esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Preparation of the CO\({}_{2}\) Sorbent and Analysis  
The aluminum oxide (Sigma Aldrich) and zirconium oxide (Sigma Aldrich) sorbent impregnated with K\({}_{2}\)CO\({}_{3}\) (Sigma Aldrich) in this study was prepared by the impregnation method. The preparation procedure was as follows: Al\({}_{2}\)O\({}_{3}\) and ZrO\({}_{2}\) supports were added to aqueous solutions of K\({}_{2}\)CO\({}_{3}\) from 10 to 40 weight % in de-ionized water. Then, the solutions were mixed with a magnetic stirrer for 24 h at room temperature. After stirring, the mixed samples were dried in a vacuum rotary evaporator [5; 19; 20; 21; 22]. These samples were calcined in an air furnace for 5 h at 500 \({}^{\circ}\)C. The calcination temperature was raised to 500 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and then ground and sieved to produce a particle size range of 150-250 \(\upmu\)m in diameter. These CO\({}_{2}\) sorbents are denoted as PAI-x and PZlx, where P, A, Z, I, \(x\) represent potassium carbonate, aluminum oxide, zirconium oxide, the impregnation method, and the potassium carbonate loading amount, respectively.  
The changes in the crystalline phase before and after CO\({}_{2}\) sorption were examined by XRD (Philips X'PERT) using Cu K\(\alpha\) radiation at the Korea Basic Science Institute. TGA and DTG (SDT Q600, TA instrument) were conducted to investigate the thermal characteristics after CO\({}_{2}\) sorption. Nitrogen gas was used as the carrier gas, and the CO\({}_{2}\) sorbent was heated from 30 \({}^{\circ}\)C to 500 \({}^{\circ}\)C at a ramping rate of 5 \({}^{\circ}\)C/min. The N\({}_{2}\) adsorption-desorption isotherms at \(-\)196 \({}^{\circ}\)C were recorded using a Micrometric ASAP 2020 instrument to evaluate textural properties such as the BET surface area, pore volume, and pore size. The changes in the properties of the sorbents after CO\({}_{2}\) sorption were investigated by Fourier transform infrared (FTIR, PerkinElmer Inc., Waltham, MA, USA). The TPD experiment was conducted in a nitrogen atmosphere, and the sample was heated from 100 \({}^{\circ}\)C to 450 \({}^{\circ}\)C at a heating rate of 2 \({}^{\circ}\)C/min.
## 4 The Anatomy of Obesity
### Central Obesity and Metabolically Healthy Obesity  
The aforementioned principles can be used to delineate two major types of adipose tissue disposition which start to develop during childhood or adolescence. Central obesity, with the adipose tissue predominantly concentrated around the viscera, is considered to have an increased unfavorable effect on cardiovascular risk [56, 57]. A recent meta-analysis of prospective studies evaluating the link between abdominal obesity and cardiovascular risk has shown a strong association between the parameters describing central obesity (waist circumference, waist:hip ratio, and waist:height ratio) and cardiovascular diseases (including ischemic heart disease, cerebrovascular disease) [58]. The deleterious effect of visceral adipose surplus appears to manifest itself even in otherwise normal-weight individuals (according to BMI values) which show evidence of excess abdominal adiposity (measured by waist circumference for example) [59].  
Central obesity has been shown to be a strong risk factor for cardiometabolic disease, not only in adults but in children and adolescents as well [60, 61, 62, 63, 64, 65, 66]. Moreover, it seems to exhibit a stronger correlation with cardiovascular risk in children when compared to BMI-defined obesity [67, 68]. In addition, the development of central obesity during childhood appears to persist into adolescence and adulthood as well [69, 70, 71].  
Conversely, due to the apparent predominant impact of visceral fat on cardiovascular risk, the existence of a so-called \"metabolically healthy obese\" pattern has been postulated, where most of the weight excess is accounted for by somatic adipose tissue, with relatively reduced visceral fat excess [72]. A large observational study involving 3.5 million individuals, however, has shown an increased risk of incident cardiovascular events in this population compared to individuals with normal weight [73]. With this observation, \"safe obesity\" might in fact represent a form of precursor state that can be found before the manifestation of the metabolic disbalances traditionally associated with obesity.
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 4 The Remarkable Physical Features of Solitons
### Solitons in Pathological EEG Traces
Like solitons, pathological EEG oscillations can travel on the hemispheric surface of the brain over relatively large distances, and, like solitons, neither tend to flatten out nor steepen and topple over. Further, some pathological EEG waves display double spatio-temporal localization with cyclic changes in shape, just as Peregrine solitons. Summarizing, the occurrence in EEG traces of features and shapes corresponding to various types of solitary waves suggests that some pathological brain electric activities might represent the biological counterpart of physical solitons.
## 3 Background
### CSI-Based Attack  
With more and more public places deploying public WiFi, CSI has received much attention [35; 36], and because of the rich information that CSI contains, it can be used to detect micro motions, such as finger motions [5; 6] and mouth motions [8]. Using a commercial receiver, an attacker can obtain a user's PIN, password or other input sensitive information. In this section, we first show why CSI can detect and recover input sensitive information, and then, we will introduce a novel attack, the CSI-based attack.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Search, and Rescue: A Problem of Two Sequential Phases
#### 3.2.1 Phase I (Search): Maze Exploration  
For the purpose of designing a path planning strategy for SAR, our focus is not on finding the absolute shortest path, but rather a good (but possibly sub-optimal) path, and to do so in the shortest time possible. This is to reduce the response time. The path could be further shortened in Phase II (Rescue), while reverse routing in the search phase.  
Our ultimate focus is on the search time, rather than path length. For this purpose, heuristic techniques are favorable due to their element of randomness, which pushes the agents to explore while still partially following an order.  
Therefore, in Phase I, a swarm of robots are injected into the search space. Each robot marks its explored territory in pheromone maps, local copies of which are shared between robots within a communication range. These pheromone map updates signal other robots to avoid already mapped/explored areas, quickening the overall exploration while shortening the search time.  
Figure 4: The indoor search is translated into a maze-solving problem. Just as with the indoor search introduced in Figure 2, the agents enter the maze at a specific start location / point (1), traverse the maze in search of the target (depicting the victim) in Phase I, shown in (2), and then (in Phase II) calculate the shortest and cheapest route amongst the scattered agents (3) to form a relay network.
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 2 Materials and Methods
### Characterization of Catalyst  
Fresh catalysts and used catalysts were collected for morphology and functional group characterization. The used catalyst was collected from the pilot plant after it had been in operation for 65 days. The morphology and microstructure characteristics of the iron mud-based catalyst were observed by scanning electron microscopy (SEM, Horiba Lab RAM HR Evolution spectrometer, HORIBA Jobin Yvon, Kyoto, Japan). The pore structure information of the Fenton iron mud samples was analyzed using a surface area and pore volume analyzer (Quantachrome Instrument, Boynton Beach, FL, USA). The specific surface area, average pore diameter, and total pore volume of the samples could be obtained by nitrogen adsorption-desorption isotherms at 77 K. To analyze the functional groups on the FSC surface, the FSC samples were characterized by Fourier transform infrared spectroscopy (FTIR, Nicolet iS50, Thermo Fisher Scientific, Waltham, MA, USA). The changes in functional groups before and after FSC use could be determined by the frequency of characteristic absorption bands. To analyze the crystal structure of the samples, the FSC samples were scanned by a Japanese Rigaku X-ray diffractometer equipped with a CuK \(\alpha\) radiation source (XRD, Rigaku SmartLab 9 kW, Rigaku, Osaka, Japan). The reactive oxygen species were identified by electron paramagnetic resonance (EPR, JES-FA200, JEOL, Tokyo, Japan). The semi-quantitative analysis of elements such as N1s, O1s, C1s, and Fe2p in the Fenton iron mud was carried out by X-ray photoelectron spectroscopy (XPS, Thermo ESCALAB 250, Thermo Fisher Scientific, Waltham, MA, USA).
## 3 Results and Discussion
### Security Control  
SC is one of the terminal applications of I-FIA. The SC of insecure OC/instance mentioned in the previous section is performed. Using the proposed SC method, the critical unit pairs that own the highest Shapley values (positive and negative) and the amount of adjustment can be determined in each iteration. After four iterations, SC can be achieved. The iteration process of adjustment is shown in Table 2.  
A total of three generators (\(G4\), \(G7\), and \(G8\)) are determined to adjust their active power output to achieve SC. Unlike the traditional strategy of preventive control by constrained optimal power flow, our proposed method narrows the number of controlled generators. It is thus welcome to be applied to the actual online SC. At the same time, the generation of the slack machine \(G1\) has little change due to the balance strategy of critical unit pairs, and the change is allowed to be ignored in SC. The total time of SC generation is 3.148 s, which satisfies the requirement of online applications.  
The TDS of OCs before and after the security control is illustrated in Figure 7. Before SC, \(G7\) and \(G8\) located in one plant in the CEPRI36 system deviate from other generators after the disturbance. Thus, the system is deteriorated out of synchronization. In contrast, after implementing SC, the generators retain synchronization after the disturbance, validating the control strategy. From the angle trajectories of eight generators, \(G7\) and \(G8\) are the critical units affecting the security and stability of the system, which validates the instance-based local and global interpretation results.
## 4 Results and Discussion
### Time-Averaged Unsteady Simulation Results
#### 4.1.2 Investigation of Performance with Different Diffusers  
In this section, the aerodynamic performance and internal flow of the centrifugal compressor stage with different diffusers are analyzed according to the unsteady time-averaged results. Firstly, the influence of different diffusers on the stage performance is investigated. The stage performance curves for different diffusers are revealed in Figure 6. The maximum total pressure-rise of the three types of vaned diffusers occurs around 0.9 \(Q_{\text{d}}\). With the further decreases in mass flow rate, the total pressure-rise of TVD and HVD decreases faster, while the total pressure-rise of SVD can remain higher than that achieved with TVD and HVD. Moreover, the total pressure-rises of the three vaned diffusers are lower than that of VD, and TVD shows the worst total pressure-rise, which could mean that there is an extensive blocking in the diffuser throat under the near-choke operating point (\(\varphi=1.2\)\(Q_{\text{d}}\)). Next, the effect of the different diffuser configurations on the diffuser performance is studied.  
The total pressure loss coefficient (\(C_{\text{pt}}\)) and static pressure recovery coefficient (\(C_{\text{ps}}\)) are important indexes for characterizing diffuser performance. The coefficients above are defined as follows [6]:  
\[C_{\text{pt}}=\frac{P_{\text{t3}}-P_{\text{t4}}}{P_{\text{t3}}-P_{\text{s3}}} \tag{2}\]  
\[C_{\text{ps}}=\frac{P_{\text{s4}}-P_{\text{s3}}}{P_{\text{t3}}-P_{\text{s3}}} \tag{3}\]  
Figure 7 displays the variation of the \(C_{\text{pt}}\) and \(C_{\text{ps}}\) for different diffusers. At the design point, the minimum \(C_{\text{pt}}\) and maximum \(C_{\text{ps}}\) are shown by TVD, which means that the loss of TVD is the smallest around design mass flow. However, at the off-design operating point, the total pressure loss of TVD increases rapidly, especially at the low mass flow rate operating point. However, SVD has a lower \(C_{\text{pt}}\) and a higher \(C_{\text{ps}}\) at low mass flow rate points, and the overall trend is relatively flat between 0.7 \(Q_{\text{d}}\) and 1.2 \(Q_{\text{d}}\). This suggests that at the off-design operating point, the performance of TVD deteriorates faster, and SVD has better adaptability. The performance of HVD is almost the same as that of VD, with a slight improvement. It is worth noting that HVD has the most excellent diffuser performance at the near-choke operating point (\(\varphi=1.2\)\(Q_{\text{d}}\)).  
Figure 6: Performance curves for the compressor with different types of diffusers.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 4 Sea Ice Extent Mapping and Validation
### Sea Ice Mapping and Validation
#### 4.5.3 Test for Half-Day Retrievals  
Since the multi-source scatterometer offers more complete coverage of observations compared to the single-source scatterometer, thus resulting in a larger number of observations being acquired in the projected grid of the Arctic and Antarctic, it would therefore be possible to shorten the time interval to 12 h (separation at noon) in an attempt to retrieve half-day sea ice extents on the basis of multisource scatterometer data.  
We selected the multisource scatterometer data for October 2020 to try this out. In this section, we exclude polar regions over land and the polar hole (no scatterometer data coverage). In Figures 12a and 13a, it can be seen that the data coverages of multisource scatterometer data (i.e., containing at least one scatterometer measurement within the grid cell) as a percentage are above 97% at both half-day and one-day time intervals in both the Antarctic and the Arctic. In Figures 12b and 13b, it can be observed that the half-day sea ice area is in good agreement with the daily sea ice area. The MAD of differences  
Figure 11: Sentinel-1 SAR images showing sea water and ice distributions. Sea ice edge lines from the multisource scatterometer data (red lines) and NSIDC SIC 15% sea ice (yellow lines) are overlapped, with the blue line being the common overlay sea ice edge line. The image acquisition dates were: (a) 4 December 2021, (b) 24 December 2021, (c) 29 November 2021, and (d) 18 December 2021.  
between daily and half-day sea ice areas are 0.088 and 0.131 million km\({}^{2}\) for the Arctic and Antarctic, respectively.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 4 Software-Based Dynamic Information Flow Tracking
### System-Level Dynamic Information Flow Tracking
In order to capture the indirect flow in security applications, they introduced different types of tag instead for expressing different security policies in terms of similar tag structure. Besides, tags convey rich provenance information, e.g., the lifetime of a data byte and activities associated with the data byte. Further, indirect flows can be processed in different security policies according to the tag type. Similarly to other system-level DIFT solutions, significant performance and memory overheads are incurred (56x performance slowdown was introduced in a QEMU-based simulator platform).
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 4 Experiments
### Dataset  
[PERSON] et al. [8], [PERSON] et al. [10], and [PERSON] et al. [9] recently published synthetic datasets for estimating 3D human poses using egocentric images captured by a fisheye camera. Among them, we chose [PERSON]'s xR-EgoPose dataset [8] for training and testing our frame  
Figure 4: RGB image of the dataset (**top left**), 8-bit depth map (**top center**), original segmentation (**top right**), modified single-body segmentation (**bottom right**), and our segmentation (**bottom left**).  
work because the dataset contains depth information, which is crucial for regressing a depth map, along with the metadata (3D joint positions in our case). Additionally, the dataset supports high scalability by representing five body types (skinny short, skinny tall, normal, full short, and full tall), a variety of skin colors (white, light-skinned European, dark-skinned European, Mediterranean or olive, dark brown, and black), nine clothe types (sports pants, jeans, shorts, dress pants, skirts, jackets, T-shirts, long sleeves, and tank tops), five shoe types (sandals, boots, dress shoes, sneakers, crocs), and nine daily life behaviors (see the action type in Table 1). The dataset images have a resolution of 1024 \(\times\) 1024 pixels and a color depth of 16 bits. The RGB, depth, normals, body segmentation, and pixel world position are available for each frame. Metadata includes 3D joint positions, height (from 155 cm to 189 cm), environment, camera pose (when the images were captured), body segmentation, and animation rig. The segmented images in the xR-EgoPose dataset [8] did not reflect the characteristics of the depth map (see the top right image of Figure 4). Therefore, we adapted the image segmentation to include three regions, similar to the depth map (see the bottom left image of Figure 4), and a simpler version with two regions (body and vignetting regions) (see the bottom right image of Figure 4). By comparing these two segmented images, we will find the best one for our framework.  
The total number of frames in the modified dataset is 341 K, with 16 female and 18 male characters. The dataset is divided into three sets, as follows:  
* Train-set: 225 K, 11 male, 11 female.
* Val-set: 30 K, 1 male, 1 female.
* Test-set: 85 K, 6 males, 4 females.
How the Rigid and Deformable Image Registration Approaches Affect the Absorbed Dose Estimation Using Images Collected before and after Transarterial Radioembolization with \({}^{90}\)Y Resin Microspheres in a Clinical Setting  
[PERSON]  
1 Department of Medical Physics, IRCCS Azienta Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy 16 Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE \({}^{99m}\)Tc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., \(\mathrm{D_{T}}\) and \(\mathrm{D_{NTL}}\), respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on \({}^{90}\)Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and \(\mathrm{D_{T}}/\mathrm{D_{NTL}}/\mathrm{D_{L}}\) were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson's (PCC), concordance (CCCo) and Bland-Altman analyses. The Kaplan-Meier curves of overall survival (OS) were calculated according to \(\mathrm{D_{T}}\). Results: All computed CCS indicated very good (>0.92) agreement for volume comparison, while they suggested good (ICC \(\geq\) 0.869, PCC \(\geq\) 0.876 and CCCo \(\geq\) 0.790) and moderate agreement in the intra- and inter-modality \(\mathrm{D_{T}}/\mathrm{D_{NTL}}/\mathrm{D_{L}}\) comparisons, respectively. Bland-Altman analyses showed percentage differences between the manual and deformable approaches of up to about \(-31\%\), \(9\%\) and \(62\%\) for tumoral volumes, \(\mathrm{D_{T}}\) and \(\mathrm{D_{NTL}}\), respectively. The overall survival analysis showed statistically significant differences using \(\mathrm{D_{T}}\) cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusions: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic \(\mathrm{D_{T}}/\mathrm{D_{NTL}}\) constraints.
# Speeding up Statistical Tolerance Analysis to Real Time
## 5 Methods: Detailing the Approach to Speeding Up Tolerance Simulations
### Implementation of Statistical Tolerance Synthesis Using Numerical Optimization  
To investigate the impact on runtime, both open source CPU (NumPy) and GPU (CuPy) implementations of the statistical tolerance analysis have been integrated into the statistical tolerance synthesis process to calculate the cost-optimal tolerances using the differential evolutionary algorithm of [35]. The chosen parameter settings of the algorithm are listed in Table 5. For the CPU implementation with NumPy, the population is distributed among all CPU cores. For the GPU implementation with CuPy, a single Python process is sequentially executed.  
The cost-driven tolerance optimization has been applied to the overconstraint door hinge assembly defined in Section 4, leading to the following optimization problem:  
\[\text{min}\ C(X) \tag{6}\]  
under given constraints:  
\[\sigma_{p}(X)\leq 0.1 \tag{7}\]  
with the bounds of:  
\[X_{(T_{1}-T_{7})}\leq 0.7 \tag{8}\]  
\[X_{(T_{1}-T_{7})}>0 \tag{9}\]  
To ensure the functionality of the assembly, the requirements for \(p\) are defined as an inequality boundary condition. It demands that the standard deviation \(\sigma_{p}\) must be less  
Figure 5: Pseudocode of the implementation of the cost-driven statistical tolerance synthesis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline strategy & best1 bin \\ bounds & [0.0, 0.7] \(\times\) 7 \\ popsize & 25 \\ tol & 0.01 \\ mutation & (0.5, 1) \\ recombination & 0.5 \\ seed & 1992 \\ init & latinhypercube \\ workers & *** \\ \hline \hline \end{tabular}  
* workers = \(-1\): Distributes the populations among all available CPU cores for parallel computation,
* workers = 1: A single Python process is spawned.  
\end{table}
Table 5: Settings for the differential evolutionary algorithm.  
than or equal to 0.1 mm in order to guarantee a sufficient tightness. The tolerance-cost models are taken from [28]. Their coefficients are listed in Table 6.