esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.2.3 ESA-CCI Soil Moisture  
The European Space Agency Climate Change Initiative (ESA-CCI) soil moisture dataset was used in the assimilation experiment. This dataset combines observations from active/passive microwave remote sensing products and generates a spatial/temporal continuous daily surface soil moisture product with original spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)[60; 61]. Since CCDAS is run at the 8\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) grid in this study for efficiency in computing, we re-gridded the soil moisture data to the model resolution. To keep as much useful information from the soil moisture product as possible, we used soil moisture derived from the '_prior_' simulation experiment to correct the inter-annual variability of the ESA-CCI product. The goals of this bias correction method were to (1) eliminate the requirement for precise texture data and (2) preserve as much pertinent data from the ESA-CCI measurement as is practical. Details can be seen in [22].
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 4 Small-Molecule Inhibitors Targeting Early Endosome-to-Golgi Transport of STx, STx1, and STx2
### Retro-2 Substances  
Retro compounds emerged from a high-throughput screen for small molecules that protect cells from ricin and STx toxins in 2010 (Figure 5) [18]. Two identified compounds, retro-1 and retro-2, provide 10 to 100-fold protection against STx1 and STx2 in HeLa cells, and block the early endosome-to-Golgi transport of STxB/STx1B when used at a concentration of 20 \(\upmu\)M [18]. Initial studies revealed that both retro-1 and retro-2 relocalize the SNARE protein syntaxin 5 from its normal site of accumulation on perinuclear Golgi membranes [18]. A more detailed mechanism was recently provided with the discovery that retro-2 targets the endoplasmic reticulum exit site component Sec16A and affects the anterograde transport of syntaxin 5 from the endoplasmic reticulum to the Golgi [27]. An alternative mechanism was also proposed whereby retro-2 enhances syntaxin 5 degradation and alters its subcellular localization by blocking its delivery to the endoplasmic reticulum targeting factor TRC40, which is required for the insertion of syntaxin 5 into the endoplasmic reticulum [69]. Structure-activity studies to improve the potency of retro analogues were performed, and the best in class molecule is (_S_) retro-2.1 (Figure 5), which has an median effective concentration (EC\({}_{50}\)) of 54 nM against STx in HeLa cells [70; 71]. In animal studies, retro-2 protects mice against toxicity induced by the plant toxin ricin, which also traffics into cells via the retrograde pathway [18]. Furthermore, a cyclized form of retro-2, termed retro-2\({}^{\text{cycl}}\), reduces morbidity and mortality in mice infected with an STEC O104: H4 that produces STx2 when used at a dose of 100 mg/kg [72].  
Retro compounds are reported to have a selective inhibitory effect on the trafficking of exogenous cargo like STx, but lack effects on compartment morphology, endogenous retrograde cargos, or other intracellular trafficking pathways [18; 27]. This is surprising given the ubiquitous role of syntaxin 5 in membrane trafficking. The effect of retro compounds on syntaxin 5 (and now Sec16a) raises the possibility of pleotropic effects on membrane trafficking in vivo, and it highlights the need for good lab practice animal toxicology studies to determine their safety profile, before phase I trials in humans can be contemplated.
# Analysis of Cost Functions for Reinforcement Learning of Reaching Tasks in Humanoid Robots
## 2 Stable Motion Imitation Framework
### Kinematic Motion Mapping  
We use 3D vision and a human body tracker to capture human movements in real time. The first step in the proposed approach is to map the observed human motion onto the humanoid robot's kinematic structure. The processing of the captured data involves resolving issues such as the correspondence problem and managing noisy sensory data. The captured movements may exceed the robot's joint limits, potentially harming the robot if the motion is transferred unaltered.  
Our mapping method comprises two key components. Firstly, the joint angle motion of a target humanoid robot is derived from the measurements computed by the body tracker. Secondly, the resulting joint angle trajectories are passed through a safety check function that prevent the execution of motions that violate the robot's joint angle limits. In addition, motions that exceed the velocity limits are slowed down. This ensures that the transferred motion is safe for the execution by the humanoid robot.  
Human body trackers generate a stream of positions and orientations of the observed body segments of a human demonstrator. These measurements should be mapped to humanoid robot joint angles. This is achieved by computing the relative orientation of two consecutive body segments with respect to each other. For example, consider the calculation of the joint angles for the humanoid robot shoulder at each measurement time. We start by computing the relative orientation between the body's trunk and the upper arm segment. The corresponding robot joint angles are then calculated using the combination of Euler angles that matches the kinematics of the target humanoid robot's shoulder. Once the joint angles are calculated, we apply a linear Kalman filter to smooth the computed joint angle trajectories.  
The safety check function first moderates the speed of movement by limiting the velocities that exceed a predefined threshold. This way we prevent fast movements that can be dangerous for the robot. In addition, once any given joint angle approaches the corresponding joint angle limit, we gradually decrease the velocity of joint motion, if necessary coming to a full stop when the desired joint angle is very close to the joint limit.  
Please refer to [12; 14] for more details about our implementation of kinematic motion mapping.
## 3 Experimental Analysis
### Comparison of Prediction Results and Methods  
The prediction results of each component in Section 3.4 are combined to obtain the final result of load prediction. Figure 11 presents the prediction results of the test set load data for the first two weeks (336 h).  
To validate the superiority of the proposed combined method, it is compared with multiple prediction methods through experimental comparisons. Initially, the proposed combined method is compared with naive forecasting methods without decomposition, including CNN, LSTM, and GPR. The parameter configurations for each prediction method align with those of the proposed combined method, while the partitioning of the dataset into training and testing sets remains consistent. Figure 12 shows the comparison of the predicted results with naive forecasting methods for the load data for the first three days (72 h) of the testing set. The proposed combined method is depicted in Figure 12, demonstrating a significantly superior level of proximity between the predicted blue curve and the actual red curve compared to other forecasting methods.  
Figure 11: Predicted results of the proposed combined method for the previous 336 h.  
Figure 10: MAPE values of residual component at each timestamp for the previous 336 h.  
To verify the superiority of the proposed combined method in using different prediction methods for different components of STL, the proposed combined method is compared with three other methods that use the same model for predicting the components obtained from STL, namely STL-LSTM, STL-CNN, and STL-GPR. The parameter settings for each prediction method are consistent with those used in the combined prediction method, and the division of the training and testing sets is also consistent. Figure 13 displays the comparison of the prediction results of the load data for the first three days (72 h) of the testing set under each prediction method. It can be observed that all methods in the figure can effectively predict the load, but the predicted values of the proposed combined method are closer to the actual values compared to the other methods.  
To provide a more intuitive comparison of the results and further validate the superiority of the combined prediction method proposed in this paper, the MAPE, MAE, and RMSE evaluation indicators were used to compare and analyze the various methods mentioned in Figures 12 and 13. The evaluation indicator values for the prediction results of each  
Figure 12: Comparison of predicted results with naive forecasting methods.  
Figure 13: Comparison of predicted results for other methods with STL.  
method are shown in Table 1. A comparison of the three evaluation indicators in Table 1 reveals that the proposed combined method exhibits lower values for all three evaluation indicators compared to the other methods, indicating higher prediction accuracy.
## 2 Modeling and Research Methods
### Modeling the CATN  
A temporal network refers to a system in which clear time evolution and temporal characteristics are evident [22; 23]. In this system, nodes may emerge or vanish during the process of temporal changes, and interactions among nodes may appear or disappear with the progress of time. This study proposed an aggregated network \(G=(V_{G},E_{G},T)\) to represent the temporal network. In this method, \(V_{G}\) defines a set of nodes, and \(E_{G}\) denotes a set of edges interacting within a finite time period of \([0,T]\). Each edge is represented with a quadruplet \(e=(i,j,t,\Delta t)\), where \(i\) and \(j\) serve as node identifiers, \(t\) stands for the time of interaction, and \(\Delta t\) signifies the duration of connectivity. Although the implementation of static network visualization for the temporal network may yield improved visualization results, this approach does not fully incorporate topological and temporal information.  
[PERSON] [24] suggested the use of time windows to transform interactions that take place during the same time window into a static graph. A snapshot-based temporal graph [25] can map dynamic temporal aggregation networks (\(G\)) to a time sequence of static graphs (\(G_{1},G_{2},\ldots,G_{n}\)). This method assists us in processing temporal information contained in the network's quadruplets. By defining time windows symbolized by \(\omega\), a temporal network with \(N=|V_{G}|\) nodes and duration \(T\) can be efficiently divided into \(L=\frac{T}{\omega}\) snapshots. In this scenario, \(L\) signifies the number of snapshots obtained after the division. Each snapshot captures either all the events that occur within a specific time window or events that occur at a precise moment in time, with the collected information that characterizes these events.  
In the framework of the CATN, a temporal network can be established by utilizing a triplet \(e=(i,j,t)\) based on flight schedules. In this notation, \(i\) and \(j\) denote the departure and arrival airports of the flight, respectively, while \(t\) indicates its departure time. Figure 1a shows a snapshot of the aggregation network, which consists of five airports and flights. Time information indicates that the departure time at \(t\) is available on the edges. By utilizing a time window of \(\omega=1\) or \(\omega=2\), one can generate temporal snapshot sequences (see Figure 1b,c).
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 5 Numerical and Simulation Results  
In this section, to assess the performance of algorithms which we proposed, we firstly introduce an evaluation index, the \"contention\" of a SN. Then, we illustrate contention by comparing the contentions produced by a GW placed by the WBG and by two manual placements, for a simple scenario of three SNs and one GW. Next, we compare the performances of the WBG and PGL algorithms for different numbers of SNs and GWs.
## 3 Results
### Fractal Characteristics
The fractal dimension value reflects the structure and spatial development characteristics of micropores in porous media [33]. For porous media, the fractal dimension value varies from 2 to 3. Value 2 represents an absolutely smooth surface, and if it is closer to 3, the pore structure will be more complex with stronger heterogeneity characteristics. By comparing the fractal characteristics and dimension values of coal, shale, and tight sandstone samples, the following results can be drawn. All the samples are obviously fractal, but the tight sandstone samples (average correlation coefficient of 0.99248) have a better linear relationship than shale (0.9819) and coal (0.96708). The shale samples (average D\({}_{3}\) of 2.79592) have stronger fractal characteristics than coal (2.62244) and tight sandstone (2.68544), indicating that pores in shale are more complex in structure, type, and spatial development characteristics than the other two. Additionally, the higher D values indicate that the heterogeneity characteristics and the complexity of pore structure of shale are stronger. Pores in shale are developed in or between the complex material compositions of various clay minerals, clastic minerals, and OM. Pores in high-rank coal have a predominant type of OM pores. Tight sandstone does not contain OM, and pores and throats are controlled by inorganic minerals. Therefore, the pore networks in shale are more complex and more irregular than in high-rank coal and tight sandstone. The complex pore types and structure are also observed in FESEM images with good consistency with the fractal modeling results, which gives direct proof of the complex types, proportions, structure, pore composition, and spatial distribution of different reservoirs. Overall, the test samples are significantly fractal within the different pore size ranges. From the processing results of mercury intrusion data, the slope of the fitting curve is  
Figure 13: FHH fractal models of shale, coal, and tight sandstone samples using LP-N\({}_{2}\)GA data. (a) Shale samples. (b) Coal samples. (c) Tight sandstone samples. different between the relatively low-pressure section and the higher-pressure section. The fractal dimensions obtained by the FHH model based on low-temperature nitrogen adsorption data are also different from those obtained by mercury intrusion. Therefore, although pores developed within different pore size ranges have fractal characteristics, their corresponding fractal dimension values are different. The fractal dimension can represent the irregularity of the sample surface or the complexity of the pore structure. The larger the fractal dimension, the rougher the surface for adsorption pores. For seepage pores, the larger the fractal dimension, the more complex the pore structure of the sample and the stronger the heterogeneity. Thus, the non-intuitive reflection of fractal dimension on pore surface structure and pore spatial structure can be used to establish evaluation schemes for pore complexity and heterogeneity.
## 3 Results  
### Simulations without RL  
Figure 2 shows histograms obtained by simulating the actions of various sets of user agents given all possible sequences of signals by the governing agent. In these simulations, no RL algorithm was used for the governing agent. Rather, all binary sequences of length 16 were generated and tried on the user agents. As such, the results of these simulations depict the space of outcomes of all possible policies. Each sequence of decisions was tried on 100 sets of user agents with similar characteristics. Therefore, each histogram shows the distribution of scores of 6,553,600 episodes. The score of each episode is calculated as the mean reward per time-step of that episode.  
Figure 2: Histograms of overall scores of episodes in reference dataset, ordered by number and mean decision threshold of user agents. Each histogram summarizes 6,553,600 data points. Each data point is an episode of 17 time-steps. The score of each episode is its mean reward per time-step. Rewards are real numbers between \(-1\) and \(0\), and they are calculated based on the ratio of user agents that cooperate with the governing agent in each time-step.  
As shown in Figure 2, the emergence of the desired norm of behavior is highly dependent on the mean cost-benefit decision threshold of the user agents. In simple terms, the more costly the behavior, the less likely it is to become a trend in the society. This is especially evident in the simulations with a mean decision threshold of 0.7 for the user agents, where nearly all episodes ended with the minimum score, and cooperation of the user agents with the governing agent was a rarity. To a lesser extent, this happened in the simulations with a mean decision threshold of 0.5 as well. The histograms of these runs show lower peaks and more dispersed distributions of scores, though their modes are still at the minimum score. On the other hand, in the simulations with a mean decision threshold of 0.3, the scores are distributed more evenly. In two of the three histograms of these simulations, the mode is not at the minimum score. In fact, these histograms show that the number of user agents has an inverse effect on the mode of scores.
## 3 Results  
### Spatio-Temporal Distributions of Global NEP IAV  
According to the BEPS model simulation results, the global NEP showed an uptrend of 0.04 Pg C yr\({}^{-2}\) and large interannual variability during 1982-2016 (Figure 3a). The mean annual NEP was 2.6 Pg C yr\({}^{-1}\), which indicated that the global terrestrial ecosystem was a carbon sink during the study period. The NEP detrended anomalies ranged from \(-2.32\) Pg C in 1998 to 1.62 Pg C in 2013, with the mean annual absolute difference being 0.9 Pg C yr\({}^{-1}\) (Figure 3a).  
The spatial pattern of the mean annual NEP showed obvious geographical heterogeneity (Figure 3b). The tropical region, especially in EBF area, was the largest carbon sink, with annual mean NEP values larger than 50 g C m\({}^{-2}\) yr\({}^{-1}\). At high latitudes of the Northern Hemisphere, the NEP value was larger than 10 g C m\({}^{-2}\) yr\({}^{-1}\) and smaller than 25 g C m\({}^{-2}\) yr\({}^{-1}\). However, in most areas of Oceania and some areas of the southwest, the NEP values were smaller than 10 g C m\({}^{-2}\) yr\({}^{-1}\) (Figure 3b).  
The spatial pattern of the absolute NEP differences (Figure 4a) was similar to the spatial pattern of the annual mean NEP (Figure 3b) across the globe during the period from 1982 to 2016. Mean absolute annual NEP difference ranged from 0 to 150 g C m\({}^{-2}\) yr\({}^{-1}\). The larger absolute NEP differences occurred in some regions of southeast North America, Europe, and South America, with values larger than 70 g C m\({}^{-2}\) yr\({}^{-1}\). The absolute NEP differences were larger than 50 g C m\({}^{-2}\) yr\({}^{-1}\) in the EBF of the tropical regions. Furthermore, a small absolute NEP difference occurred in Oceania, most areas of Africa, and southwestern North America, with the mean difference less than 30 g C m\({}^{-2}\) yr\({}^{-1}\). These results suggest that ecosystems with larger productivity had larger NEP IAV globally.  
We compared the different vegetation types on the absolute NEP difference (Figure 4b). The results indicated that the forests, including ENF, EBF, DBF, and MF have a similar mean absolute NEP difference, with the absolute mean difference being around 76.8 g C m\({}^{-2}\) yr\({}^{-1}\). This was followed by CRO, for which the mean absolute NEP difference was 62.8 g C m\({}^{-2}\) yr\({}^{-1}\). The mean absolute NEP difference in SHR was the smallest, with the value being less than 15.2 g C m\({}^{-2}\) yr\({}^{-1}\). The results indicate that forests have larger interannual variabilities compared to other vegetation types on a global scale.  
Figure 4: The spatial pattern of (**a**) the mean absolute NEP difference and (**b**) counted by different vegetation types during the study period.  
Figure 3: (**a**) Time series of NEP and NEP detrended anomalies, (**b**) the spatial distribution of annual mean NEP during 1982–2016.
## 3 Results and Discussion  
### Extraction Experiments  
The three ILs utilized in this study have been previously tested for their capability to extract the heavy metals Cu, Ag, Cd and Pb from pure water as well as from several synthetic and natural waters, including drinking water and seawater. High extraction efficacies have been described at mg L\({}^{-1}\) levels in liquid-liquid as well as in a solvent bar micro-extraction setup [28; 34]. The extraction of Hg had not been tested before.
## 2 Related Works  
PF using TDOA measurements have been widely studied, but the processing of noise in the algorithm and noise environment have been emphasized rather than high speed processing [4; 5]. Also, a target estimation method using TDOA underwater was studied [6]. In this paper, we focus on providing algorithm implementation and verification by approaching GPU from acceleration using parallel processing and estimating the sudden movement of the target using multiple system state equations.  
As a method for measuring TDOA, various methods using the GCC algorithm have been introduced [7; 8]. They have relative advantage for real-time applications in which performance is more important. The significant approach based steered response power-phase transform (SRP-PHAT) introduces an effective approach for the robust signal processing in sound localization [9], and GPU-based acceleration is also proposed in TDOA measurement using SRP-PHAT [10]. Our paper is based on initial approach [11], which using the GCC-PHAT-based TDOA measurement, specially presenting our experience in implementing GPU-based acceleration approach to guarantee real-time performance. In addition, the GPU-based acceleration for TDOA measurement value in specific number of sensors [12] has been presented case study.  
Various studies on the GPU-based PF [13; 14] and parallelization of multi-target tracking have been presented. The scheduling algorithm and processor selection in paralleling PF processing is introduced [15]. A significant approach [16] also introduce the parallelization approach of the memetic algorithm and PF for tracking multi objects. Compare to these approaches, we additionally consider the uncertain model of target system state, in case of suddenly abnormal target movement. So, we adopted multiple state-space models which are selectively applied to the PF processing, to determine the good enough estimation result. This requires the more computation resource or increase the calculation time, so that we had to accelerate the computation in PF processing by allocating them into independent calculation unit block in GPU.  
There are many studies of SMC methods in using uncertain state-space models [17]. Some approaches, which adopt multiple state-space models to overcome weakness in PF processing for the uncertain model, are introduced [18; 19]. These studies consider the PF processing using multiple state-space models in single system. Our approach is slightly comparable because we try to allocate the individual kernels for PF processing into multiple processing unit in GPU. This enables to apply the specific state-space model to the PF processing kernel independently, so that we could get scalability using the proposed architecture in case of tracking large-scaled moving target, which is more important in IoT-driven applications. Some target localization studies for IoT-driven applications using radio-frequency identification (RFID) tags including PF processing have already been studied [20; 21; 22]. Therefore, this study aims at implementation of scalable GPU-based algorithm which can be used in IoT sensing applications.