esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Methodology
### Functional Block Partitioning  
As mentioned in Section 4, we analyze the algorithm based on the instruction and operation order, as in [39]. We then divide the pipeline into functional blocks as summarized in Figure 5. The first functional block, dedicated to the _Particle Initialization_ step, initializes the particle poses with random numbers and the landmarks as null values. The output of the _Particle Initialization_ block constitutes the input of the _Particle Prediction_ functional block, together with the inertial sensor signals from the simulator. This functional block is supposed to run only at the first iteration of the algorithm. The predicted poses and the unlabeled observations retrieved from the simulator are the input of the _Data Association_ functional block. The associated observations outputted by the _Data Association_ block and the pose prediction are fed to the _Proposal Adjustment_ block, which outputs the updated robot pose estimate. The output of this functional block and the associated observations are then fed to the _Landmarks Update and Weights Computation_ block. The corrected landmark estimates, the corrected particle pose, and the newly assigned weights feed the _Resampling_ functional block, which is responsible for copying the selected particles to the new particle set and re-initializing the particle weights. The output of the _Resampling_ blocks, together with the new signals coming from the inertial sensors, will constitute the input for the _Particle Prediction_ and _Data Association_ functional blocks for the next iteration.  
All the functional blocks are composed of a single kernel except for the _Data Association_ functional block, which is subdivided into multiple kernels. Further implementation details are discussed in Section 5.  
Figure 4: Simulation environment schema.
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Data Analysis
#### 4.1.3 Evaluation Metrics  
In assessing the performance of our product demand prediction models, we employ three key evaluation metrics that offer a comprehensive view of model accuracy and fit. These metrics are essential for quantifying the discrepancy between the actual demand values and the predictions made by our models.  
* Mean Absolute Error (MAE): MAE is a straightforward metric that calculates the average absolute difference between the actual demand \(y_{i}\) and the predicted demand \(\hat{y}_{i}\) across all observations. It provides an intuitive measure of the model's accuracy, with lower values indicating better performance. The MAE is defined as \[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}|,\] (5) where \(N\) is the total number of observations. This metric is particularly useful for understanding the magnitude of prediction errors without considering their direction.
* Root Mean Squared Error (RMSE): This metric offers a more sensitive measure of model accuracy by squaring the errors before averaging, thus giving greater weight to larger errors. RMSE is defined as the square root of the average of squared differences between the predicted and actual values: \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}},\] (6) where \(N\) represents the total number of observations. The RMSE is beneficial for identifying when a model might be prone to producing significant errors, as it penalizes larger discrepancies more heavily than smaller ones.
* R-squared (R2): R-squared, also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It indicates how well the regression predictions approximate the real data points. An R-squared of 1 indicates that the regression predictions perfectly fit the data. The formula for R-squared is \[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_{ i}-\hat{y})^{2}}\] (7) where \(\tilde{y}\) is the mean of the actual demand values. R-squared is a relative measure of fit that can be useful when comparing different models' abilities to explain the variability in the data.
## 6 Discussion
### Limitations and Future Directions  
While this study demonstrates the effectiveness of satellite-based methane monitoring, several limitations should be acknowledged. The spectral resolution of Sentinel-2, while sufficient for detecting large methane plumes, may not be adequate for identifying smaller or less intense emissions. Additionally, the heterogeneity of surface conditions, particularly in regions like Changzhi, can pose challenges for automatic plume detection and quantification. Future advancements in satellite technology, such as the upcoming CHIME and Carbon Mapper missions, are expected to improve the revisit frequency and spectral resolution, further enhancing our ability to monitor methane emissions globally. Moreover, the integration of machine learning algorithms could improve the accuracy and efficiency of plume detection and quantification, particularly in complex environments.  
In conclusion, this study underscores the value of combining multispectral and hyperspectral satellite data for the detection and quantification of methane super-emitters. The proposed tiered observation approach, which leverages the strengths of different satellite platforms, offers a scalable and cost-effective solution for global methane monitoring. As satellite technology continues to advance, the integration of these tools with targeted ground-based observations and policy frameworks will be crucial for achieving significant reductions in global methane emissions.
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 4 Gene Editing with CRISPR/Cas in Thyroid Cancer Research  
The thyroid cancer field has further developed by using gene editing with CRISPR/Cas due to the relative straight-forwardness of the methodology compared to other gene-editing platforms such as Zinc-fingers nucleases or TALEN [1]. One of the cornerstones to investigate a gene function is to blunt its expression. Unlike RNA interference, which requires the expression of short-interfering segment to maintain gene knockdown, the gene editing alters the DNA and leads to a stable modification of genome. Consequently, when this segment is transcribed it contains changes in RNA sequence that may modify the coding sequence of a mRNA, or the structure of a noncoding RNA.  
In this section, we discuss studies that used CRISPR/Cas to target coding and non-coding genes for the investigation of thyroid cancer biology. It is important to note that although we tried to organize the studies according to the target gene function or signaling pathway, most of the results cross-talk with each other.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 2 Relevant Work
### Machine Learning-Based Deployment Method  
Machine learning (ML)-based deployment methods primarily rely on data-driven training to iteratively optimize strategies in self-play. The core idea of ML lies in continuous trial and error within an environment, refining behavioral policies based on acquired rewards without relying on explicit models or prior knowledge. Traditional RL methods model problems through Q value tables, but these are limited to small-scale, simple scenarios. By integrating deep neural networks, agents can iteratively optimize policies in simulated environments, making them highly suitable for tackling complex, dynamically changing problems with challenging mathematical formulations. In recent years, RL-based methods have been widely applied to fields such as board games, UAV deployment, spatial planning, and building layout design, as summarized in Table 1.  
In summary, ML-based methods have demonstrated significant potential in addressing challenges within complex, dynamic, and uncertain environments. However, their application to emergency communication network deployment remains limited and lacks in-depth exploration.  
Our contributions are primarily reflected in the following aspects:  
* We comprehensively consider the impacts of various environmental factors on different hub elements, quantify them, and establish an evaluation reward function.
* We propose a deep reinforcement learning model based on a fully convolutional network (FCN). By adopting an FCN as the value network, the model effectively expands the agent's action space while preserving spatial topological features.
* Experimental results demonstrate that our method significantly improves the overall deployment effectiveness of emergency communication networks compared to existing approaches.
## 2 Materials and Methods
### Infections and Viral RNA Quantitation  
Infectious work with EBOV and MARV was performed in the BSL4 facility at the Robert Koch Institute (Berlin, Germany), according to standard operating protocols (SOPs). Cells were seeded in 6-well plates and inoculated in triplicates with EBOV (strain Makona, C05; adaptation: 2 passages on MoKi cells) at 3 \(\times\) 10\({}^{5}\) TCID\({}_{50}\)/well, EBOV-GFP with MOI 0.1 or MARV (strain Musoke) at 10\({}^{4}\) TCID\({}_{50}\)/well for 1 h. Cells were then washed twice with PBS, after which 3 mL cell culture medium was added. For EBOV quantitation, 140 \(\mu\)L supernatant per well was collected in AVL Buffer (19073, Qiagen, Hilden, Germany) after 1, 24, 72, and 96 h, or weekly until 150 dpi for persistently infected cells. Samples were then mixed with an equal volume of 100% ethanol before removal from the BSL4. Viral RNA was extracted using the QIAamp Kit (Qiagen) according to the manufacturer's instructions. Viral RNAs were quantified by qRT-PCR (Applied BiosystemsTM 7500, Waltham, MA, USA) using the AgPath-IDTM One-Step RT-PCR Kit (4387391, Thermo Fisher, Waltham, MA, USA). Primers/probe and cycling conditions were described previously [35]. Viral RNA copy numbers of each sample were calculated from a standard curve, that was produced using EBOV in vitro transcripts (concentrations ranging from 10 to 10\({}^{7}\) copies/\(\mu\)L). Differences in replication rates were calculated as geometric mean of triplicates in lg(x) (viral RNA variation within triplicates did not allow statistical calculations).
## 3 System Model and Problem Formulation
### Problem Formulation  
#### 3.2.1 TLE Data  
According to the recommendations of the third generation Partnership Project Non-Terrestrial Network (3 GPP NTN), the two types of satellite ephemeris consist of SKOE data and data on the position and velocity. SKOE data are more widely exploited, particularly in the TLE format in system information block 19 (SIB19) [21; 22]. SKOE data include the mean motion \(n\), mean inclination \(i\), mean eccentricity \(e\), mean anomaly \(M\), argument of periapsis \(\omega\), and the mean longitude of the ascending node \(\Omega\).  
The TLE of the SKOE's describes the characteristics of the satellite's ephemeris in the form of two lines with 70 columns each. The first line records basic information such as the satellite's name and launch time. The second line records the parameters of the satellite's operational orbit, which play the main role in orbit extrapolation. The details of the second line of the TLE table are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence Number** & **Digit Positions** & **Data Meaning** \\ \hline
1 & 01–01 & Line number \\
2 & 03–07 & Satellite number \\
3 & 09–16 & Inclination \\
4 & 18–25 & Mean longitude of the ascending node \\
5 & 27–33 & Eccentricity \\
6 & 35–42 & Argument of periapsis \\
7 & 44–51 & Mean anomaly \\ \hline \hline \end{tabular}
\end{table}
Table 1: The second line of TLE data.  
Figure 3: The flowchart of the proposed method.  
As Table 1 shows, the TLE data reflect the mean orbital parameters using the true equator and mean equinox (TEME) expression, filtering out short-term perturbations.  
Figure 4 illustrates the parameters of the TLE data. The SGP4 algorithm uses the TLE data to calculate the SKOEs to the position \(\mathbf{r}\) and velocity \(\dot{\mathbf{r}}\) of the satellite in the TEME coordinate system [23; 24]. Here, the TLE ephemeris in the TEME coordinate system should convert the Earth-centered Earth-fixed (ECEF) coordinate system into X, Y, and \(Z\) coordinates as a function of time. The conversion formulas are as follows:  
\[\begin{pmatrix}X\\ Y\\ Z\end{pmatrix}_{TOD}=R_{z}(\Delta\varphi\cos\epsilon)\begin{pmatrix}X\\ Y\\ Z\end{pmatrix} \tag{4}\]  
where \(R_{z}(\Delta\varphi\cos\epsilon)\) is the rotation matrix used to convert the position vector from the TEME coordinate system to the ECEF coordinate system. \(R_{z}\) denotes the rotation matrix around the z-axis, \(\Delta\varphi\) denotes the Earth's rotation angle due to the change in time, and \(\cos\epsilon\) represents the cosine of the angle between the equatorial plane and the ecliptic plane.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 3 System Applications
### D Imaging with I-COACH  
As mentioned in Section 2.1.2, I-COACH is capable of recording 3D objects and reconstructing the 3D image, without two-wave interference. In this subsection, we describe the experiment carried out in [54] as a typical example of 3D holographic imaging by I-COACH. The experimental I-COACH setup for imaging 3D reflective objects is shown in Figure 21. In channel-1, a pinhole with a diameter of 25 \(\upmu\)m was used. The light emitted from the object passes through a beam splitter BS\({}_{1}\) and is collimated by a lens \(L_{2}\) with a focal length of \(f_{0}\) = 20 cm. The collimated light is polarized by \(P_{1}\) along the active axis of the SLM and modulated by the CPM displayed on the SLM mounted at 20 cm from the lens \(L_{2}\). The intensity patterns are recorded by a digital camera mounted at \(Z_{h}\) = 40 cm from the SLM. In channel-1, the pinhole is shifted to various axial locations and a PSH library is created. In channel-2, an object is critically illuminated by an illumination system using the same beam splitter BS\({}_{1}\). Three different objects, namely, a LED, two one-cent coins, and stapler pins were selected for the study. Three intensity patterns were recorded for each object and averaged with 20 such sets of CPMs. Cross-correlations of the synthesized complex holograms with the PSH library extracted the different planes of the objects. In this case, we did not use the phase-only filtering in order to reconstruct the above objects with greyscale intensity values. The reconstruction results of I-COACH for the three objects after averaging are compared with direct imaging as shown in Figure 22a-c, respectively. The reconstruction results of I-COACH reveal that the performance of averaged I-COACH is close to that of direct imaging.
## 3 Results
### Analysis of Model Structure  
On sunny days, among the seven LUE models, the ratio of GPP that was simulated only by considering the water stress scalar in models (\(\mathrm{GPP_{wat}}\)) (i.e., \(\mathrm{PAR}\times\mathrm{FPAR}\times\mathrm{LUE_{max}}\times\mathrm{Ws}\)) to the potential GPP (PGPP) (i.e., \(\mathrm{PAR}\times\mathrm{FPAR}\times\mathrm{LUE_{max}}\)) varied from 0.19 to 0.56, with an average value of 0.43; on cloudy days, the ratio varied from 0.29 to 0.77, with an average value of 0.61 (Figure 5a). In addition, the ratio of GPP that was simulated by only considered the temperature stress scalar in the seven LUE models (\(\mathrm{GPP_{tem}}\)) (i.e., \(\mathrm{PAR}\times\mathrm{FPAR}\times\mathrm{LUE_{max}}\times\mathrm{Ts}\)) to PGPP varied from 0.66 to 1.00 on sunny days and 0.81 to 1.00 on cloudy days, and the average value was 0.80 and 0.88, respectively (Figure 5b). The differences between \(\mathrm{GPP_{wat}}/\mathrm{PGPP}\) and \(\mathrm{GPP_{tem}}/\mathrm{PGPP}\) illustrate the contribution of environmental regulation scalars to the differences in the GPP simulations. The results further suggest that, in this study area, the differences in performance between LUE models were due more to the response of the models to water stress compared with the response of the models to temperature stress.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 4 Methodology  
### CNN Classification with a Custom Loss Function for Aleatoric Uncertainty Estimation  
A typical CNN may receive as input an image patch and map that patch to a single output. For an example of this type of architecture for lake ice classification see [PERSON] et al. [21].  
Figure 3: Examples of image analysis ice charts’ total ice concentration during (**a**) freeze–up period (19 January 2014) and (**b**) break–up period (1 April 2014).  
To learn the uncertainty in the data, the approach taken here is to use a CNN architecture that can predict two items, \(y^{W}_{predict}\) and \(y^{W}_{var}\), with the second one for the uncertainty (represented here as a variance); \(W\) represents the network parameters. To do this for our binary classification problem, we followed [PERSON] and [PERSON] [36] and perturbed the logit produced by the CNN (\(y^{W}_{predict}\)) by a sample drawn from a Gaussian distribution with a mean of zero and variance that is a learned parameter (\(\epsilon\sim N(0,(y^{W}_{var})^{0.5}\)). Each perturbed logit was processed by a sigmoid function (\(P_{i}=sigmoid(y^{W}_{predict}+\epsilon)\)) followed by a binary cross-entropy loss function as  
\[loss=-\Sigma_{i}(1/T\Sigma_{t}(y_{i}log(p_{i,t})+(1-y_{i})log(p_{i,t}))\]  
where \(T\) is the number of Monte Carlo runs (set as 50 in our algorithm) for Gaussian sampling, \(y_{i}\) is the label value of for the \(i\)th sample, and \(p_{i,t}\) is the CNN output for the \(i\)th sample for the \(t\)th Monte Carlo run. The mean value of these binary cross-entropy loss functions was then used to update the network weights and the variance. Note that while the mean value could be compared with the label (supervised learning), the variance was updated through backpropagation (unsupervised learning).  
The learned variance was for the logit, but for the end-user a predicted probability of ice and its representative variance at each lat/lon value was more useful as it tells the user directly about the uncertainty in the desired quantity. The CNN model could predict a logit and a variance of that logit at each lat/lon value. To obtain our desired quantities, we generated a set of ice probabilities by perturbing the logit with a perturbation drawn from a Gaussian distribution with the variance equal to that specified at the given lat/lon value (\(y^{W}_{predict}+N(0,y^{W}_{var})\)). Each perturbed logit was passed through the sigmoid and the mean and variance of these predicted probabilities was computed as predicted probability of ice and its representative variance.
# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 3 Results  
After constructing the network, there are 2145 nodes and 1,411,634 edges with the 2 h duration, and the average and median degrees of the nodes are 1316 and 1480, respectively. Within the 4 h duration, there are 1867 nodes and 1085,636 edges, and the average and median degrees of the nodes are 1162 and 1359, respectively. Within the 24 h duration, there are 1144 nodes and 446,469 edges, and the average and median degrees of the nodes are 780 and 922, respectively. The numbers of nodes, together with the average and median node degrees, gradually decrease as the time duration increases (from 2 h to 4 h and 24 h). The numbers of edges among nodes in the network also decrease accordingly. The sizes of these networks basically agree with our common understanding that, in general, the number of individuals staying together for short periods is greater than the number of those staying together for longer periods while engaging in social activities or at particular places.  
The top row of Figure 5 below show the changes in inertia values against different clustering numbers for the 2 h, 4 h, and 24 h scenarios, from left to right. The three figures in the bottom row show the changes in silhouette score. It can be observed that the suitable number of clusters for the 2 h duration is 24, and that for the 4 h scenario is 18, while for the 24 h scenario, the suitable number is 9.
## 1 Introduction
### Correlation between the Evaluation Scores and the Adjustment Levels  
The feature values for colorfulness, contrast, and sharpness used in the CQE method can effectively represent the levels of each adjustment. As a result, the calculated quality scores obtained by linearly combining these values may represent the overall levels of the image adjustments. However, the subjective evaluation scores for these adjusted images may be different from the quality scores calculated by CQE because HVS perceives images differently than the calculated features and quality scores used in CQE do. As such, we conducted experiments to investigate the correlation between the subjective and calculated scores and the level of image adjustments.  
Eight levels of the colorfulness, contrast, and sharpness adjustments were applied to 24 images in the TID2013 database [35], which resulted in 576 adjusted images. The color saturation method [36], linear contrast adjustment [37], and unsharp masking method [3] were used for colorfulness, contrast, and sharpness adjustments, respectively. Eight observers participated in subjective evaluations for these adjusted images and rated the image qualities from 1 to 5 with 1 representing the worst quality and 5 the best. Lastly, we calculated mean opinion scores [38] from these subjective scores.  
Figure 1 shows the mean scores of the subjective scores and the calculated feature values used in a previous assessment method, CQE [30], along with the levels of image adjustment. While the calculated feature values keep increasing as the level of adjustment increases, the subjective scores increase only until the level of adjustment of about four or five levels and then they decrease thereafter until the eighth adjustment level. Figure 1 shows that the calculated feature values and the subjective  
scores have different tendencies depending on the level of adjustment. Generally, the subjective scoring is considered the gold standard for image assessment methods because human perception is the ultimate image receiver. This suggests that excessive adjustment of an image can have adverse impact on the perception of HVS for the image. Therefore, in this study, we propose a new fully automatic assessment method that can consider the relationships between the adjustment levels and the visual preference of HVS. Our assessment algorithm utilizes the features of HDR images, which are better matched with HVS preferences than LDR images. The following Section 2.2 demonstrates that HDR images receive higher subjective evaluation scores than LDR images.