esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contact Angles According to NP Size in the Three-Phase Rock-Oil-Aqueous System  
We observed changes in contact angle according to NP size in the three-phase rock-oil-aqueous system (Figure 7a). The contact angle was 120.5\({}^{\circ}\) when the NPs did not feature an aqueous phase (rock/oil/pure water). However, the contact angle increased to 142\({}^{\circ}\) as the CSNP size decreased (Figure 7b), indicating enhanced wettability caused by NP adsorption onto the rock surface. There are two possible reasons for these findings. First, NP diffusion in suspension increases as the particle size falls. The relevant equation is \(D=\frac{k_{B}T}{6\pi\mu\mu}\); the diffusion coefficient \(D\) is inversely proportional to the particle radius \(a\). \(k_{B}\) is the Boltzmann constant, \(T\) is the absolute temperature, and \(\mu\) is the viscosity.  
As the equation shows, smaller particles exhibit more Brownian motion, thus enhancing NP absorption onto the rock surface with the maintenance of between-particle spacing. Second, the NP number increases as particle size decreases at a fixed particle concentration; the CSNPs were of similar density (2.36 \(\times\) 10\({}^{-21}\) to 2.50 \(\times\) 10\({}^{-21}\) g/nm\({}^{3}\)). More particles adsorb to the rock surface creating a wedge film between the oil and the rock, thereby changing the contact angle on the rock surface. In theory, the disjoining pressure is the pressure required to remove fluid from the surface of a reservoir rock, thereby overcoming the adhesion force [12; 38]. The nanofluid film exerts a structural disjoining force between the oil and the rock surface because of an increase in NP entropy in the nanofluid, reducing oil adsorption to the rock surface. The oscillation amplitude of the film energy increases as the particle diameter decreases because more particles are pumped into the film by the entropic forces created by the confinement effect of the film [12; 39; 40]. Indeed, we found that the 12 nm FSNP dispersion exhibited the highest contact angle because it has the highest number of NPs (of all NPs tested) in the aqueous phase, although the difference in the contact angle was negligible compared with the difference of the 7 nm CSNP dispersion. These results are similar to the adsorption phenomenon of surfactant on the rock surface. Adsorption density increased with the increase of surfactant concentrations [41]. In the case of NP, the smaller the particle size, the higher the number of particles, thereby the adsorption density on the rock surface may increase with smaller NPs. Consequently, particle size affected the number of particles in the aqueous phase, which affected the contact angle with the rock surface.  
Figure 7: **(a)** Contact angles of differently sized NPs and (b) contact angles as a function of NP size.
## 3 Piperine: Human Clinical Trials
### Clinical Trials on Piperine Effect on Human Health
#### 3.2.3 Other Conditions  
Piperine has been applied as a bioavailability enhancer of curcuminoid preparations in several clinical studies (Table 2). In subjects suffering from chronic pulmonary complications due to sulfur mustard exposure and receiving a standard respiratory therapy, treatment with combination of piperine and curcuminoids for 4 weeks significantly improved St. George respiratory Questionnaire (SGRQ) and COPD Assessment Test (CAT) (total and subscale) scores, elevated GSH and reduced MDA, thus improving general health-related quality of life (HRQoL) [185]. [PERSON] et al. [186] studied the effectiveness of supplementation with a curcuminoid preparation containing piperine on measures of oxidative stress and inflammation in patients with metabolic syndrome. The results in the test group demonstrated improved serum SOD activities and reduced MDA and C-reactive protein (CRP) concentrations compared with placebo. With the aim of determining the impact of a single consumption of a CBFI (capsaicinoids, epigallocatechin gallate, piperin, and l-carnitine) on satiety, resting energy expenditure (REE), respiratory quotient, glucagon-like peptide-1 (GLP-1), free fatty acids (FFA) and glycerol release, [PERSON] et al. [188] conducted a clinical trial on 37 overweight adults, and the results showed improvement in all investigated parameters.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 5 Experimental Setup and Numerical Results
### Average Packet Collision versus Network Size and Network Load  
Figure 5(a),b demonstrate the numerical results of our study by taking into account packet collision/loss against network size and normalized throughput. In this case we also considered 50% and 100% of the theoretical maximum network capacity. In Figure 5(a) packet loss increased when the network size increased. This is due to the fact that when there are many VLNs in the network, the odds are that many packets are generated and transmitted. Therefore more collision can occur at high values of network size. In addition, the network load also contributed to packet loss/collision as it can be seen that there is more packet loss when the network load is 100% compared to 50%. Figure 5(b) shows that the average packet loss affects the throughput of the system. Thus packet loss decreases the throughput in both the 50% and 100% network load.  
Figure 5: Average delay versus network size and network load.
## 2 Related Work
### State-of-the-Art FEQS Methods
#### 2.1.2 Weighing FEQS  
As opposed to the imprecise method of counting trucks, weighing FEQS is a way of collecting statistics regarding the precise weight of material moved by the truck. This information is obtained from contact weighing tools, like the truck scale [13]. Weighing FEQS can obtain relatively accurate truck loading quantities and is applicable to scenarios that have a small amount of total transportation, multi-party contracting, or high unit load price, like earthmoving of small construction projects or highway freight charge [14; 15]. In these scenarios, the truck model, load types, and values can be complex. Also, managing different subcontractors requires separate financial settlements, resulting in the need for more precise statistics toward single truck transportation.  
The major class of limitations of weighing FEQS are problems imposed by the need for truck scale--a single truck scale costs between $35,000 to $100,000, has limited durability, and needs to be replaced after a given operating period [16], resulting in high deployment and maintenance costs. These scales also require trucks to first stop and then conduct the weighing, and can thus be a bottleneck that causes traffic jams during peak times [14], thus disturbing the truck flow and lowering overall transport efficiency [17].
## 2 Background
### Edge AI Hardware  
There is a diverse range of Edge AI hardware platforms in the market to meet the varied requirements and challenges of different edge computing applications such as performance requirements, power constraints, size and form factors and cost considerations. [PERSON] et al. [24] provided a comprehensive examination of hardware platforms utilized in Edge AI: AI Acceleration Units, which are specialized processors designed to accelerate AI and machine learning (ML) tasks, are typically optimized for specific computations like neural network operations. Intel Neural Compute Engine (Intel Corporation, Santa Clara, CA, USA), MediaTek AI Processing Unit (APU) (MediaTek Inc., Hsinchu City, Taiwan), Google Edge TPU (Google LLC, Mountain View, CA, USA), NVIDIA Deep Learning Accelerator (NVDLA) (NVIDIA Corporation, Santa Clara, CA, USA), Gyrfalcon Matrix Processing Unit (MPE) (Gyrfalcon Technology Inc., Beijing, China), Mythic M1076 Mythic AMP (Mythic Inc., Austin, TX, USA), Synstant Neural Decision Processors (Synstant Corp., Irvine, CA, USA), and Hailo AI Processor (Hailo, Tel Aviv, Israel) can be given as examples. Field-Programmable Gate Arrays (FPGAs) are flexible processors that can be configured to optimize AI tasks, allowing custom design for specific applications. Some examples are Intel MAX V CPLD (Intel Corporation, Santa Clara, CA, USA), Intel Cyclone 10 LP (Intel Corporation, Santa Clara, CA, USA), and Intel Cyclone 10 GX (Intel Corporation, Santa Clara, CA, USA). System-on-a-Chip (SoC) and System-on-Module (SoM) Devices integrate multiple components, including AI accelerators, into a single chip or module, often used in embedded and edge devices. Intel Movidius Myriad X (Intel Corporation, Santa Clara, CA, USA), HiSilicon Kirin 970 (HiSilicon Technologies Co., Ltd., Shenzhen, China), Qualcomm Snapdragon 855+/860: SoC (Qualcomm Technologies, Inc., San Diego, CA, USA), and MediaTek Helio P90 (MediaTek Inc., Hsinchu City, Taiwan) can be given as examples. Specialized (Dedicated) AI Modules are AI-specific modules designed to be added to various systems or used for development purposes. Google Coral Accelerator Module (Google LLC, Mountain View, CA, USA), Gyrfalcon MPE (Gyrfalcon Technology Inc., Beijing, China), and Mythic M1076 (Mythic Inc., Austin, TX, USA) can be provided as examples to this category. Development Boards (e.g., BeagleBone AI (BeagleBoard.org Foundation, Austin, TX, USA), OpenMV Cam (OpenMV, San Francisco, CA, USA), SparkFun Edge Development Board (SparkFun Electronics, Niwot, CO, USA), Syntiant Tiny Machine Learning Development Board (Syntiant Corp., Irvine, CA, USA), and STMicroelectronics STM32 (STMicroelectronics, Geneva, Switzerland)) are designed for prototyping, development, and testing of AI applications and solutions. NVIDIA devices (NVIDIA Corporation, Santa Clara, CA, USA) are another family of single board computer systems used in Edge AI applications. NVIDIA devices utilize GPUs for AI and machine learning tasks. Examples include Jetson Nano (NVIDIA Corporation, Santa Clara, CA, USA), Jetson TX2 (NVIDIA Corporation, Santa Clara, CA, USA).
## 6 Dimensionality Reduction  
In this section, we show the dimensionality reduction aspects related to our automated system design methodology. There are many available methods used for dimensionality reduction, _i.e._, principle component analysis (PCA), linear discriminant analysis (LDA), projection pursuit, multi-dimensional scaling (MDS), _etc._[18, 58]. A special case of dimensionality reduction described here is feature selection. Feature selection is a method to find minimum feature subset giving optimum discrimination between two or more defined groups of objects. This method is an iterative algorithm, also called Automatic Feature Selection (AFS). This approach is applied for optimized sensor system design by reducing or discarding the irrelevant or redundant features or groups of features, which are  
Figure 20: Visual inspection of four gases data: (a) raw sensor signals and (b) extracted by evolved Gaussian kernels.  
extracted by feature computation methods at previous stage. Moreover, the sensorial effort will be saved due to efficient selection. Figure 21 describes the elimination process of unnecessary features or groups of features, as well as sensors.  
Basically, the AFS can be divided into two groups, _i.e._, (1) wrapper method that is performed dependently of the learning algorithm or classifier (e.g., RBF, SVM, _etc._); and (2) filter approach that utilizes various statistical techniques underlying training data and operates independently of the classifier [51]. In general, the wrapper method provides selected features that lead to more accurate classifications than that of the filter method. However, the filter method executes many times faster than the wrapper method.
## 7 Evaluation and Discussion
### Experimental Setup
#### 7.1.3 Runtime Measurement  
Since some of the protected code segments run for a short time, it is very important to gather accurate runtime information. For this reason, we use hardware performance counters to gather runtime information. This process is described in Table 2. To determine how many runs are needed to reduce noise during program execution, we use a 95% confidence interval to compute the confidence range after each run. When the difference between the upper and lower confidence intervals is less than 2%, the program can be stopped running.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**APP\_Name**} & **Key-Code Segment** & **Length of** & **File Size** & **Execution Time of** \\  & **Description** & **Key-Code Segment** & **(Byte)** & **Key-Code Segment (\#s)** \\ \hline \multirow{2}{*}{Calculator.exe} & Multiply operation algorithm & \multirow{2}{*}{48} & \multirow{2}{*}{2,265,190} & \multirow{2}{*}{32} \\  & in Windows Calculator. & & & \\ \hline Compress.exe & File Compression algorithm. & 110 & 229,447 & 1970 \\ \hline \multirow{2}{*}{Csnake.exe} & Positioning algorithm & \multirow{2}{*}{60} & \multirow{2}{*}{233,563} & \multirow{2}{*}{0.34} \\  & in game snake. & & & \\ \hline \multirow{2}{*}{Hanio.exe} & Hanio algorithm, and & \multirow{2}{*}{82} & \multirow{2}{*}{548,942} & \multirow{2}{*}{39,750} \\  & the plate number is 5. & & & \\ \hline \multirow{2}{*}{Hviewer.exe} & Response message algorithm & \multirow{2}{*}{4} & \multirow{2}{*}{2,195,556} & \multirow{2}{*}{86} \\  & in simple Browser. & & & \\ \hline \multirow{2}{*}{Ipmsg.exe} & Message sending algorithm & \multirow{2}{*}{363} & \multirow{2}{*}{417,869} & \multirow{2}{*}{4139} \\  & in IP Messenger. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test cases used in _VMGuards._  
\begin{table}
\begin{tabular}{l} \hline \hline _QueryPerformanceCounter(\(\diameter\)Start\_Time); //start time_ \\ _VMGuards\_Start_ /key code start flag_ \\ _Key-Code Segment_ \\ _VMGuards\_End_ /key code end flag_ \\ _QueryPerformanceCounter(\(\diameter\)End\_Time); //end time_ \\ _//CPUI counter numbers_ \\ _CPUCounter\_Num = End\_Time.QuadPart-_ \\ _Start\_Time.QuadPart;_ //Clock Frequency of CPU timer_ \\ _QueryPerformanceFrequency(\(\diameter\)freq);_ //The execution time of Key-Code Segment, accurate_ \\ _down to \(\mu\)s_ \\ _i64 toa(C*1000000)/freq.QuadPart, szBuf, 10);_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time of key-code segments.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 4 Resolutions and Satellite Vibration
### Range Resolution  
Chirp rates are utilized to estimate range distance. Suppose that the range distance of target 1 is \(R_{1}\) with chirp rate \(K_{1}\) and the range distance of target 2 is \(R_{2}\) with chirp rate \(K_{2}\).  
\[K_{1}=\frac{f_{0}v^{2}}{cR_{1}},K_{2}=\frac{f_{0}v^{2}}{cR_{2}}. \tag{23}\]  
The envelope of range-matched filtering for chirp rates is a Fresnel function. The bandwidth of 3 dB is \(\rho=3.4743/T_{s}^{2}\). The range resolution \(\rho_{r}=\mid\)\(R_{1}-R_{2}\)\(\mid\) can be calculated as follows:  
\[\begin{split}\frac{K_{1}}{2}-\frac{K_{2}}{2}&= \frac{f_{0}v^{2}}{2 cR_{1}}-\frac{f_{0}v^{2}}{2 cR_{2}}=\frac{3.4743}{T_{s}^{2}},\\ \rho_{r}&=\mid\!\!R_{1}-R_{2}\!\!\mid\approx\frac{6. 9486 cR_{0}^{2}}{f_{0}v^{2}T_{s}^{2}}=\frac{6.9486f_{0}D^{2}}{k^{2}c}.\end{split} \tag{24}\]  
Figure 4: The principle of virtual synthetic aperture radiation source location method.  
As a result, the range resolution is constant for the same antenna and source signal. For different sources with the same antenna, the higher center frequency causes worse range resolution. However, if we process only part of the received data, the range resolution will change. Suppose that the length of the captured data is \(t_{s}\), and the range resolution is  
\[\rho_{r}(t_{s})=\frac{6.9486 cR_{0}^{2}}{f_{0}v^{2}t_{s}^{2}}. \tag{25}\]  
Therefore, the longer the virtual aperture is, the better the range resolution will be.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Experimental Platform Overview
#### 4.1.1 L-30A UAV System  
The L-30A UAV system, as shown in Figure 11, consists of variable-sweep wing UAVs, a ground control station (GCS), and a data link, all designed to execute cooperative multi-UAV tasks. The L-30A UAV dynamically adjusts its sweep angle to optimize aerodynamic performance, while its integrated sensors, including an inertial measurement unit (IMU), a global positioning system (GPS), and high-resolution cameras, provide real-time data for position, velocity, attitude angles, and sweep angle. The system uses a data link to enable high-speed, low-latency communication between the UAVs and the GCS, facilitating the transmission of flight status, sensor data, and control commands. The GCS acts as the command center, receiving and analyzing UAV data in real time, allowing for mission control and flight adjustments, while the UAV formation maintains coordination autonomously to ensure precise execution of cooperative tasks.
###### Abstract  
A theoretical model of the nonlinear signal-to-noise interaction (NSNI) in a bi-directional Raman amplified system with receiver-side digital back-propagation (DBP) or split-DBP is given, which is helpful for the design of such a system. In the proposed model, the distributed Raman gain and the spontaneous Raman scattering are taken into account. The results of the theoretical calculation are compared with the results of transmission simulations, which indicates that the theoretical model matches well with the results of simulations when the pre-compensation length is less than 100 km. For the cases of pre-compensation lengths more than 100 km, the theoretical model has an error of less than 0.1 dB compared with the simulations. By using the theoretical model, the efficiency of the split-DBP is analyzed, and the results are compared with transmission simulations. Both the results of theoretical calculation and simulations show that the split-DBP can effectively mitigate the NSNI in such a system. By adopting split-DBP, with an appropriate pre-compensation length, the signal-to-noise ratio (SNR) of the signal increases by about 1 dB. In addition, the impact of the double Rayleigh scattering (DRB) is also analyzed using the proposed model, and the results show that DRB has little impact on the system.  
optical fiber communications; digital back-propagation; Raman amplification; nonlinear signal-to-noise interaction; nonlinearity mitigation +
Footnote †: journal: Applied Physics Letters B
## 4 Evaluation of Industrial Applicability
### Case Study  
The case study was realized in partnership with an institution that offers AM part production using printers for MJF. Here, an HP Jet Fusion 4200 printer was used to fabricate parts from PA 12 (Nylon 12). In addition, the part post-processing included powder removal, sandblasting, optional dyeing, and sorting of the AM parts for customer pickup. The facility provides MJF production as a service to universities. Two to three build jobs are produced per week, and the facility can produce approximately 60-100 parts each week. The part geometries generally differ between build jobs.  
For this case study, the proposed workflow was set up and the parts were recognized automatically using the sorting system (Figure 12). Six different build jobs were analyzed, including a total of 519 printed AM parts with 215 distinct geometries, meaning that six different classification networks were trained. The system's ability to accurately recognize parts was analyzed. For this, the top-one and top-three part classification accuracies were measured. Accuracy is defined as the fraction of correct part classifications by the neural network. For top-one accuracy, the highest ranked network prediction must match the true part class, while for top-three accuracy, the three highest ranked network predictions must  
Figure 11: Comparison of the set classification accuracy obtained on the test set with different numbers of camera positions in the rendering scene.  
match the true class. As the proposed GUI presents the three highest network predictions (Figure 6), top-three accuracy was also analyzed.  
To acquire information about the impact of the automated part recognition process on the sorting times, two build jobs were sorted once manually and once using automated part recognition, and the total sorting times were measured. The two build jobs involved 100 parts and 63 distinct geometries.
# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
###### Abstract  
Carbon dioxide (CO\({}_{2}\)) is currently the most harmful greenhouse gas in the atmosphere. Obtaining long-term, high-resolution atmospheric column CO\({}_{2}\) concentration (XCO\({}_{2}\)) datasets is of great practical significance for mitigating the greenhouse effect, identifying and controlling carbon emission sources, and achieving carbon cycle management. However, mainstream satellite observations provide XCO\({}_{2}\) datasets with coarse spatial resolution, which is insufficient to support the needs of higher-precision research. To address this gap, in this study, we integrate spatial information with the extreme random trees model and develop a new machine learning model called spatial extreme random trees (SExtraTrees) to reconstruct a 1 km spatial resolution XCO\({}_{2}\) dataset for China from 2016 to 2020. The results indicate that the predictive ability of spatial extreme random trees is more stable and has higher fitting accuracy compared to other methods. Overall, XCO\({}_{2}\) in China shows an increasing trend year by year, with the spatial distribution revealing significantly higher XCO\({}_{2}\) levels in eastern coastal regions compared to western inland areas. The contributions of this study are primarily in the following areas: (1) Considering the spatial heterogeneity of XCO\({}_{2}\) and combining spatial features with the advantages of machine learning, we construct the spatial extreme random trees model, which is verified to have high predictive accuracy. (2) Using the spatial extreme random trees model, we reconstruct high-resolution XCO\({}_{2}\) datasets for China from 2016 to 2020, providing data support for carbon emission reduction and related decision making. (3) Based on the generated dataset, we analyze the spatiotemporal distribution patterns of XCO\({}_{2}\) in China, thereby improving emission reduction policies and sustainable development measures.  
XCO\({}_{2}\); spatial downscaling; SExtraTrees; high resolution; spatial features +
Footnote †: journal:  
Article  
XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees