the transmitter or source node are amplified first, then forwarded to the destination [5; 6; 7; 8].  
In recent years, the integration of transmit and receive beamforming (i.e., using multiple antennas) in dual-hop relaying received a lot of attention due to its improvement of the quality and reliability of the wireless links [2; 3; 4; 5]. In multi-antenna systems, beamforming techniques are used to improve the signal-to-noise-ratio (SNR) [4].  
Besides the beamforming techniques, the implementation of adaptive transmission techniques in a cooperative relaying system provide a better system performance, as compared to non-adaptive transmission techniques [2]. In adaptive transmission schemes, the transmitter adapts the constellation size, transmit power, data rate, coding gain, or any other mixture of such parameters to increase the system performance, according to the channel conditions [2].
# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 3 Proposed Method
### Loss Function  
In this section, we will describe the loss function used in our model. MGCN consists of two heads, which are utilized for model training in the detection and recognition stages, respectively.  
For detection head, we employ two objective functions, the regression loss and the classification loss, to optimize the detection module.  
\[L_{dec}=L_{c1}+\lambda L_{r1} \tag{4}\]  
where \(\lambda\) denotes the balance parameter, which is set to 10. \(L_{c1}=\) and \(L_{r1}\) represent regression loss and classification loss, respectively.  
\[L_{c1}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}log(p_{i}) \tag{5}\]  
Figure 5: Spatial attention model.  
where \(y_{i}\) denotes the ground truth label of the \(i\)-th feature and \(p_{i}\) represents the predicted probability that the feature belongs to the \(i\)-th class.  
\[L_{r1}=-\frac{1}{N_{p}}\sum_{i=1^{N_{p}}}L_{l_{1}}(r_{i},\delta_{i}) \tag{6}\]  
where \(N_{p}\) presents the number of positive samples and \(L_{l_{1}}\) denotes the Smooth-\(l_{1}\) loss. The margin parameter \(r_{i}\) controls the separation between positive and negative features in the embedding space. \(r_{i}\) and \(\delta_{i}\) represent the calculated regressor and ground truth regressor, of \(i\)th positive sample, respectively. The detection head is trained at 0.5 IoU threshold, which aims to differentiate between positive and negative samples.  
For the second head, we adopt three objective functions to optimize the end-to-end person search model, the regression loss, the classification loss, and the identity classification loss.  
\[L_{reid}=L_{r2}+L_{nae} \tag{7}\]  
where \(L_{r2}\) represents classification loss, which is same as \(L_{r1}\). Follow [42], \(L_{nae}\) loss contains classification loss and identity classification loss [18]. \(L_{c1}\) aims to ensure the features extracted by the re-identification network have both high intra-class consistency and inter-class separability.  
Therefore, taking into account the aforementioned factors, the objective loss function \(L_{total}\) of MGCN can be expressed as  
\[L_{total}=L_{dec}+L_{reid} \tag{8}\]  
Moreover, we summarize the algorithm procedure of MGCN in Algorithm 1.  
```
1:Person search dataset;
2:Most similar person in each gallery image;
3:while not converge do
4:for\(epoch=1:20\);\(epoch++\)do
5: By trustworthy bounding box regression generates a set of feature map \(G\);
6: Construct the aligning module as in Equation (1) obtained feature \(\tilde{G}\);
7: Feed \(\tilde{G}\) into the channel attention model \(A_{c}(\tilde{G})\) according to Equation (2);
8: Feed feature map into the spatial attention model \(A_{s}(\tilde{G})\) according to Equation (3);
9: Learn the feature map \(M\) of each person by combining above multi-attention-guided module;
10:endfor
11:endwhile
```  
**Algorithm 1** Algorithm of MGCN
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 5 Simulation Studies
### Data Mule for Medium Complexity WSN (\(M=3,n=13\))  
In the next case study, a deployment of 13 static sensor nodes served by \(M=3\) MoEls is assumed. The storage capacity of these nodes is set to \([0,3,8,1,2,4,5,6,1,2,3,2,1]\) Kbytes, which corresponds to visiting/service time \(v_{t_{j}}=[0,24,68,7,15,34,39,48,7,15,24,15,7]\) s. The expiration time for the nodes is initialized as \(e\_initial=[0,1800,1500,1200,1200,1500,\)\(1500,1800,2400,2400,2400,1200,1800]\) s. The time budget limit for each route is \(B=400\) s, while the charging duration value is \(60\) s. Following the same approach of the combined MES scheme and MILP formulation, the simulation results are presented in Figure 4 and Table 3. In this Figure, the routes of the 5 th-9 th iterations of the algorithm are presented.  
In Table 3, it can be seen in the fifth, sixth, and eighth iteration rows that the estimated corresponding high prioritized nodes are \(9,3,\) and \(11\). These are the nodes that it is critical to be visited within the \(M\) successor routes; otherwise, their stored data will be counted as data loss. Indeed, it can be seen in Figure 4 that the aforementioned nodes are visited in the sixth, seventh, and ninth iteration extracted routes. Actually, since the initial expiration times \(e\_initial\) for the distant nodes \(9\) and \(11\) were both set to \(2400\) s, these nodes do not need to be visited quite frequently. Hence, the MoEls do not need to spend extra energy to meet these distant nodes in each iteration, and based on the estimated priority, these nodes are visited when it is critical for them to be served.  
Figure 3: Mobile Element Scheduling Routes with 3 MoEls and 7 Nodes.  
In the fifth iteration, we observe that the route of MoEl 3 passes close to the static node 8 without visiting it. This happens since in the fourth iteration of the algorithm, the expiration time for node 8 was calculated as 1500.6 s, which corresponds to low priority for the next iteration compared to the rest of the static nodes. The same occurs also for the same node in the eightth iteration route of MoEl 1. Hence, the MoEls try to visit the nodes that are in the first places of the prioritized list, while concurrently needing to meet the defined limit of each route time budget \(B\).
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 3 Experiments
### Ablation Experiments
#### 3.3.2 Ablation Experiment of Feature Fusion Network  
In this study, we introduce a Feature Aggregation Module (FAM), which is integrated into the Feature Pyramid Network (FPN) of the YOLO-OHFD framework. This enhancement is designed to improve the fusion capability of the model across various feature scales. The implementation of FAM aims to optimize the multi-scale feature integration, thereby enhancing the detection accuracy and robustness of YOLO-OHFD.  
In this experiment, we conducted group comparison experiments to verify the role of the FAM and its DCN in the feature fusion network. where FAM (Conv) denotes a FAM module based on ordinary convolution and FAM (DCN) denotes a FAM module based on DCN.  
As shown in Table 3, the experimental results demonstrate that the accuracy reaches 84.40% with an fps of 46.96 without the addition of the FAM. However, when the FAM with ordinary convolution is added, the accuracy does not significantly improve, but instead decreases, the detection speed. Meanwhile, the introduction of the DCN resulted in a significant improvement in accuracy, although the fps was slightly increased. because the reason is that we eliminate the bias in the upsampling process of oriented follicular features by introducing DCN and constructing learnable offsets. The ordinary convolution merely increases the complexity of the network, and there are still biases in the fusion of network features at different scales. According to the experimental results, it can be seen that the FAM based on DCN effectively improves the feature fusion capability of FPN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Class1** & **Class2** & **Class3** & **Class4** & **mAp** & **FPS** \\ \hline CSP + Res & 75.83 & 76.11 & 74.35 & 71.61 & 75.55 & 44.02 \\ CSP + Res2 & 79.76 & 79.19 & 77.39 & 75.18 & 78.91 & 46.19 \\ CSP + Res + ECA & 83.27 & 83.86 & 81.69 & 79.01 & 83.12 & 41.06 \\ CSP + Res2 + ECA & 88.56 & 87.39 & 84.38 & 81.96 & 87.05 & 43.63 \\ CSP + Res + CBAM & 82.96 & 82.37 & 79.25 & 76.95 & 81.83 & 33.26 \\ CSP + Res2 + CBAM & 87.33 & 86.17 & 82.96 & 80.18 & 85.77 & 35.61 \\ CSP + Res + CA & 83.56 & 83.11 & 80.96 & 77.52 & 82.68 & 36.02 \\ CSP + Res2 + CA & 88.17 & 86.89 & 83.62 & 80.76 & 86.51 & 37.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments on the effect of different modules in the feature extraction network on detection accuracy.
# A New Spatial Distance Metric for Verification of Precipitation
## 3 Results
### Analysis of Idealized Examples
All non-spatial methods will display such behavior since they only compare values at collocated grid points. The distance metrics,  
Figure 5: Same as the bottom frame of Figure 1a but instead of the distance metrics, the results of six non-spatial measures are shown: root-mean-square-error (RMSE), probability of detection (POD, also called hit rate), false alarm ratio (FAR), critical success index (CSI, also called threat score), Heidke skill score (HSS) and Gilbert skill score (GSS, also called equitable threat score). which are spatial methods, do not exhibit such behavior (Figure 1a). They clearly show that the forecast error grows even after \(d\) is larger than 40, with the best estimates of actual displacement provided by PSD and CDST. It is also worth noting that large displacements of precipitation are common in real-world situations. For example, in a 9-day forecast, large-scale phenomena like cyclones will frequently be positioned at completely wrong locations. Since most precipitation in the mid-latitudes can be attributed to the frontal systems located inside the cyclones, the displacement will be large, and the likelihood of precipitation in the forecast not overlapping with the actual precipitation will be high. Also, when \(d>40\), the value of RMSE will decrease if the forecast field is set to zero (not shown) - meaning that it would be better in terms of score's value for the model to predict no precipitation as opposed to predicting at least some precipitation at some non-overlapping location. This is an example of the so-called 'double penalty problem' mentioned in the introduction, where a forecast is penalized for predicting an event where it did not occur and again for failing to predict the event where it occurred. The HSS and GSS also exhibit such behavior, although the impact on their values is relatively small (not shown).
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 4 Progress of CO\({}_{2}\) Utilization in China
### Development Status of CO\({}_{2}\) Biological Utilization in China  
CO\({}_{2}\) biological utilization combines synthetic biology processes with CO\({}_{2}\) to produce valuable products, including food and feed, biofertilizers, chemicals, biofuels, and gas fertilizers [131]. Products generated by CO\({}_{2}\) biological utilization technology have high-added values and substantial economic benefits [132].  
In China, the conversion of CO\({}_{2}\) to food and feed has been commercialized on a large scale, with a CO\({}_{2}\) utilization of 1000 tonnes/year and an output value of about 50 million yuan/year; about 50,000 tonnes of CO\({}_{2}\) are utilized annually to create biofertilizers, with an annual production value of 500 million yuan; about 10,000 tonnes of CO\({}_{2}\) are used annually for chemical conversion, with an annual production value of 20 million yuan; and roughly 10,000 tonnes of CO\({}_{2}\) are used to make gas fertilizer each year, with an annual production value of about 20 million yuan [67].  
In addition to the existing CO\({}_{2}\) biological utilization technologies such as microalgae fixation [84, 133] and gas fertilization [59], the research institutions and universities in China are dedicated to the development of more advantageous CO\({}_{2}\) biological utilization technologies, and some outstanding outcomes have been obtained. [PERSON] et al. proposed an innovative method to produce high-performance biodegradable plastics by directly employing captured CO\({}_{2}\) in conjunction with cyanobacterial cell factories [134], which resolves the issues of plastic pollution and the replacement of non-grain raw materials in PLA production. [PERSON] et al. realized the efficient cell-free chemorenzymatic starch synthesis from CO\({}_{2}\), bringing the opportunity to replace the traditional starch manufacturing factory. This technical route of synthesizing starch from CO\({}_{2}\) has achieved the first-time complete synthesis of CO\({}_{2}\) to starch from beginning to end in the laboratory, which is a major disruptive and original breakthrough in the field of artificial starch synthesis [135]. [PERSON] et al. constructed a biosynthetic pathway of CO\({}_{2}\) to organic matter by using Rhodobacter sphaeroides strains [136]. [PERSON] et al. proposed a new \"genomic scalpel\" that can quickly prune and knock out the genome of microalgae named \"Nannochloropsis oceanica\" to form an efficient cell factory, which can be combined with available CO\({}_{2}\) and sunlight to customize the production of biomolecules such as biofuels or bioplastics [137]. The continuous development of CO\({}_{2}\) biological utilization technology lowers the price of processing raw materials while simultaneously improving environmental friendliness, which is consistent with the concept of green ecology and accelerates the transition toward carbon neutrality.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 4 Improved Algorithms
### Nonlinear Error Corrections by Using a BP Neural Network Model
If the model is successfully trained, the neural network corrections will be added to the results of the first step prediction. Otherwise, the BP result are not added to the model, and only the initial deviation correction is amended. Figure 10: The clock offset forecasting process. Figure 9: The fitting residuals by the BPNN as shown in **(a)** GEO satellite fitting residuals; **(b)** IGSO satellite fitting residuals; and **(c)** MEO satellite fitting residuals. Using the sequential adjustment method with the fixed length, Equation (12) can be processed for real-time clock fitting and predicting [14; 15; 16; 26]. We use 48 h as the fixed length for fitting and 24 h as the fixed length for predicting.
## 4 Simulation and Discussion
### PDF of the UWOC Channel  
In dynamic scenarios containing temporal coherence information, 288 realizations of Monte Carlo simulations are conducted for each group of channel parameters. The received light intensity of each realization is obtained to draw the PDF. Using the values in Table 1, the light intensity PDF is shown in Figure 7. We tested the fitting degree of the simulated  
Figure 5: Intensity distributions of the received spots in **(a)** the vacuum, **(b)** the water with turbulence only, **(c)** the water with attenuation only, and **(d)** the water with attenuation and turbulence simultaneously.  
PDF and various common distributions, including normal, lognormal, logistic, and Weibull distributions. Their similarity values (as calculated via the MATLAB fitting tool) are 746.9, 747.1, 742.6, and 724.5, respectively. Accordingly, lognormal distribution with the highest similarity is selected to fit the received light intensity PDFs in subsequent simulations.  
Figure 8 shows the effects of the three turbulence properties on the UWOC channel. A more dispersed distribution means more severe light fluctuation in time domain and stronger turbulence. These results are consistent with the previous conclusion gleaned from Figure 6 regarding the turbulence properties in space domain.  
Figure 9 shows the influence of flow velocity, absorption and scattering, and transmission distance on the UWOC channel. In view of the almost overlapping PDFs with \(\
u=1,\ 2\), and 4 in Figure 9a, the velocity of the water flow has no effect on laser transmission when all the parameters are within the predetermined range. However, the aggravation of absorption and scattering effects in Figure 9b or the extension of distance in Figure 9c will significantly reduce the mean value of the PDF, and the reduction is positively correlated with the distance and coefficients.  
Figure 8: Simulated PDFs with different turbulence values of (**a**) \(\varepsilon\), (**b**) \(\chi_{\text{T}}\), and (**c**) \(\omega\).  
Figure 7: PDF with various fitting distributions.  
Figure 9: Simulated PDFs with different (**a**) flow velocities, (**b**) absorption and scattering coefficients, and (**c**) transmission distances.