esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 1 Introduction  
Faults, which are commonly found in sedimentary basins, can serve as conduits, barriers, or combined barrier-conduit structures for fluid migration in the subsurface [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The primary factors that influence their control based on fluid flow are their episodic seismic activity and sealing capacity during quiescence periods [18; 19; 20; 21; 22; 23; 24; 25]. Although faulting (reactivation) occurs for short geological periods and typically ceases as the tectonic activity stops, fault sealing plays a crucial role over long geological timescales [26; 27; 28], making it significant for hydrocarbon migration, accumulation, and other fluid-storage operations, such as carbon dioxide, hydrogen, and air.  
There are three major mechanisms of fault sealing: juxtaposition seal, fault rock capillary seal, and cementation [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. It is well-documented that even in reservoir/reservoir juxtaposition conditions, the low permeability of the fault gouge generated during the faulting process can effectively seal and support a high hydrocarbon column [44; 45]. Therefore, evaluating and predicting the composition and distribution of the fault gouge along the fault surface and thus the sealing potential of the fault will provide a solid foundation for analyzing hydrocarbon migration and accumulation [28]. For quantitatively predicting the volume and type of fault gouge within the fault zone, several algorithms have been developed, including the Clay Smear Potential (CSP) [33], Shale Water Factor (SSF) [35], and Shale Gouge Ratio (SGR) [36]. Empirically, a high clay content within the fault zone is associated with a high capillary threshold pressure, but calibration is still necessary toassess the fault sealing capacity [46; 47]. In addition, an associated geochemical analysis would help to reconstruct the hydrocarbon flow process [48; 49].  
Here, we illustrate the control of faults on hydrocarbon migration and accumulations in the extensional setting of the south western Pearl River Mouth Basin (PRMB), which is one of the most important petroliferous basins in the northern South China Sea [50; 51; 52; 53]. The fault geometries, activity history, and fault sealing potential are used to create a migration pathway model, which is then validated with the locations of verified hydrocarbon accumulations and used to predict potential unidentified accumulations.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 4 Two-Stage Detection  
Figure 7 shows the structure of MSC-AmBC detection at the reader. Specifically, the detection is composed of two stages which are designed for utilizing the inherent characteristics of the sources (i.e., OFDM pilot subcarriers) and tags (i.e., sparsity, duty-cycling operation). In the following subsections, we describe a series of signal processing techniques that enable successful detection of AmBC signals.
# Deep Learning-Based FSS Spectral Characterization and Cross-Band Migration
## 2 FSS Structure Design and Spectral Characterization Analysis
It can be seen that although their spectral curves were not exactly the same, the spectral curves of the two had a similarity in the trend. A similar conclusion can be obtained by changing the coding of the FSS. This means that FSSs with a certain geometry can have similar spectral characteristics in different electromagnetic wave frequency ranges by scaling their geometrical size. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Band**} & **Period** & **Discrete Cell** & \multirow{2}{*}{**Substrate**} & **Relative** & \multirow{2}{*}{**Substrate**} & **Metal** \\  & **Length** & & & & **Dielectric** & **Thickness** & **Conductivity** \\ \hline
5\(\sim\)30 GHz & 8 mm & 0.5 mm & FR-4 & 4.3 & 1 mm & 4.1 \(\times\) 10\({}^{7}\) \\
0.5\(\sim\)3 THz & 80 μm & 5 μm & HRS & 11.7 & 3 μm & 4.1 \(\times\) 10\({}^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of cross-band FSS model parameters. Figure 7: Cross-band comparison for the same FSS model. (**a**) GHz band Rogers5880, (**b**) GHz band without substrate, (**c**) THz band High resistance silicon, and (**d**) THz band without substrate. According to the above analysis, the influence law between the parameters of FSS and its spectral characteristics can be summarized as follows:  
1. When the metal thickness of the FSS was greater than its skin depth, the spectral characteristics of FSS with different metal types and thicknesses were similar;
2. Different thicknesses of substrates affected their equivalent dielectric constants, resulting in a shift in the resonant frequency of the FSS;
3. When the period length of FSS was scaled at the same time and the highest frequency of incident electromagnetic wave was the same multiple, the spectrum curves of FSS before and after scaling were similar for pure metal FSS with the same shape.
## 2 Materials and Methods
### Potential-Controlled Adsorption  
Maleic acid was utilized as a model analyte. For potential-controlled adsorption experiments, 6.5 mL of 5 mM maleic acid were adjusted to a pH of 7. A potential of +500 mV vs. OCP was applied during the adsorption phase for 15 min. After the adsorption phase, test rig and counter electrode were rinsed and cleaned extensively using DI-water so that only the effect of the working electrode would be observed and no remaining analyte on the test rig and counter electrode could influence the concentration measurement. DI-water at an adjusted pH of 7 was filled into the test rig to recover the model analyte. A potential of \(-\)500 mV vs. OCP was applied for 15 min for the desorption phase. Due to its strong UV absorbance maleic acid can easily be detected spectrophotometrically [31; 32; 36]. Hence, adsorbed and desorbed amounts of maleic acid were determined as triplicates by measuring the concentration in the supernatant through HPLC runs at a wavelength of 258 nm. Electrosporption experiments were conducted for three subsequent runs with washing steps for the monolith and test rig after each run to test the stability of the electrodes.
## 3 Mathematical Preliminaries and System Model
### FlyMesh System Model  
In this paper, the multi-hop FlyMesh was established to provide communication services for flight mobile terminals and ground mobile terminals, as shown in in Figure 1. The multi-hop FlyMesh system is composed of a flight base station (FBS), a flight mobile terminal (FMT), and a ground mobile terminal (GMT), which forms a complex network topology. Due to the rapid movement of drones, the topology structure will randomly change.  
The information randomly selected a link for data transmission. To facilitate the analysis, we assumed the information transmission at a certain instant, that is, the information was fixed on a particular link for communication. The nodes outside the link were all interference nodes. Simultaneously, UAVs were equipped with directional antennas to reduce the effect of signal interference [29], as shown in Figure 2. The transmission gain \(g^{t}\) and the receive gain \(g^{r}\) can be described separately as:  
\[g^{t}=1+\gamma\cos\theta, \tag{6}\] \[g^{r}=1+\gamma\cos\varphi, \tag{7}\]  
where \(\theta\) represents the transmission beam width and \(\varphi\) is the receiving beam width. \(\gamma\) is the degree of influence of beam width on antenna gain. Then, the gain of information transmission between the two nodes can be expressed as:  
\[G=\delta g^{t}g^{r}, \tag{8}\]  
where \(\delta\in(0,1)\) indicates the degree of alignment of the directional antenna. \(\delta\to 0\) means that the beams are parallel and no information can be sent between nodes, as shown in Figure 2c. \(\delta\to 1\) indicates that the beam is aligned, as shown in Figure 2a. Figure 2b indicates that \(\delta\) belongs to (0, 1) and its value adjusts with the degree of beam alignment.  
Figure 1: FlyMesh model.  
Figure 2: Three different alignment modes between beams: a, b, c.
## 2 Experimental Design
### Sorption System Design
#### Flow Rate and Configuration  
As a dual-stage system, the two columns are connected in a serial-flow arrangement, such that the first column receives the original stock with a higher HMI concentration and the second column receives the effluent from the first column. The upflow configuration ensures an overall better quality of flow, with a low liquid hold-up and a good stock feed distribution across the column cross sectional area. In contrast, for the downflow mode, an increase in pressure drop and flooding of the column bed is more probable [28]. Consequently, the stock is fed in an upflow direction to ensure proper and thorough distribution to the column beds and to minimize the need for backwashing and head loss effects.  
A critical parameter in the design process is the flow rate. Existing research demonstrates that lower flow rates result in high detention times in the column, which is needed due to the relatively slow uptake rate of zeolite [24; 27]. The HMI solution volume element is in contact with a given zeolite bed layer for only a limited period of time. Consequently, equilibrium is not usually achieved and thereby results in a lower overall uptake of HMIs from the influent stock solution. Preliminary testing involved a peristaltic pump, using the corresponding silicon rubber tubing. Significant back pressure was observed and the capacity of the peristaltic pump was hindered. Consequently, the required flow rate was unachievable; the rotational speed and strength decreased for the feed to completely traverse through the entire system. Subsequently a diaphragm-type metering pump (No. 950218125-C Plus, max 45-LPD, 80-psi, 125-AC, 50/60-Hz; PULAstron; Punta Gorda, FL, USA) is employed in the final design, which mechanically facilitates the desired stock feeding rate. Based on the 45-LPD (31.25 mL/min) capacity of the metering pump, preliminary flow rate testing of the pump set to 100% stroke (mechanically pumped volume) and 50% rate established an initial, repeatable, point-of-reference flow rate of 6.36 \(\pm\) 0.32 mL/min. This stroke-rate setting is maintained and is comparable to the lower end of the 6-18 mL/min range recommended by [PERSON] et al. [24; 27], to provide sufficient detention time in the system.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 2 Proposed Antenna Design Procedure
### Radiation Pattern in Terms of Gain  
In Figure 8, the simulated polar radiation patterns are compared for an antenna with and without a dielectric layer in the E-plane and H-plane in the 23.5 GHz frequency band. To efficiently benefit from the MIMO system, high decorrelation in the radiation patterns is a desirable feature. As shown in Figure 8, ports 1 and 2, the radiation pattern is well-de-correlated, indicating that one-antenna radiation results in low overlapping with the other one antenna within the same panel. This can be help to achieving sufficiently diverse performance. The total efficiency in the desired frequency band without a dielectric layer is 87.8%, whereas after incorporating a dielectric layer, the total efficiency is 89.6%. The gain measured at multiple frequency points for an antenna without a dielectric layer is compared with the simulated gain in Table 2.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis
### Evaluation Indicators  
The evaluation metrics used in this paper are the number of parameters (Params), the number of floating point operations (FLOPs), and the average precision (mAP). The mAP is calculated as shown in Equations (8) and (9), with recall R as the horizontal coordinate and correctness P as the vertical coordinate. The AP value of a single category is obtained by integrating the formed region, and the average value of all categories is mAP. [EMAIL_ADDRESS] is the average precision when the IoU threshold is equal to 0.5, [EMAIL_ADDRESS]: 0.95 is the average precision under the IoU threshold from 0.5 to 0.95. This index can evaluate the performance of the model more comprehensively; it is the average precision of small, medium, and large targets, respectively.  
\[AP=\int\limits_{0}^{1}P(R)dR \tag{8}\]  
\[mAP=\frac{1}{c}\sum\limits_{i=1}^{c}AP_{i}=\frac{1}{c}\int\limits_{0}^{1}\sum \limits_{i=1}^{c}P(R)dR \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Map** & **Feel the Wild** & **Anchor Frame** \\ \hline
20 \(\times\) 20 & Big & (138,219), (273,118), (315,308) \\
40 \(\times\) 40 & Medium & (53,31), (58,118), (315,308) \\
80 \(\times\) 80 & Small & (29,28), (23,63), (67,24) \\
160 \(\times\) 160 & Tiny & (10,11), (15,24), (25,13) \\ \hline \hline \end{tabular}
\end{table}
Table 1: K-means++ generates the prior box.
## 5 Proposed Equation  
To consider the effect of different loading conditions, the moment gradient factor is multiplied by the values of \(M_{ocr}\). In addition, the existing equations for calculating the moment gradient factor are taken from Equations (3)-(6) and correspond to the recommendations of AISC [1], BS 5950 [4], AS 4100 [2] and the study of [PERSON] et al. [17]. Moreover, Figures 9 and 10 show comparisons of the elastic critical buckling of beams with LC\({}_{1}\) and LC\({}_{2}\), respectively, between the FEA results and the existing equations. It can be seen that the estimated values using the recommendations of the standards AISC, BS5950 and AS4100 are conservative for most cases and tend to be too conservative as the \(L\)/\(n\) ratio increases. In addition, there are also a few cases of small values of \(L\)/\(n\) (i.e., \(L\)/\(n\) of 10, 15) with unconservative and acceptable results. Moreover, [PERSON]'s study provides too conservative results for all models, which increase as the \(L\)/\(n\) ratio increases, as shown in Figures 9d and 10d. Furthermore, the standards of AISC, BS 5950, AS 4100 and [PERSON]'s study give maximum differences for conservative values of 50.7%, 59.3%, 55.3% and 64.8%, respectively. Additionally, the maximum differences for unconservative values are \(-\)35.4%, \(-\)4.6% and \(-\)27.4% for AISC (2016), BS 5950 and AS 4100, respectively.  
In order to increase the accuracy, a proposed equation was developed for the elastic critical buckling of steel beams subjected to simultaneous transverse loadings at the top flange and negative end moments as follows:  
\[M_{cr}=F_{b}C_{b}M_{ocr} \tag{7}\]  
in which \(M_{ocr}\) is the elastic critical buckling of beams subjected to equal end moments and is calculated using Equation (1); \(C_{b}\) is given in AISC 2016 and in Equation (3) and has been widely used in previous studies; \(F_{b}\) is the factor considering the length-to-height ratio (\(L\)/\(n\)) and transverse loading on the top flange. Based on the FEA results, the proposal for \(F_{b}\) was  
Figure 8: Finite element analysis results for LC\({}_{2}\). (**a**) \(\alpha=0\). (**b**) \(\alpha=1\).  
Figure 7: Finite element analysis results for LC\({}_{1}\). (**a**) \(\alpha=0\). (**b**) \(\alpha=1\).  
developed as a set of linear expressions and is defined in Table 3, in which the expressions of \(F_{b}\) were developed for LC\({}_{1}\) and LC\({}_{2}\) based on the ratio between \(M_{1}\) and \(M_{2}\), including the cases of beams subjected to transverse loading on the top flange with one negative end moment (i.e., \(\alpha=0\)) and two negative end moments (i.e., \(0<\alpha\leq 1\)).  
Figure 10: Comparison of elastic critical LTB for LC\({}_{2}\) between FEA results and existing equations. (a) A3C [1], (b) BS 5950 [4], (c) AS 4100 [2], (d) [PERSON]’s study [17].  
Figure 9: Comparison of elastic critical LTB for LC\({}_{1}\) between FEA results and existing equations. (a) A3C [1], (b) BS 5950 [4], (c) AS 4100 [2], (d) [PERSON]’s study [17].
## 2 Materials and Methods
### Organic Acid Determination  
Approximately 0.5 g of frozen pulp was extracted two times with 5 mL aqueous 3% (\(w/v\)) metaphosphoric acid at room temperature for 30 min under periodical vortexing. At the end of each 30 min period, the sample was centrifuged, and the two supernatants were combined and filtered through a 0.45-\(\upmu\)m-pore nylon syringe filter. The analysis of organic acids took place with a Shimadzu Nexera X2 HPLC system equipped with an LC-30 AD pump and SPD-M20A diode array detector (DAD). The analysis was performed on a Li-Chrospher RP18 column (250 mm \(\times\) 4.6 mm, 5\(\upmu\)m, Merck Kenilworth, NJ, USA), eluted isocartically at a flow rate of 1 mL min\({}^{-1}\). The mobile phase consisted of 0.02% (\(v/v\)) formic acid in water. The organic acids were detected at 200 nm and identified based on their retention times. Citric, malic, fumaric, and ascorbic acid were detected, and the total organic acid concentration was also calculated. Final concentrations were expressed as mg g\({}^{-1}\) fresh weight.
_applied sciences_  
_Article_  
**Characterization of Ancient Burial Pottery of Ban Muang**  
**Bua Archaeological Site (Northeastern Thailand) Using**  
**X-ray Spectroscopies**  
**[PERSON] 1,2, [PERSON] 3,*, [PERSON] 3, [PERSON] 4, [PERSON] 5, [PERSON] 6 and [PERSON] 7,7t**  
1 Department of Physics and Materials Science, Faculty of Science, Chiang Mai University,  
Chiang Mai 50200, Thailand; [EMAIL_ADDRESS]  
2 Center of Excellence in Materials Science and Technology, Chiang Mai University, Chiang Mai 50200, Thailand  
3 Department of Earth Sciences, Faculty of Science, Kaestasart University, Bangkok 10900, Thailand; [EMAIL_ADDRESS]  
4 Synchrotron Light Research Institute, Nakhon Ratchasima 30000, Thailand; [EMAIL_ADDRESS]  
5 Plasma and Beam Physics Research Facility, Department of Physics and Materials Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand; [EMAIL_ADDRESS]  
6 Science and Technology Research Institute, Chiang Mai University, Chiang Mai 50200, Thailand; [EMAIL_ADDRESS]  
7 Independent Researcher, Lopburi 15000, Thailand; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]  
+Retired Army Officer at the Royal Thai Army.  
**Abstract**: Ancient potteries found at Ban Muang Bua, located in northeastern Thailand, associate with Thung Kula Ronghai culture. Most of them are products used in daily life and grave goods. The potshers were examined using techniques based on X-ray spectroscopy. Elemental composition and morphology were analyzed using proton-induced X-ray emission spectroscopy (PIXE) and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Three-dimensional analysis was performed using X-ray tomographic microscopy based on synchrotron radiation (SR XTM). Irregular plate-like particles of composites with a wide range of size distribution were found in the potshers. The major (O, Si, and Al), minor (C, Fe, Ca, and K), and trace elements (P, S, Ti, Na, Mg, and Zn) which were observed can provide the information about raw materials and production of pottery. The 3D tomographic images show the internal feature of these samples. The combination of SEM-EDS, PIXE, and SR XTM is a powerful tool for archaeological research especially in terms of composition and internal structure. The results imply that the raw materials of pottery were sandy soil derived from marine sands, clays, and salt deposits that were mostly iron-rich-kaolin clay. The production was carried out with low firing temperatures (\(\sim\)600-900 \({}^{\circ}\)C) in open-air kilns.  
**Keywords**: ancient burial pottery; Ban Muang Bua; SEM-EDS; PIXE; SR XTM
## 4 Discussion
### Description of Culturable Microbial Communities
_Pseudomonas hunanensis_ and _Peribacillus frigoritolerans_ were previously isolated in samples of Sahara dust plumes deposited as red rain in the southeast of Spain [16]. Plant and animal pathogens were found among the identified species. _Pantoea endophitica_ has recently been described as a plant pathogen, causing bacterial rot in tobacco plantations [75]. _Sanguibacter inulinus_ and _Sanguibacter keddieii_ are potential animal pathogens which were first isolated from blood in apparently healthy cows [76; 77]. Bacteria with detrimental effects on human health were also isolated from our samples. _Actinobacter lwoffii_ and _Niallia circulans_ are pathogen strains involved in human infections and especially in immunodeficient patients [78; 79]. Similarly, _Microoccus luteus_, _Enterobacter cancergetus_ and _Pseudescherichia vulgaris_ are opportunistic pathogens that cause infections in humans [80; 81]. Interestingly, _Actinobacter lwoffii_ was also identified in Saharan dust collected in Greece (the eastern Mediterranean) [63]. There is a pressing need for more extensive, long-term studies to elucidate the observed variations in airborne microbial communities worldwide. Given the current scenario of global climate change and the potential impact of microorganisms on recipient ecosystems (such as public health and agronomy), it is essential to foster new scientific advancements and collaborative alliances. This will enable us to investigate the transport and viability of specific microbes through diverse atmospheric routes and evaluate their persistence over time.
## 6 Conclusions  
In this study, the use of physical resource blocks in the radio access network in accordance with network slicing according to different applications was investigated to ensure end-to-end quality of service in 5G networks. The QoS type, bandwidth and numerology changes were analysed using the NS-3 simulation tool. In terms of RAN network slicing, the effects of using network slice-aware QoS algorithms, network slice-aware dynamic banding and numerology to meet different application needs are demonstrated. Future studies will investigate the network slice-aware dynamic QoS management and packet-processing mechanisms that have emerged as a result of this study.  
How to dynamically manage the quality of service of users by using SDN and NFV technologies together in 5G networks has also been investigated. An application was developed that detects congestion in the transmission network via the OpenDaylight SDN controller and requests network scaling from the NFV manager. In the case of network congestion, it was observed that 93.7% of the expected data throughput is achieved at the receiving end when using the multiple shortest paths traffic routing method. It was observed that when the SDN-NFV based dynamic network scaling method proposed in this study was used, expected data rates were achieved at a rate of 99.6%. According to these results, it was observed that the proposed method can be used to ensure continuity of service quality in 5G and beyond networks.  
Conceptualization and simulations were performed by A.C. and O.C. Writing, reviewing, editing, supervising were performed by A.K. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are obtained in the article.