esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 5 Conclusions  
By reviewing our current understanding of seismic resolution and proposing a workflow for the high-resolution interpretation of depositional facies and reservoirs, I have revealed that the following:  
1. For lithologic and facies mapping using seismic data, \"high-resolution\" refers to the ability to interpret the top and base of a sedimentary bed with a thickness of \(\lambda/4\) (typically between 15 and 150 m) in a vertical view. Additionally, it enables the detection of beds as thin as \(\lambda/80\), which range from 1 to 5 m when applying seismic sedimentology from a horizontal perspective.
2. Displaying 90\({}^{\circ}\) data and frequency fusion on a striatal slice is one of the most effective thin-bed detective workflows for seismic sedimentology among various attribute and visualization format choices.
3. Seismic modeling is essential for verifying and calibrating seismic sedimentological interpretations, such as defining seismic resolution and the true link between reflection configurations and local striatal and sedimentary architectures.
4. Machine-learning-based approaches can offer a more accurate interpretation of thin-bed seismic sedimentology.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Restrictions apply to the availability of these data. Data were ob-tained from third parties and are available to public. Acknowledgments: The State of Texas Advanced Resource Recovery (STARR) program supported this study. This publication is authorized by the Director, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. Conflicts of Interest: The author declares no conflicts of interest. References
## 2 Related Works
### Offloading Methods with Different Modeling Objects  
According to the task type and offloading requirements, the offloading decisions are generally classified into three categories: minimizing delay, minimizing energy consumption, and minimizing system cost. Offloading strategies for minimizing delay are to complete the task execution with the lowest time requirement. Offloading strategies for minimizing energy consumption are to make the whole system with the lowest energy consumption while satisfying certain delay requirements during task execution. Offloading strategies for minimizing system cost are generally to make a trade-off between delay and energy consumption, particularly trying to find a balance between energy consumption and delay to meet the different user requirements of IoT applications.  
[PERSON] et al. [19] studied the fine-grained task offloading problem in edge computing for low power IoT systems and proposed a lightweight and effective offloading scheme for multi-user edge systems to minimize the execution time by offloading the most appropriate IoT tasks to the edge servers. [PERSON] et al. [20] consider the scenario where multiple terminal users offload repetitive computational tasks to network edge servers and share computational results among these servers. They designed optimal fine-grained collaborative offloading strategies that utilize data caching to minimize task execution delays at the terminal. [PERSON] et al. [21] proposed a federated RL-based channel resource allocation framework for the fifth generation (5G) networks, which suggested collaborating learning estimates for faster learning convergence. [PERSON] et al. [22] made extensive research efforts on developing beyond sixth generation (6G) wireless networks, which aimed at bringing ultra-reliable low-latency communication services. [PERSON] et al. [23] found it difficult to achieve a balance between high resource consumption and high communication costs and proposed a local computing offloading method that minimizes the total energy consumption consumed by the terminal devices and edge servers by jointly optimizing the task offloading rate, the CPU frequency of the system, the allocated bandwidth of the available channels, and the transmission power of each device at each time slot. [PERSON] et al. [24] proposed an edge computing network architecture that enables edge nodes to share computational and radio resources, to minimize the total energy consumption of terminal users while satisfying the latency requirements of the tasks. [PERSON] et al. [25] proposed a cloud-edge computing system that includes the terminal layer, edge layer, and cloud layer. Based on this, a profit-maximizing collaborative computing offloading and resource allocation algorithm is designed to minimize the system cost and ensure that the response time of the tasks is satisfied. [PERSON] et al. [26] addressed the resource allocation problem in Edge-IoT systems through developing a novel framework named DeepEdge, which allocated resources to the heterogeneous IoT applications with the goal of maximizing users' Quality of Experience (QoE).
## 2 Materials and Methods
### Western Blot Assays  
Cell lysis was carried out with use Cell Lysis Buffer (Cell Signaling Technology, Danvers, MA, USA) with the addition of Protease Inhibitor Cocktail (BioShop, Burlington, ON, Canada) according to the protocol provided by the manufacturer. The PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) test was used to determine the protein content in the cell lysate, and the analysis itself was carried out using a Multiskan GO Microplat Spectrophotometer (Thermo Fisher Scientific). To investigate the mechanism of action of the experimental juices, proteins of the cell lysate were separated using Mini-PROTEAN TGX Stain-Free precast gels (Bio-Rad). Next, the Trans-Blot Turbo Mini Nitrocellulose Transfer Packs (Bio-Rad) were used, for the fast, efficient transfer of proteins from gels using the Trans-Blot(r) TurboTM Transfer System (Bio-Rad). Subsequently, the immobilized proteins were incubated with the appropriate primary antibody: Apoptotic protease-activating factor 1 Apaf-1 (#8723), Bcl-2-associated X protein Bax (#5023), cytochrome c (#11940), caspase-3 (#9662), caspase-7 (#9492), caspase-8 (#4790), mitochondrial serine protease HtrA2/Omi (#9745), Poly (ADP-ribose) Polymerase PARP (#9542), p53 upregulated modulator of apoptosis PUMA (#12450), second mitochondrial-derived activator of caspase (Smak/Diabolo) (#2954), Fas-associated death domain FADD (#2784), Fas cell surface death receptor Fas (#4233), receptor-interacting protein kinase RIP (#3493), tumor necrosis factor receptor-1 TNFR1 (#3736), tumor necrosis factor receptor type 1-associated death domain TRADD (#3684), serine/threonine kinase Akt1 (#4691), NF-x\(\upbeta\) p65 (D14E12) XP(r) (#8242), p38 MAPK (D13E1) XP(r) (#8690), p44/42 MAPK (#4695), tumor protein p53 (#2527), stress-activated protein kinase SAPK/JNK (#9252), and \(\upbeta\)-actin ACTB (#4970) as a reference protein (Cell Signaling Technology). Finally, the appropriate secondary antibody conjugated with horseradish peroxidase (#074 or #7076, Cell Signaling Technology) was applied. Detection was executed by chemiluminescence, using ClarityTM Western ECL Substrate (Bio-Rad). Visualization of the detected protein was performed using the ChemiDocTM Imaging System (Bio-Rad). Densiometric assays were performed using ImageJ (author [PERSON]). Results are shown as a mean \(\pm\) SD normalized to the internal reference protein (\(\beta\)-actin). Untreated negative control (UC) was set as 100% expression level.
## 3 Mathematical Modeling
### Hovering Within the Triple NEA System (153591) 2001 SN263  
The triple NEA system (153591) 2001 SN263 was chosen as the target for the ASTER mission, the first proposed Brazilian Deep-Space Mission [40]. This system describes a heliocentric orbit with 1.99 AU semi-major axis, 0.48 eccentricity, 6.7 deg inclination and a pericenter distance of 1.04 AU [147], such that it presents Earth minimum orbit intersection distance  
Figure 10: Evolution of the \(x\)-position (**left**) and \(x\)-control effort (**right**) for a spacecraft hovering around the stable equilibrium point \((x_{e},y_{e})=(0,1450.75)\) in the \(Oxy\) effective potential for 2001 SN263-ALPHA.  
Figure 9: Uncontrolled (dash) and controlled (dot) \(Oxy\)-trajectories around the stable equilibrium point \((x_{e},y_{e})=(0,\,1450.75)\) (central black dot \(\bullet\)) in the \(Oxy\) effective potential for 2001 SN263-ALPHA.  
(MOID) of 0.05061 VAU, marginally out of the PHA's category (MOID \(<\) 0.05 AU). Such orbital characteristics make this asteroid system an easily accessible target for a space mission.  
The ASTER mission will consist of two different remote-sensing phases. During the first phase, the spacecraft will be in a hovering position at approximately 40 km from the primary body. The second phase will consist of a terminator orbit phase with very low demand for maneuvers [63]. Proper specification of approximation/hovering orbits near such small body systems is mandatory for ensuring the success of such visiting missions. This task usually involves the search for stable trajectories that can be used by a spacecraft to observe the triplet from a mid-range distance, with minimum fuel expenditure, aiming at data acquisition for better environmental understanding and model updates. There may then follow a search for a trajectory closer to the bodies, eventually internal to the moonlets' orbits, or ever closer parking on a natural or artificial equilibrium point within the system.
[PERSON] and [PERSON] [23] develop a hybrid computation methodology to investigate turbulent flows containing shock waves, concluding that shock can promote high-level mixing in the non-similar region by amplifying the local turbulence, but this trend is limited in local space. It can be found that a shock wave can achieve mixing enhancement, but with the introduction of a wedge generator, the promoted area is mainly near the interaction point. It is worth noting that as the most typical and simple means of fuel injection, a wall transverse jet and its flow field structure [24; 25] and noise control [26] have been studied in detail by many scholars. Due to the jet's obstruction of the incoming flow, a bow shock wave will be formed in the upstream area of the jet. In addition, because the position of the jet hole is controllable, the position of the shock wave generated by the jet is also controllable, which provides a good idea for us to use the controllable shock wave induced by the jet to achieve mixing enhancement. The previous research [27; 28] related to wall jets is limited to the wall jet injecting fuel, whereas our research attempts to use the wall jet to induce a shock wave to act on the plane mixing layer. Based on this, we propose a new strategy called: wall-jet induced shock wave, which retains the advantages of shocks to enhance the growth rate of the downstream mixing layer and overcomes the disadvantage of the local range of shock interaction. Meanwhile, introducing shock waves through the wall-jet can conquer the drawbacks of the passive method and it does not require the participation of fixed devices in the flow field, which reduces flow loss. Furthermore, this scheme holds the advantage of an active method and the position of the shock wave interacting on the mixing layer can be controlled. By introducing shock wave induced through controlled wall jet to promote the mixing enhancement of supersonic mixing layers, it can expand the application of the classical problem of Shock wave/mixing layer interaction (SWMLI) and provide a new method to support the realization of efficient mixing under supersonic conditions. In the present work, the large eddy simulation (LES) method is employed to analyze the effect of shock waves induced by a wall jet on the supersonic planar mixing layer, and the flow and growth properties of the mixing layer is studied by means of turbulence characteristics and evaluation of the thickness of the mixing layer. Besides, the modal decomposition is performed by the DMD method to further explore the deep-seated mechanism of mixing enhancement influenced by a wall jet. Through extensive reading of the literature, we found that DMD has been applied to the decomposition of the flow field where the moving shock wave [29] is located, which shows the feasibility of DMD. The remainder of this paper is organized as follows: Section 2 gives a brief introduction to the numerical method and the DMD process. Section 3 shows the simulation results in terms of flow field visualization and growth characteristics and analyzes the flow mechanism by mode decomposition. Finally, Section 4 gives the conclusions.
## 2 Laser Layout and Features
### Auxiliary Beamline  
A small portion of the main beam, approximately 10%, is redirected before the cryo-cooled amplifier. The auxiliary pulse can be fully compressed, or not, according to the needs of the experimental campaigns maintaining high-repetition-rate operation.  
The pulse compression is achieved by a dedicated low-power compressor, shown in Figure 1, adopting two 1480 grooves/mm gratings with a gold coating and a retro-reflector mirror. Finally, the compression stage delivers \(\sim\)40 fs compressed pulses, with an efficiency about 70%, yielding a pulse peak power of \(\sim\)10 TW. These 10 Hz, 10 TW compressed laser pulses are delivered to the vacuum interaction chamber in the GEM area.  
The laser pulse is focused with a flexible focusing optical setup, allowing it to perform a wide range of experiments. Here, experiments are conducted to explore applications that can benefit from the 10 Hz repetition rate as the laser-guiding experiments, few-MeV electron acceleration via LWFA, amongst others. This experimental area also offers a unique advantage by providing rapid access to high-intensity laser-matter interactions for developing diagnostics applicable to plasma and laser systems. Moreover, it serves as a testing ground for innovative concepts such as high-repetition rate gas cells, prior to their deployment in higher-intensity interaction processes.  
For the gas jet and gas cell experimental campaigns, the setup is optimized to operate at a 1 Hz repetition rate due to the time required to restore the vacuum level to approximately \(10^{-6}\) mbar in the target chamber. This pressure is sufficiently low to avoid any disruption in laser propagation and to maintain the quality of the vacuum-gas interface.
## 2 MAC Design Issues in CRBANs  
Cognitive radio is a new technology that enables the more flexible and efficient use of the radio spectrum. It allows unlicensed users to access the radio spectrum without harmful interference with licensed users. A CR device intelligently adapts its spectrum usage by changing radio frequency according to predefined learning parameters to select the best operating frequency and transmission parameters. The industrial, scientific and medical (ISM) band is shared by various technologies, such as IEEE 802.11 (Wi-Fi), IEEE 802.15.1 (Bluetooth) and IEEE 802.15.4 (ZigBee). These technologies operate and coexist in the same frequency band, which may cause interference between different radio systems.  
Issues related to CR can span all layers of the communication protocol stack. The main functions of CR are spectrum sensing, spectrum access and spectrum sharing and are mostly performed by the PHY and MAC layers. There are several differences between the CR-aware MAC protocol and traditional MAC protocols. One is the number of available channels, which varies with both time and spatial dimensions in CR networks, but is fixed for each user in traditional networks. Furthermore, in CR networks, the MAC protocols should handle interference with PUs to protect them [10]. MAC protocols play an important role in exploiting spectrum opportunities, collision avoidance, interference control and avoidance for PUs and coordinating spectrum access to SUs. The main issues that must be addressed by CR-aware MAC in CRBANs are as follows.
## 4 RFQ-Decelerator for the GSI HITRAP Facility
### Beam Dynamics Investigations  
The acceptance of an accelerating-focusing channel could be obtained from DYNAMION beam dynamics simulations, using a broad 6D phase space input particle distribution, in particular \(\pm 180^{\circ}\) in beam phase length, emulating the full range of the cavity rf-phase. Every macroparticle, used for DYNAMION simulations, has its own ID-number, stored in a file together with particle coordinates and velocities. The macroparticles, accelerated to the desired energy, are marked and then selected from the input distribution to represent the acceptance of the channel at its entrance. Obviously, such an algorithm could be used for a decelerating channel as well. Therefore, this method has been implemented to the HITRAP RFQD to calculate firstly the longitudinal acceptance. For this purpose, the transverse coordinates and velocities of all particles were set to zero, i.e., a pencil-beam on axis was simulated.  
Figure 5: 3D surface of the original RFQD rod (**top**), reconstructed from the photometric measurements (**bottom**).  
The results of beam dynamics simulations in a wide range of rf-tank voltages clearly indicated a serious discrepancy with the design parameters of the RFQD: only particles with an input energy of 525 \(\pm\) 10 keV/u could be potentially decelerated with the fabricated RFQD channel, while the design energy range is significantly lower at about 500 \(\pm\) 10 keV/u. It can be clearly identified that not even a partial overlap of the required energy range with the corresponding simulated results could be observed.  
A supplementary set of experimental and numerical studies has shown that the HITRAP IH-DTL structure, when operated at nominal settings, credibly decelerates ions of 4 MeV/u to the design energy of about 500 \(\pm\) 10 keV/u required at the RFQD input. At the dramatic costs of reduced beam transmission for decelerated particles, the IH-DTL section could be detuned to provide for ions with a final energy of up to 515 keV/u, but not higher. Therefore, the energy of the beam that could be decelerated with the IH-DTL structure under all possible settings does not match the RFQD input energy that was reliably calculated based on the actual shape of the manufactured rods. For this reason, it must be concluded that the original RFQ decelerator on the HITRAP setup was not capable to deliver a decelerated beam under these conditions.
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
## 3 System Model
### Acoustic Propagation Model  
In order to capture the underwater physical layer features, the BELLHOP ray tracing model [25] is used in our system model, which provides a propagation model similar to experimental implementation for UWSNs [26]. This channel model can consider absorbing boundary conditions to perform acoustic ray tracing for a given speed of sound profile. The ray trajectories of arrivals is predicted by using Gaussian beam tracing, taking into account parameters such as bathymetric information, the horizontal distance between sensors, the depth of source and receiver, signal frequency, and more importantly the sound speed profile [27].  
To implement the ray tracing and transmission loss, the parameters are adopted from in [28]. The iso-speed sound velocity profile is used in our channel model with the bottom sound speed and the speed of sound in water equal to 1800 m/s and 1500 m/s, respectively. Furthermore, the sea bottom density is assumed as 1843 kg/m\({}^{3}\), while the seawater density is taken as 1024 kg/m\({}^{3}\).
## 2 Materials and Methods
### Volatile Compounds Analysis  
4.1 Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS) Analysis  
The volatile compounds in raw meat were tested as described by [PERSON] et al. [15] with minor modifications. The volatile compounds were extracted by headspace solid-phase microextraction (HS-SPME). Three grams chopped sample and 1.5 \(\upmu\)L 2-methyl-3-heptanone solution (1.60 \(\upmu\)g/mL in methanol) as an internal standard were placed in a 20 mL headspace vial. The vials were kept incubated at 50 \({}^{\circ}\)C for 20 min. The 65 \(\upmu\)p polydimethylsiloxane/divinylbenzene fused silica (PDMS/DVB) coating fiber (Supelco, Inc., Bellefonte, PA, USA) was exposed to the headspace vial for 40 min to absorb the volatile compounds. The fiber was inserted into the sampler of a gas chromatograph (GC) for 2 min for desorption. A gas chromatograph-mass spectrometer (GC-MS) (QP2010 plus, Shimadzu) equipped with a DB-WAX (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was used to detect volatile compounds in raw meat. Helium has a flow rate of 1 mL/min as a carrier. The temperatures of the injector and ion source were 200 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C, respectively. The initial temperature of the oven was 40 \({}^{\circ}\)C, and the heating procedure of the oven was held at 40 \({}^{\circ}\)C for 3 min, ramped up to 120 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, ramped up to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and held for 13 min. Mass spectrometry was performed in electron collision mode (EI) with a voltage of 70 eV and a full scan mode of 35-500 \(m/z\).  
According to the computer spectrum library (NIST11, NIST11s), compounds with the matching degree of volatile compounds greater than 80 were identified by MS, and the highest matching degree was 100. LRIs were analyzed according to the retention times of external standard n-alkanes (C\({}_{7}\)-C\({}_{40}\)) under the same GC-MS-detection conditions, and LRIs were compared with n-alkanes (C\({}_{7}\)-C\({}_{40}\)) reported in the literature to identify the aromatic compounds [16]. Semi-quantitative analysis of volatile compounds in raw meat was carried out using 2-methyl-3-heptanone as an internal standard. The concentration of volatile compounds was calculated according to the peak area ratio and the concentration of 2-methyl-3-heptanone.
###### Abstract  
Background: According to the International Standard (ISO10993-4), a test model using human blood must be used for blood compatibility evaluation of medical devices. However, various safety investigations have reported outcomes from animal models simulating clinical conditions. Additionally, the validation of animal blood in blood compatibility assessment models is limited. In this study, the differences in the reactivity of different species to coagulation and complement, as well as the main mechanisms governing blood clot formation upon contact with medical devices, were examined. Moreover, it aimed to acquire information required to design a incompatibility evaluation model and interpret the results. Results: Rabbits, porcine, rhesus monkeys, and cymondgus monkeys were exposed to one negative and two positive control substances, and variations in the partial thrombolistin time (PTT) were observed. The activity of the complement system was observed in accordance with the test method in ISO 10993-4. Although the PTT absolute values varied across animal species, the normalized PTT values--61% for rabbits, 66% for pigs, 63% for rhesus monkeys, and 65% for cynomolgus monkeys--exhibited no statistically significant differences. After reacting human and porcine sera with the material, the test model using human serum distinguished the positive and negative control candidates, whereas the test model using porcine serum could not discriminate them. Conclusions: These results suggest similar reactivity in blood coagulation across species after substance contact. However, complement system activity indicated a significant difference in reactivity between human and porcine blood. This finding will be useful in the design of a blood compatibility evaluation model for medical devices and interpretation of the results.  
ISO 10993-4; coagulation; complement; partial thrombolistin time (PTT); ASTM F2382 4.0/.