esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### XRD Pattern Evolution during CSP
#### 3.3.2 XRD Peak Width  
Figure 12 presents variations in the FWHM for the 200 XRD peak during the CSP in situ studies. Relative to the initial value, the FWHM increases as the sample is both loaded with compressive stress and heated to the desired CSP temperature. It is assumed that the heterogeneous nature of the powder and water mix result in significant microstrains once the powder is under load. These microstrains are then expected to dissipate somewhat as the densification proceeds and water is expelled from the sample. In principle, at 100% theoretical density the only microstrains remaining would reflect the anisotropic elastic constants of the KDP crystal structure, depending on the local environment surrounding any given crystallite. Regardless of the degree of microstrain that sets in for one of the in situ tests, the relaxation effect (relative reduction in FWHM with time) in Figure 12 is a complex function of overall densification, loss of nanoscale pores, and the lubricating effect of dissolving and re-precipitating low-density KDP material--whether as an aqueous shell around each water-filed pore or as a phase at grain boundaries as surrounding grains sinter together. The 20 value for the KDP 200 XRD peak at the 21 keV X-ray energy used is \(\approx\)9.15\({}^{\circ}\). The FWHM of this peak is \(\approx\)0.05\({}^{\circ}\) but the _additional_ FWHM, apparent in the early stages of CSP but dissipating somewhat later is typically \(\approx\)0.01\({}^{\circ}\) (Figure 12). Measured in microstrain, \(\Delta\)d/d, where d is the 200 XRD peak lattice spacing, this would amount to \(\approx\)1.1 \(\times\) 10\({}^{-3}\), or \(\pm\)5.5 \(\times\) 10\({}^{-4}\), which is reasonable compared to the overall compressive  
Figure 11: Ultrafine low-density volume fraction versus cold sintering time for 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C with p = 100 MPa and 125 MPa, and for p = 100 MPa, 125 MPa, 150 MPa at 60 \({}^{\circ}\)C. Lines are guide to the eye. Arrows indicate maxima in the volume fractions during each in situ study.  
strain expected (1.7 \(\times\) 10\({}^{-3}\) to 2.5 \(\times\) 10\({}^{-3}\) for p = 100 MPa to 150 MPa) given the likely degree of inhomogeneity in the system during CSP. Some peak narrowing may also be associated with grain growth and removal of crystal defects by diffusion.
## 4 Implementation
### User Study  
We conducted three user studies to validate the practical impact of the DFF device. The first user study tested the accuracy of the directional sensation generated via force concentration. The second user study tested how well the users could feel various magnitudes of weight generated via force concentration. The last study tested the impact of DFF in the reported immersivity of a VR application. A total of 12 participants, consisting of six males and six females, aged between 20-24 (Mean = 21.33; SD 1.37) were recruited for the three user studies and subsequent interviews. The 3 studies lasted approximately 50 to 60 min.
###### Abstract  
The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving good performance when addressing multiple shadow problems, by reducing background surface similarity and ghost artifacts. First, a combined contrast enhancement technique is applied to the input frame sequences to produce high-quality output images for indoor surroundings with an artificial light source. After obtaining suitable enhanced images, segmentation and noise removal filtering are applied to create a foreground mask of the possible candidate moving object shadow regions. Subsequently, geometry and color information are utilized to remove detected shadow pixels that incorrectly include the foreground mask. Here, experiments show that our method correctly detects and removes shadowed pixels in object tracking tasks, such as in universities, department stores, or several indoor sports games.  
color information; median filter; luminance; image enhancement; moving shadow detection; ghosting artifacts +
Footnote †: journal: Medical Image Analysis
## 3 Design and Training Methods for DQN Feature Constrained Control
### Definition and Division of State Space  
To address the real-time nature of the IBVS task, we discretize the high-dimensional camera image space and build an offline camera FOV environment to map visual features to state \(S\). This state \(S\) is used as an input to the Q-network. Further, the Q-network controller can then make correct feature constraint decisions based on the current state \(S\). Thus, the input state of this Q-network learning is defined as shown in Figure 1. Firstly, the camera image plane is discretely divided into a space of grid, and the two main regions of grid space feature constraints and visual servoing (VS) are defined, where the feature constraint region is further categorized into dangerous area and forbidden area based on the distance from the camera FOV boundary. Then, the Q-network input environment state is given by the following equation:  
\[\left\{\begin{array}{l}x=\text{int}\Big{(}u\times\frac{m_{u}}{(u_{\text{max }}-u_{\text{min}})+1}\Big{)}\\ y=\text{int}\Big{(}v\times\frac{u_{v}}{(u_{\text{max}}-v_{\text{min}})+1} \Big{)}\end{array}\right. \tag{5}\]  
where (\(u\), \(v\)) are the pixel coordinates on the image plane, with \(u_{max}\) and \(u_{min}\) representing the upper and lower limits of the horizontal axis, and \(v_{max}\) and \(v_{min}\) representing the upper and lower limits of the vertical axis. Further, the state set \(S=\Big{\{}S_{(x,y)}\Big{|}s_{(x,y)}=(x,y)\Big{\}}\) is obtained from Equation (5). Namely, \(S\) denotes the state space related to the point feature (Agent) in the camera FOV grid environment.  
The state \(S\) is defined by the grid position \(S_{(x,\,y)}\) = (\(x\), \(y\)), as depicted in Figure 1. Through Equation (5), the pixel coordinates (\(u\), \(v\)) of the point feature in the actual camera FOV resolution image are transformed into the position \(S_{(x,\,y)}\) = (\(x\), \(y\)) in the grid environment. Namely, \(S_{(x,\,y)}\) = (\(x\), \(y\)) represents the state of the point feature (Agent) in the grid environment. Regarding state transitions, we define the transition probability function \(P(S\), \(a)\) to describe how the state of the Agent changes when an action \(a\) is applied in the grid environment. Specifically, the Agent moves within the grid environment according to the selected action, and the new state is determined by the Agent's new position \(S^{\prime}(x,y)\) after executing the action. For reward shaping, we assign higher reward values to states within the VS mission area, which helps guide the Agent to train more effectively, thereby achieving the goal of constraining the point feature's coordinates in the actual camera FOV.
###### Abstract
Variables related to the device structure and the environment such as the fill factor and the TE leg height were optimized for human body application [19; 20; 21; 22]. On the other hand, in human body applications, few studies on material engineering for higher power density have been reported. In general, the performance of the thermoelectric materials can be improved by such processes as doping [23], polycrystalline grain size control [24], superlattice period control [25], etc. These engineering techniques change the three main properties of the TE material: the Seebeck coefficient (\(\alpha\)), the electrical conductivity (\(\sigma\)), and the thermal conductivity (\(\kappa\)). These properties trade off against each other, as shown in the equation of ZT = (\(\alpha^{2}\)\(\sigma\)T)/\(\kappa\)[26]. As a result, the material engineering techniques reported to date are aimed at finding the maximum point of the ZT. [PERSON] et al. confirmed the importance of material thermal conductivity in a heat sink-limited condition [26]. [PERSON] et al. showed that when the material ZT is fixed, as the thermal conductivity of the material decreases, power density generated from body heat increases [20]. However, while this result emphasizes the importance of material thermal conductivity in device simulations, it does not suggest how this affects the engineering of the materials. In this study, an experiment was conducted based on P-type BiSbTe\({}_{3}\) which is commercially used for a room temperature condition [27]. Material properties of the P-type BiSbTe\({}_{3}\) according to carrier concentration were extracted, and then the power density of the device was calculated for each material property. The material properties were simulated by using first-principles calculations [28], and the power density was calculated based on the thermal resistance model in the human body application. The results showed that the optimum carrier concentration for ZT and power density were different. In this study, the cause of the discrepancy between the carrier concentrations for the maximum ZT and the maximum power density in the human body application was analyzed and a theoretical approach for material engineering was proposed in order to reduce this mismatch.
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
###### Abstract
Small sharp peaks emerged in the A-RBs at 966 cm\({}^{-1}\) for the C-H bending of trans-alkene in polybutadiene (PB), C-H bending of terminal-alkene in PB at 911 cm\({}^{-1}\), and out-of-plane bending of the C-H in the monosubstituted aromatic ring in polystyrene (PS) [16; 17]. All these studies identified rubber-derived compounds in A-RBs' matrix; however, a better understanding of these modifications necessitates further investigation into their effects on asphalt saturates, aromatics, resins, and asphaltenes (SARA) fractions. The composition of the SARA fractions of asphalt binder affects its performance and aging characteristics, as confirmed through both experimental studies and molecular dynamic simulations [18; 19; 20; 21; 22; 23; 24; 25]. As the asphalt aged, the contents of resins and asphaltenes increased, while saturates and aromatics decreased [19; 20; 22]. This conversion turns aromatics into resins that can further be converted into asphaltenes [26; 27]. In A-RBs, there was a positive correlation between asphaltene and resins content and adhesion, while saturates and aromatics had negative correlations with adhesion. The rubber particles' size and proportion in A-RBs influenced SARA composition. Increasing the rubber percentage or decreasing the rubber particle size caused increases in the asphaltene and resins components and decreases in the saturate and aromatic components. This was due to the absorption of the asphalt's light components by rubber particles [21]. Rubber inhibited the increase of carbonyl in asphaltenes, resins, and aromatics, promoting the aging resistance and low-temperature performance of A-RBs [28]. Previous studies [18; 21; 22; 28] examined A-RBs' entire matrix, along with SARA components, chemically and rheologically. Due to the rubber modifications, the distribution of SARA fractions was altered, influencing rheological performance. These alternations were promoted due to the rubber swelling through absorbing low-molecular-weight fractions (saturates and aromatics). However, these studies lacked in-depth analysis of the interaction between rubber and SARA components through FTIR spectral changes. Consequently, the primary objective of this study was to understand the chemical interactions between rubber and asphalt fractions. This was executed through (1) utilizing FTIR to detect functional group changes and spectral shifts in the SARA fractions of the liquid phase as a result of rubber modifications; (2) evaluating the effect of changing interaction conditions (shear speed and temperature) on these spectral changes; and (3) correlating thermal analysis of the rubber particles (dissolution extent and dissolved components) and rheological performance of the A-RBs (stiffness and elasticity evolution) with the FTIR spectral changes.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 5 Conclusion and Future Works  
In this paper, a coupled CNN and LSTM prediction model is proposed and applied to USV roll motion prediction. The CNN layer extracts spatially relevant and local time-series features of the USV sensor data. The LSTM layer reflects the long-term USV movement process and predicts roll motion for the next moment. The fully connected layer decodes the LSTM output and obtains the final prediction results. Two case studies were carried out. The sensor data measured by \"JingHai-VI\" and \"JingHai-III\" USVs of Shanghai University were modeled for roll motion prediction. In both case studies, the experiment results indicated that the proposed model had superior performance to ARIMA, DNN, univariate LSTM, and multivariate LSTM models. We proved that the proposed model was efficient in predicting USV roll motion.  
Future work will focus on modeling all six degrees of freedom motion of USVs, and an attention mechanism will be added to the prediction model. Moreover, larger data sets will be used to train the prediction model.  
Conceptualization, W.Z. and P.W.; Resources, Y.P. and D.L.  
This research was founded by the National Science Foundation for Distinguished Young Scholars of P. R. China (under Grant 61525305), State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center (under Grant SKLA20180303), Natural Science Foundation of Shanghai (under Grant 19 ZRI417700), Project of Shanghai Municipal Science and Technology Commission (under Grant 17 DZ1205000), High Performance Computing Center, Shanghai University.  
The authors declare no conflicts of interest.
## 4 Neuoprotective Peptides and Their Therapeutic Application in HIE
### CARPs Are Intrinsically Neuroprotective  
An early study by [PERSON] et al. [63], demonstrated that cationic arginine-rich hexapeptides (i.e. containing 2-6 arginine residues; net charge +3-+7; e.g., RRRRRR-NH\({}_{2}\)) were neuroprotective in an in vitro hippocampal neuronal NMDA excitotoxic model. These findings were in line with subsequent studies in our and other laboratories, demonstrating that the CPP TAT (YGRKKRRQRRR; net charge +8) possesses intrinsic neuroprotective properties following in vitro glutamic acid excitotoxicity and oxygen glucose deprivation (OGD), as well as in vivo following excitotoxicity and cerebral ischemia in rats [13; 14; 64; 65; 15]. We subsequently demonstrated that in an in vitro cortical neuronal excitotoxic model, the CPPs poly-arginine-9 (RRRRRRRRR-NH\({}_{2}\); net charge +10) and penetatin (RQIKIWFQNRRMKWKK-NH\({}_{2}\); net charge +8) were even more potent than TAT [12]. The high potency of R9 led us to explore other poly-arginine peptides (e.g., R1, R3, R6-R15, R18, R22),as well as several other CARPs, including protamine in the in vitro excitotoxic and OGD neuronal injury models [27; 28; 29]. These studies confirmed that CARPs are highly neuroprotective, with efficacy increasing with arginine content and peptide positive charge; peaking at R15 to R18 for poly-arginine peptides [27; 29] [Note arginine and lysine (K) are the only positively charged amino acids, while histidine has a low positive charge].  
Our laboratory has also demonstrated that CARPs and in particular the poly-arginine peptide R18 are neuroprotective in in vivo experimental brain ischemic and/or hypoxic injury models [18; 20; 21; 22; 24; 26; 27; 29], and that CARPs can reduce neuronal cell surface glutamate receptor levels and excitotoxic calcium influx [19; 22; 23; 25; 27; 28]. As highlighted previously [19], based on our findings, we have proposed that the neuroprotective properties of most, if not all, putative neuroprotective peptides fused to cationic CPPs (e.g., TAT, R9 and penetrain), as well as other CARPs, are determined by the arginine content and positive charge of the peptide, with potency also influenced by other amino acid residues (e.g., lysine, tryptophan and phenylalanine) [19; 29]. Importantly, an arginine and cationic charge mediated neuroprotective mechanism of action of CARPs, including CPP-fused to putative neuroprotective peptides (e.g., TAT-NR2B9c and TAT-NR2 Bct), is supported in studies by [PERSON] et al. [65] and more recently [PERSON] et al. [66]. Based on studies from our own and other laboratories, we believe that the mechanism of action of putative neuroprotective peptides fused to a CPP need to be critically re-evaluated. In light of the potentially confounding effects of CPPs and/or peptide arginine content and positive charge, we will review studies using neuroprotective peptides in HIE models. First, we provide a summary of the known and potential neuroprotective actions of CARPs.
## 4 Discussion
#### Strengths and Limitations  
The relatively homogeneous group with MetS treated at the Metabolic Diseases Outpatient Clinic is the strength of our study. It is worth noting that the respondents did not come from all over Poland, but the study reflects the demographics and social diversity of the Polish population. Another strength was that the research was conducted face-to-face and not online. In case of doubt when filling in the questionnaire, the respondents could ask questions about points they did not understand, and the anthropometric measurements were carried out by a trained person and did not rely on the patient's self-assessment. In addition, the innovative approach used in this study should be emphasized, as there are few studies in this area on people with metabolic disorders. We used several measurements to assess hydration, including TBW, Sosm, HCT, Na, K, Uosm, and USG, and to assess health status, including SBP, DBP, WC, BMI, uric acid, and urea.  
There are several limitations to our study. First, our study sample was not large, and some may argue that it was not large enough to detect statistical significance. However, the sample size was in accordance with the guidelines set by the ethics committee. Secondly, due to the recruitment method used in the study, it was not possible to calculate the response rate and reasons for refusal. Therefore, it cannot be excluded that this study is aimed at a group of adults who are more likely to respond but included people with metabolic problems who came to the outpatient clinic. A certain limitation of the study was that the project plan did not include an assessment of the participants' food consumption, so there is no information on food consumption that could be an additional source of water for the respondents. The frequency of non-alcoholic beverage consumption was collected using the FFQ method, which may introduce errors in actual beverage consumption; however, the FFQ is a well-established, validated, and widely used questionnaire. Another limitation was the use of a body composition device that could only measure total body water without the ability to measure intracellular and extracellular water; however, these measurements are planned in future studies.
# Risk Identification of FPSO Oil and Gas Processing System Based on an Improved FMEA Approach
## 2 FPSO Oil and Gas Processing System  
As the most important component of the FPSO upper module, the FPSO oil and gas processing system roughly resembles its on-shore contemporaries, including oil-gas-water separation, associated gas processing, electric dehydration and desalination, production sewage processing, crude oil external measurement, chemical injection, heating medium, torch-venting systems, etc. The processing technology flow diagram of FPSO oil and gas processing system is shown in Figure 1.  
The submarine oil-well produces fluid by a single-point hose, which is heated to a certain temperature by a crude oil heat exchanger before it enters a primary (high-pressure) separator. There, with the addition of chemicals, the separation of oil, gas, and water can be initially realized. The separated gas is sent to the associated gas-processing system, and the separated oilly sewage enters the production sewage treatment system. The crude oil that is separated is heated to a certain temperature by the heat exchanger and the crude oil heater, and then enters the high-pressure separator for secondary separation. The gas generated after the second separation enters the associated gas processing system; the sewage enters the production sewage processing system; and the crude oil, after the secondary separation treatment, enters the tertiary separator again for separation. The crude oil is buffered, stabilized, and decompressed in the vessel after three separations,  
Figure 1: The processing technology flow diagram of floating production storage and offloading system (FPSO) oil and gas processing system.  
then heated and transported to the electric dehydration and desalination system to remove impurities such as emulsified water and inorganic salts in crude oil, to finally obtain relatively pure crude oil. The pure crude oil is sent to be measured at the crude oil external measurement system. After that, it is transported to the cargo tank of the FPSO for storage. The gas generated in the above process is uniformly transported to the associated gas processing system for separation, dehydration, decontamination, and purification, before the natural gas can be obtained. The purified natural gas can then be used as fuel for the FPSO itself, and the excess can be injected into the oil field or to the torch to vent or burn. The sewage generated in the process is collected in the production sewage processing system for filtration, sedimentation, oil-water separation, and other processes. The purified sewage can be directly discharged or can be injected back into the oil and gas field, while the non-standard sewage will be processed again until it is purified and then discharged into the sea.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 4 Parameter Influence of the Dynamic Voltage Feedback Circuit  
The influence of the parameters of the dynamic voltage feedback circuit on the turn-off characteristics of the IGBT at each stage during the turn-off process is shown in Table 1. Among them, \(\Delta U_{cc}\) is the difference between the off-voltage peak and the DC bus voltage, and \(R_{g}\) is the off-resistance.