esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Sampling and Analysis of Naphthalene
## Experiments
### GC-MS Conditions  
The analyte was desorbed from the SPME fiber and analyzed on 6890N/5975C (Agilent, Santa Clara, CA, USA) GC-MS equipped with split/splitless inlet. For separation of naphthalene, a 30 m \(\times\) 0.53 mm BP-20 (SGE Analytical Science, Ringwood, VIC, Australia) column with film thickness 0.50 \(\upmu\)m was used. The constant flow of He in the column was 7.5 mL\(\cdot\)min\({}^{-1}\). The column flow rate was higher than typical because of a permanent 1:4 split at the end of the GC column to an olfactometry port (not used for this research). The ion source, quadrupole and MS interface temperatures were 230, 150 and 240 \({}^{\circ}\)C, respectively. The GC injection port was working in splitless mode at 240 \({}^{\circ}\)C; purge activation time was 1 min. Owen temperature was programmed from initial 70 \({}^{\circ}\)C (held for 0.5 min) to 240 \({}^{\circ}\)C at the heating rate of 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). Retention time for naphthalene was 5.80 min. Detection was carried out in a SIM mode at m\(\cdot\)z\({}^{-1}\) 127 and 128 for selective detection and quantification of naphthalene. Total GC run time was 14 min.
## 6 Discussion
Additionally, the exploration of chipless RFID technology for structural health monitoring presents a non-contact solution for defect detection, eliminating the need for electronic chips [125; 126; 127; 128; 129; 130; 131]. These emerging techniques show promising potential in revolutionizing material inspection and structural integrity assessments and are bound to be enhanced further with subsequent research. Despite their numerous advantages, MNDT techniques are not without challenges. One of the primary limitations is their sensitivity to environmental conditions, such as temperature and humidity, which can affect the accuracy of measurements. Moreover, the sophisticated equipment required for MNDT, including high-frequency signal generators and sensitive detectors, hinders widespread adoption in field applications. However, the recent integration of AI with MNDT methods presents a promising avenue. AI, in particular ML, can significantly enhance data analysis, enabling the automation of defect detection and classification and thus reducing the reliance on expert interpretation [141]. This integration can lead to more efficient and accurate investigations, particularly for complex structures [149]. Future research should focus on several key areas to overcome current limitations and expand the applicability of MNDT techniques. Enhancing the resolution and sensitivity of these methods will be crucial for detecting smaller and more complex defects. Developing more cost-effective, portable, and user-friendly devices can facilitate broader adoption, especially for on-site inspections. Additionally, further exploration into the integration of AI algorithms can refine data analysis processes, making MNDT methods more resilient and reliable.
## 1 Introduction
To date, various SLM machines, such as the EOSINT M100, M270, and M280, Concept Laser M1 using, Mlab, PM100 Dental, the PXM system, SLM Realizer, and SLM Realizer 2, have been used in fabricating metal frameworks for RPDs and tooth-supported prostheses [16], with the EOSINT machines being the most frequently used [17]. In Japan, the Japanese Pharmaceutical Affairs Law approved the M100, M270, and M290 SLM machines manufactured by EOS (Krailling, Germany) for use in RPD fabrication [17]. The M290 is a mid-sized SLM machine (2500 \(\times\) 1300 \(\times\) 2190 mm\({}^{3}\)) with a laser power and beam spot size of 400 W and 100 \(\upmu\)m, respectively. The M100 is a small machine (800 \(\times\) 950 \(\times\) 2250 mm\({}^{3}\)) suitable for a dental laboratory with limited space, with a small laser spot (beam spot size: 40 \(\upmu\)m), providing excellent detail resolution that is ideal for fabricating highly complex dental devices. Both machines are widely used globally; however, their parameter settings differ, and their influences on the characteristics of the fabricated devices remain unclear. Several studies comparing the mechanical properties of Co-Cr alloys fabricated using different SLM machines have been reported [18; 19; 20]. However, these studies used different Co-Cr powders, machines manufactured by different companies, and varying scanning strategies. It has been reported that the powder size distribution considerably influences the microstructures and mechanical properties [21]. [PERSON] et al. reported that powder granulocyte remarkably influenced heat-treated Co-Cr alloys when using a different powder size distribution because of the different extents of recrystallization occurring between samples with different powder size distributions [21]. Apart from the powder size, differences in the chemical composition also affect the microstructures and mechanical properties. In addition, the scanning strategy impacts the cooling rate [22], which affects the ratio of face-centered-cubic (FCC) and hexagonal close-packed crystal structures [23]. Therefore, the results are influenced by various complex factors, such as the chemical composition of the alloy powder, SLM operating parameters, and scanning strategy [24]. This study aims to investigate the influence of different SLM machines on the mechanical properties and microstructures of the fabricated alloys. The investigation is conducted under conditions with minimal confounding factors by using SLM machines (M100 and M290) from the same company, the same alloy powder, and the same scanning strategy. The null hypothesis is that the type of SLM machine would not influence the mechanical properties of the Co-Cr alloys. The results obtained from this study are expected to serve as a criterion for dentists and dental technicians when selecting SLM machines for fabricating dental prostheses using SLM.
## 3 Results and Discussion  
A numerical investigation of the deformation and damage evolution of the NiAl sample was conducted using discrete element analysis based on micro-CT imaging. A large number of particles and small time steps make micro-CT-based DEM computations very time consuming. Naturally, parallel computing is perceived as an obvious way to increase computational capabilities. However, the selection of an efficient parallel algorithm is highly dependable on the considered problem and the method used [37; 38; 39]. Double-precision DEM computations were performed using the developed GPU code [19] on the NVIDIA(r) Tesla(tm) P100 GPU Computing Accelerator. Geometry reconstruction, DEM simulation, and visualization of results were performed on the computational infrastructure [39] of the Vilnius Gediminas Technical University.  
A uniaxial compression test of a cube-shaped NiAl sample was simulated using the developed discrete element model based on micro-CT imaging. The NiAl sample of 183 \(\times\) 183 \(\times\) 183 \(\upmu\)m\({}^{3}\) was represented by an assembly of 188,190 particles of radius 1.5 \(\upmu\)m (Figure 2d). The irregularly packed sample was subjected to slow uniaxial vertical compression by moving two rigid plates. The compression strain rate was 53 s\({}^{-1}\), which was found to be sufficiently low to obtain quasi-static loading. In other directions, the deformations are free.  
The main material properties of NiAl were measured in the experimental research. The parameters of the DEM model were considered as follows. The density of the NiAl particles is equal to 5910 kg/m\({}^{3}\). [PERSON]'s ratio of NiAl is equal to 0.3; therefore, the ratios of normal to shear stiffness of the particles and bonds are considered equal to 3.0. The friction coefficient of the NiAl particles is assumed equal to 0.3. According to initial calibrations, the elasticity moduli of the particles and bonds are equal to 25 \(\times\) 10\({}^{9}\) Pa, which results in the slope angle of the numerically obtained stress-strain curve being close to that of the experimentally measured one. The normal stiffness of the bond is computed by using the values of the bond elasticity modulus and radii of bonded particles [9]. The tangential stiffness of the bond is obtained by dividing the normal bond stiffness by 3.0, because of the relevant Poisson's ratio value. In the normal and tangential directions, the bond strength values are equal to 3 \(\times\) 10\({}^{8}\) Pa and 3 \(\times\) 10\({}^{9}\) Pa, respectively. According to the initial calibrations, carefully selected bond strength values have a crucial influence on the accurate prediction of the stress peak and following fall-down of the macroscopic stress-strain curve. The greatest challenge is to capture small deviations of the stress-strain curve from the line, avoiding premature fall-down of the curve. In such a complex case,typical of the slightly ductile behavior of materials, calibration of the bond strength values might be required to correct the value of the bond elasticity modulus, which governs the slope angle of the numerical stress-strain curve.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Machine Learning for Aquatic Vegetation Classification
#### 2.2.2 Hardware and Software Configuration  
As noted earlier, DNNs, being computationally-intensive, require powerful processing units for training in a reasonable timeframe. DNNs have millions of learnable parameters which undergo hundreds or thousands of optimization cycles during training. This precludes the usage of standard CPUs, which have a limited number of cores (eight in typical desktop computers), for DNN training. However, modern graphics processing units (GPUs), developed for gaming purposes, have thousands of cores which facilitate parallel computation via the Nvidia CUDA platform. For this research, a single Dell Precision T7500 workstation was configured with the following hardware: Intel Xeon CPU (2.13 GHz, 2 processors), 12 GB RAM, and Nvidia GTX 1070 Ti GPU with 8 GB memory. The Nvidia CUDA deep neural network (cuDNN) library and other supporting software was installed to enable MATLAB, Python, and Google Colab to take advantage of the GPU.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 1 Introduction
Data and model\(-\)driven deep reinforcement learning (DRL) algorithms can correctly identify complex timeless environments and provide optimal decisions. Applying DRL to battery scheduling optimization strategies and decision\(-\)making, through well\(-\)trained DNNs to form a complete sensing and decision system, can effectively drive optimal scheduling toward reliability and accuracy. In this study, we first construct the environment of reinforcement learning, namely the battery aging model. We apply a long short\(-\)term memory neural network (LSTM) to extract temporal features of continuous data and develop health indicators closely related to battery capacity. The Gaussian regression process (GPR) is proposed to mine the dependency between battery SOC and health indicators, and model verification is performed to ensure that the environment responds to the agent promptly. In the reinforcement learning algorithm section, we propose the double DQN method to find the optimal solution for battery charging and discharging scheduling, expecting the number of battery cycle life to reach the maximum. To alleviate the problem of the \"dimensional curse\", principal component analysis is proposed to reduce the dimensional state space. It saves computation time by projecting the high\(-\)dimensional continuous state set into a lower\(-\)dimensional feature space. We add the temperature and internal resistance difference constraints to the reward function of reinforcement learning, which provides direction for the agent. The results indicate that the model extends the number of aging battery cycle lives, and that it can adjust the parameters automatically through the reward and punishment mechanism to adapt to different batteries. The main contributions of the method are as follows:  
(1) An optimization model of a battery aging strategy based on reinforcement learning is proposed, which can dynamically provide an optimization strategy for subsequent stages at any aging stage and enhance the model's adaptability. (2) A new method is proposed to optimize the power battery scheduling problem using principal component analysis and double DQN, enhancing the battery's charging and discharging cycles in the training set. (3) Adding temperature and internal resistance difference constraints in the reward function of the reinforcement learning model, strengthening the training efficiency. This paper is structured as follows: Section 2 introduces a mathematical description of the optimization problem. In Section 3, we describe the battery aging environment model and conduct experiments to evaluate the accuracy of the model in describing battery aging behavior. The double DQN algorithm for solving the optimization problem is presented in
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### Simulation Method of Each Signal Source
#### 2.3.1 GNSS and LEO Constellation Simulation
Figure 7: Global visibility analysis of LEO constellation. The measurement value of pseudo distance ratio is obtained by Formula (24):  
\[\hat{\hat{\rho}}=\hat{\rho}+\delta\hat{\rho}_{cd}+\delta\hat{\rho}_{ct} \tag{24}\]  
where \(\hat{\hat{\rho}}\) is the measurement of satellite navigation pseudo-range ratio. \(\hat{\rho}\) is the theoretical distance. \(\delta\hat{\rho}_{cd}\) is the receiver clock drift. \(\delta\hat{\rho}_{ct}\) is the range-rate tracking error. The error parameters used for satellite navigation in this paper are given in Table 3.  
Table 3 shows related parameters used by GNSS and LEO satellite navigation models. On the basis of theoretical distance and range rate obtained by simulation, range error and range rate error are added, which are the measurement values of pseudo-range and pseudo-range rate of GNSS and LEO satellite navigation models.
# Advanced Microbubbles as a Multifunctional Platform Combining Imaging and Therapy
## 2 Advanced Microbubbles for Imaging and Therapy
### Cardiovascular Diseases  
It is difficult to diagnose the thrombus in the blood vessels or heart cavity since in some cases a small thrombus is hard to locate. In recent years, a variety of thrombus-targeted MBs have been engineered to provide a new perspective for emboli detection by means of US imaging. [PERSON] developed the perfluorocarbon-containing bubble liposomes (BL) with RGD peptides, which bind to activate platelet glycoprotein IIb/IIIa complexes to enhance signals in thrombus US imaging [65]. The targeted BL were mixed with whole blood in vitro, and injected into rabbits with acute thrombotic occlusions in the ilio-femoral artery in in vivo experiments and used a 7.5-9 MHz linear transducer and a conventional ultrasound machine to image the thrombi. The results showed that these targeted MBs could markedly enhance the image contrast in thrombus imaging and may be useful for diagnosing acute thrombotic vessel occlusion. Ultrasound of arterial thrombi has immense potential, but the high-shear arterial flow limited effective binding of MBs to the thrombi. To overcome this obstacle, cyclic peptide with a sequence of Arg-Gly-Asp-D-Phe-Cys was fabricated and proved that they had much higher binding affinity and selectivity to the glycoprotein IIb/IIIa receptors than its linear counterparts. They are thus likely to be the optimal targeted probes for imaging arterial thrombi with US modality. [PERSON] also constructed novel MBs conjugated to detect arterial thrombi, which had greater GP IIb/IIIa-targeted binding capability under all shear stress conditions [66]. The echogenicity of the platelet-rich thrombus was enhanced in vitro and the intensity of the abdominal aortic thrombi showed 3.2-fold higher than the control in vivo. With this technique, ultrasound provides a strategy to diagnose the acute coronary syndrome in patients without clear symptoms or electrocardiographic features. [PERSON] applied clinically translationable dual P- and E-selectin-targeted MBs to detect transient myocardial ischemia [67]. The results showed high US imaging enhancement within the ischemic area compared with remote myocardial tissues in myocardial ischemia/reperfusion rats. In the meantime, it has been demonstrated that these dual selectin-targeted MBs in transient myocardial ischemia provide reliable detection of transient myocardial ischemic events (late-phase enhancement persisted 5 and 24 h post reperfusion) without inducing ventricle dysfunction and myocardial necrosis.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 2 FD-SDWMN Architecture
### Overall Architecture  
FD-SDWMN is a distributed, multi-domain, SDWMN control plane that enables efficient delivery of end-to-end network services. The FD-SDWMN control plane composes of distributed controllers that are in charge of network domains, they communicate with each other to exchange their local information to aggregate a network-wide view for efficient end-to-end management. Figure 2 shows that the controller architecture is composed of two parts: the local level, where the main functionalities of a local domain are gathered, and the global level, which aggregates other domains' control information  
Figure 1: Flat Distributed-Software Defined Wireless Mesh Network (FD-SDWMN) flat architecture.  
such as topology state, QoS properties, etc. Also, southbound interfaces are used to push forwarding policies to data plane elements and gather their status. Finally, the northbound interfaces provide controllers with applications' management policies and requirements.  
Multiple modules compose the controller functionalities, they are managed (start, stop, update and communicate) by the core component. FD-SDWMN architecture leverages from the existing SDN controller's modules, such as the OpenFlow driver for OpenFlow protocol implementation, router and host managers for keeping track of network elements and link discovery that implements LLDP (Link Layer Discovery Protocol) for topology discovery. Furthermore, new modules are developed to enhance architecture functionality as displayed in Figure 2. Controllers must communicate with each other to gather and accumulate overall network status information. In order to accomplish this task, each controller uses two essential parts: (i) a coordinator module discovers neighboring domain controllers to establish and maintain a reliable and secure distributed publish/subscribe channel. Moreover, (ii) different agents that exchange needed information among controllers via this channel.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 3 Bioactive Compounds in the Prevention and Treatment of Colorectal Cancer
### Effects of Cannabinoids in CRC-Associated Mechanisms  
Over history, there has been increasing evidence of cannabis's beneficial effects on CRC. Several in vitro and in vivo experiments showed that natural phytocannabinoids from _C. sativa_ can interact with some of the mechanisms inherent in cancer. Among them, apoptosis, autophagy, inflammation, migration, invasion, and metastasis are the target mechanisms that have demonstrated better outcomes in CRC carcinogenesis with cannabinoids (Table 3). As mentioned earlier, cannabinoids can exert anticancer effects in part due to their interaction with the ECS. This is a complex cell signaling system present in all mammals, which is involved in regulating various physiological and cognitive processes, such as appetite, pain, mood, and memory. It is composed of three main components: endocannabinoids, receptors, and metabolic enzymes. Endocannabinoids are lipid-based neurotransmitters produced by the body, such as anandamide and 2-arachidonoylglycerol (2-AG), which bind to cannabinoid receptors (CB1 and CB2) found on the surface of cells, triggering a cellular response. Cannabinoids then can act on the ECS by mimicking endocannabinoids and binding to CB1 and CB2 receptors. Other receptors interact with endocannabinoids and modulate the ECS, including G-protein-coupled receptor 55 (GPR55), transient receptor potential cation channel subfamily V member 1 (TRPV1), TRPV2, transient receptor potential cation channel subfamily M member 8 (TRPM8), and peroxisome proliferator-activated receptors (PPARs) (Figure 4) [49]. The role of ECS in CRC has been reviewed elsewhere [17]. However, it is important to note that CRC cells and tissues express  
Figure 3: Chemical structure of the main terpen found in _C. sativa_.  
both CB1 and CB2 receptors, and TRPM8, TRPA1, TRPV1, and TRPV2 receptors, to which cannabinoids can bind to exert biological effects on CRC [12; 17].
# Independent System Calibration of Sentinel-1B
## 5 Antenna Pointing Determination  
The SAR antenna pointing determination is required to illuminate the acquired swaths with maximum signal power as the antenna patterns are designed exactly for this purpose. In addition, an accurate knowledge of the antenna pointing is required for correctly applying the antenna pattern correction during SAR processing.  
The procedure of determining the antenna pointing has been divided into two parts: (i) azimuth antenna pointing and (ii) elevation antenna pointing. Both cases rely on different measurement approaches which will be briefly discussed for Sentinel-1B in this section.