esa-sceva/satcom-chunk-collection huggingface dataset:
# Sedimentary Model of Sublacustrine Fans in the Shahejie Formation, Nanpu Sag
## 3 Sample and Method  
All data utilized in this study were provided by the Exploration and Development Research Institute of PetroChina Jidong Oilfield Company. A systematic investigation was conducted on the distribution and characteristics of Es1 fan bodies in the northern Nanpu Sag through comprehensive analysis of drilling cores, full-coverage 3D seismic datasets (covering approximately 270 km2), lithological data, and well logging information. The methodology employed consisted of four main components: Firstly, detailed core examination and description were performed on 21.93 m of conventional coring samples (10 cm diameter) from three exploration wells within the study area. This included thin-section petrographic analysis to identify distinct lithofacies assemblages, sedimentary structures, and compositional characteristics. Secondly, high-precision synthetic seismograms (11.1 Hz dominant frequency) were used for seismic-well calibration, enabling accurate identification of stratigraphic interfaces through integration of 3D seismic data with log data. Thirdly, particular focus was placed on two key wells penetrating the target interval within the central depression zone NP4-68 and NP4-92, which served as primary evidence for fan body recognition. Additionally, structural elements of the fan systems were interpreted through horizontal distribution analysis of gamma ray (GR) and deep lateral resistivity (RLLD) logs from six strategically selected wells. The GR response with medium-low API values may indicate localized fluctuations caused by the incorporation of argillaceous debris. When the RLLD curve exhibits bell-shaped or funnel-shaped patterns, it reflects turbidite sand bodies characterized by poor vertical sorting. Finally, depositional facies types, including deltaic and gravity-flow fan systems, were identified through integrated analysis, supported by root-mean-square (RMS) attribute extraction to define their planar distribution limits. By synthesizing controlling factors, such as sediment supply dynamics, paleotopographic configuration, tectonic influences, and depositional processes, sublacustrine fan deposition models were established for the study area.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurries for Primary Cementing through Fractured Formations
#### 5.3.6 Gel-Cement Slurry  
Gel cement is defined as any type of Portland cement in which a certain percentage of bentonite has been replaced with the same amount of cement by weight, and the bentonite thoroughly and uniformly has mixed with the cement. Bentonite is added in concentrations up to 20% BWOC [44]. Adding bentonite affects the rheological properties of a cement slurry, reduces the density of the gel-cement slurry, and also reduces the strength of the set cement [3; 44].
## 3 Results and Discussion
### Photocatalytic Activity Evaluation
#### 3.5.1 Composition of the Rice Husk Extract before the Photocatalytic Reaction  
Figure 6 displays the total ion chromatogram (TIC) of the organic compounds detected in rice husk extracted before the photocatalytic reaction. Typically, a TIC shows peaks corresponding to compounds present in the analyzed sample; the graph represents the relationship between retention time in minutes and abundance of the compound in the sample. The GC-MS results of the rice husk extract before the photocatalytic reaction indicated that it contained a large fraction of carboxylic acid (41.33%), phenolic compounds (30.32%), and alcoholic compounds (10.12%). It also contained a smaller quantity of long-chain alkanes (5.06%), aldehyde compounds (3.2%), amine groups (0.41%), and some other compounds. Table 1 presents the GC-MS results, showing a list of identified compounds from rice husk extract before the photocatalytic reaction. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**RT (min)**} & **Name of the Compound Found in the Rice Husk Extract** & \multirow{2}{*}{**Area (\%)**} \\  & & **before the Photocatalytic Reaction** & \\ \hline \multicolumn{4}{c}{**Carboxylic Acids in Rice Husk Extract**} \\ \hline
1 & 12.03 & Benzoic Acid & 1.88 \\ \hline
2 & 17.00 & Monomethyl Ester, Nonanediolic Acid & 0.39 \\ \hline
3 & 21.84 & n-Hexadecanoic Acid & 12.23 \\ \hline
4 & 23.55 & Methyl Ester, Octadecenoic Acid & 0.45 \\ \hline
5 & 24.00 & 9-Octadecenoic Acid & 18.42 \\ \hline
6 & 27.62 & Oxiranylmethyl Ester, 9-Octadecenoic Acid & 3.49 \\ \hline
7 & 29.76 & 2,3-Dihydroxypropyl Ester, 9-Octadecenoic Acid & 1.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major compounds present in the rice husk extract before the photocatalytic reaction. Figure 6: Total ion chromatogram of the rice husk extract before the photocatalytic reaction. Figure 7 presents the compounds identified in rice husk extract according to their parental compound. The major fraction in rice husk consisted of carboxylic acids, phenols, alcohols and alkanes, aldehyde/ketones, and other compounds [33].
# Estimating Significant Wave Height from SAR with Long Integration Times
## 3 Experiments of Estimating Significant Wave Height from SAR with Long Integration Times
### Case 2-Multiple Ocean Waves from SAR with Long Integration Times
#### 3.2.1 Experiment of Significant Wave Height Estimation of the First Ocean Waves  
When \(\Delta V_{opt}^{1}=20\) m/s, the refocused SAR image of the first ocean waves is shown in Figure 9a. The corresponding wave spectrum is shown in Figure 9b.  
As shown in Figure 9a, it can be found that the contrast of ocean waves is greatly improved. Meanwhile, the wave spectrum is clearer in Figure 9b, which is more conducive to estimate significant wave height. Using the process shown in Figure 1, the wavelength \(\lambda_{p}^{1}=78.87\) m of ocean waves can be obtained. Due to \(\Delta V_{opt}^{1}>0\), the wave propagation direction after 180\({}^{\circ}\) ambiguity removal is \(\phi^{1}=191.24^{\circ}\). Finally, the significant wave height \(SWH^{1}=0.45\) m is calculated by Equation (19).  
Figure 8: Normalized peak-to-background ratio curve with focus setting.  
Figure 7: Azimuthal autocorrelation function and its Gaussian fitting results.
## 3 Materials and Methods
### Hardware and Software Used  
The hardware and software configuration used for the research experiments was oriented around a custom system for image classification using DL. The hardware part is described as a robust platform to handle the computationally intensive demands of the experiments. The system integrates an Intel Core i9-11900K CPU and massive support for neural network training provided by an ASUS NVIDIA RTX 2080 Ti GPU equipped with 11 GB of dedicated GDDR6 memory and CUDA compatibility. The system memory is 126 GB RAM. All hardware components run on a stable Ubuntu v22 operating system. These systems handle deep learning, multitasking, and parallel processing efficiently. This is important for dataset management and complex neural network architectures. On the software side, experiments of the present study were done using the Python v3.9 programming language, popular for machine learning development due to the wide support for libraries and frameworks effective in analyzing and manipulating data, digital images, and developing DL models. The PyTorch v2.0 library was used for specific deep-learning tasks, a modern and flexible tool for implementing neural network architectures. PyTorch's CUDA compatibility leverages GPU acceleration capabilities for fast optimization and training of deep learning models.
## 5 Conclusions  
In this study, we used a low-temperature plasma jet to inhibit potato germination and determined the suitable conditions for low-temperature plasma to inhibit potato germination. The main conclusions are as follows: (1) The effects of plasma treatment parameters on potato germination and weight loss content were: treatment voltage \(>\) treatment time \(>\) gas flow rate. In the plasma treatment of potatoes, the optimal treatment conditions were voltage 18.05 kV, treatment time 20.21 s, and gas flow rate 12.79 L/min. The germination rate of the treated potato was significantly reduced to 31.42%, the weight loss rate was reduced to 2.15%, and the potato was still able to maintain its normal physiological activity under these conditions. (2) Compared with the blank control group, the overall trend of SOD, CAT, POD, and PPO enzyme activities in the plasma-treated group was significantly higher than that of the control group after 16 d of storage, in which the SOD and CAT activities of the treated group increased by 52.63% and 29.27%, respectively, compared with the control; and the POD and PPO activities of the treated group increased by 8.69% and 18.58%, respectively, compared with the control group. (3) The plasma treatment group increased the hardness and brittleness of potatoes, and reduced the accumulation of sugars and dry matter consumption, thus maintaining the storage quality of potatoes. The hardness of the treated group increased by 6.06% compared with that of the control group; the brittleness of potatoes in the treated group decreased by only 24% within 16 d, while that of the control group decreased by 37.19%. Reducing sugars decreased by 32.56% compared to the control; dry matter increased by 7.66% compared to the control.  
Conceptualization, S.C.; methodology, S.C.; software, S.C.; validation, S.C. and J.L.; formal analysis, S.C.; investigation, S.C.; resources, X.W.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, S.C., J.L. and X.W.; visualization, S.C.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (31972144). Sponsor: [PERSON].  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author at [EMAIL_ADDRESS].
## 6 Design of Experimental Scheme and Result Analysis  
### Key Technological Solutions in the Stress Test of Impeller Blades  
As the impeller blades of the pump rotate rapidly with high fluid pressure, several key technological problems need to be solved to test the working stress of blades under the special operating conditions: the protection of strain gauges and wires, the lead wires arrangement and seals, and the connection of the rotation signals to be measured and stationary instruments.  
The protection of the strain gauges and wires under high water pressure, especially those on rotating components, are not only impacted by pressure load and temperature, but also by the inertia force and scouring force of water. Thus, strain gauges and wires without good protection may be separated from components, even leading to the failure of experiments. In this paper, we applied a coating to protect the strain gauges and wires. The coating method usually applies one or several layers of a chemical waterproof agent to the strain gauges and wires, which are characterized by good insulativity and bonding capacity, good plasticity, low elastic modulus, non-toxic and non-corrosive effects, stable pressure and temperature effects, solidification at room temperature, and convenience, etc.  
High-strength insulated wires can be used as wires for strain measurement in high-pressure water. No damage of the wires is allowed, and strict waterproofing treatment of connections between the strain gauges and the wires, and the wires and splices, should be conducted.  
In the experimental research, the most critical technical difficulty is the connection and transmission between stress test signals of impeller blades and test instruments in sealed and rotating conditions with pressure. In this condition, the conventional transmission modes, such as contact guyed, ring-brushing and mercury modes and non-contact induction and radio emission modes, cannot meet test requirements. As a result, a dynamic signal testing system characterized by a high accuracy, large capacity and offline collection and control is designed in this experiment, as shown in Figure 14. The standard deviation of the strain gauges is 2%, and the standard deviation of the wires is 0.5%. Therefore, the total standard deviation is 2.5%.
## 4 Experiments
### Image Classification
are then computed by repeating all the above procedures to the training and testing sets 10 times. Table 1 shows the comparative classification performance AMCR, Avg. Dim. (in the bracket) and AMF1-score (in the square bracket) with respect to the number of testing images selected (\(r=1\) to 9) for each individual in the standard part using the JAFFE face database. Note that both training and testing sets contain only standard images and thus, we shall evaluate the performance of various methods without non-standard images. As can be seen from Table 1, the proposed method generally performs better than the other methods. The TC-PCA usually achieves the highest AMCR and AMF1-score regardless of the number of testing images. Moreover, the TC-PCA provides a more effective way for dimensionality reduction than other PCA methods as it usually performs better than the classifier with all dimensions. This may not be the case for other PCA methods. Table 2 summarizes the AMCR and AMF1-score with respect to \(r\) using the Yale face database. We remark that the training and testing sets consist of both standard (i.e., normal face images) and non-standard images (i.e., face images under different lighting conditions). The percentages of non-standard images in the training set when \(r=1\), 2, 3 and 4 are 17.97%, 20.35%, 23.47% and 27.71%, respectively. In addition to reporting the overall AMCR and AMF1-score, we shall assess the performance of various methodologies in classifying non-standard and standard images. The following are some discussions and the main points we observe from Table 2.  
1. The overall AMCR and AMF1-score of TC-PCA is usually the highest among the PCA methods for any number of testing images. It is higher than other PCA methods up to 4.6% for both AMCR and AMF1-score. When the number of testing images is one (i.e., \(r=1\)), the proposed method performs better than the second best, OM-RPCA method by 2.4% for AMCR and 1.4% for AMF1-score. Besides, the overall AMCR and AMF1-score of any PCA method increases with the number of testing images and approximately converges to 81%. As the number of standard images increases (i.e., more testing images), the recognition problems are easier for most methods, which lead to higher overall AMCRs and AMF1-scores. For a smaller number of testing images, the portion of non-standard images are higher in the testing set and the method must learn more precise features to correctly classify the images. This shows that the proposed method learns more effective features than other PCA methods. 2. The TC-PCA outperforms other methods in classifying non-standard images up to 8.26% for AMCR and 7.2% for AMF1-score. These results experimentally justify that the proposed method can successfully discard information given by the non-standard images such as face images under
## 3 Nanomaterials in Various Diseases Management  
Nanomaterials have antimicrobial activity. Silver nanoparticles have anti-bacterial and anti-fungal properties. [PERSON] et al. [70] have reported the fungicidal effects of nano-silver against _Alternaria alternata_, _A. brassicicola_, _A. solani_, _Botrytis cinerea_, _Cidasosporium cucumerinum_, _Corynespora cassicola_, _Cylindocarcon destructans_, _Didymella bryoniae_, _Fusarium oxysporum f. sp. cucumerinum_, _F. oxysporum f. sp. lycopersici_, _F. oxysporum_, _F. solani_, _Fusarium sp._, _Gomerella cingulata_ and a few other fungi. [PERSON] et al. [71] showed the antifungal and antibacterial activity of AgNPs against _Erwinia sp._, _Bacillus megaterium_, _Pseudomonas syringe_, _Fusarium gramitearum_, _F. avenaceum_, and _F. culnorum_ fungi. [PERSON] et al. [72] reported the antibacterial activity of AgNPs against _E. coli_. [PERSON] et al. [73] reported the antibacterial activity of AgNPs against _Staphylococcus aureus_ and _Klebsiella pneumoniae_. [PERSON] et al. [74] reported that AgNPs have antibacterial activity against Gram-positive (_Bacillus subtilis_) and Gram-negative (_Escherichia coli_) bacteria. [PERSON] et al. [75] reported that AgNPs have antibacterial activity against food borne pathogens _Pseudomonas aeruginosa_, _Escherichia coli_, and _Bacillus subtilis_. [PERSON] and [PERSON] [76] reported that AgNPs show antifungal activity against _Alternaria alternata_, _A. citri_, and _Penicillium digitum_ fungi. [PERSON] et al. [77] found theentifungal activity of AgNPs against _Alternaria alternata_, _Macrophumina phaseolina_, _Botrytis cinerea_, _Sclerotiuin sclerotiorum_, _Curularia lunata_, and _Rhizzoctonia solani_ fungi. [PERSON] et al. [78] described the antifungal activity of AgNPs against _Bipolaris sorokiniana_ and _Magnaporthe grisea_ fungi. [PERSON] et al. [79] reported that AgNPs and a silver-chitoson composite show antibacterial activity against _Pseudomonas syringae_ pv. _syringae_ bacteria. [PERSON] et al. [80] reported that chitosan NPs have antifungal activity against _Macrophonia phaseolina_ and _Alternaria alterneta_ fungi. [PERSON] et al. [81] reported that chitosan NPs have antifungal activity against _Fusarium solani_ and _Aspergillus niger_ fungi. [PERSON] et al. [82] reported that AuNPs have antibacterial activity against _E. coli_ bacteria. [PERSON] and [PERSON] [83] observed that ZnO and AuNPs have antibacterial activity against _E. coli_ bacteria. The gold nanoparticles showed toxic effect on bacteria, _Salmonella typhimurium_, in which the macro gold did not exhibit. [PERSON] et al. [84] synthesized gold nanoparticles from _Abelmoschus esculentus_ and reported their antifungal activity. The antifungal activity of AuNPs was tested against _Puccinia graministrici_, _Aspergillus niger_, _Aspergillus flavus_ and _Candida albicans_ using the standard well diffusion method. The maximum zone of inhibition was observed in the Au NPs against _P. graminis_ and _C. albicans_.
## 3 Research Methodology
### Security Implications of OTP\({}^{3}\)
#### 3.3.2 Control Plane  
The control plane is dedicated to system management during the operational stage. Prior to initiating data transmission between two paired zero-trust modules, the modules are required to seek permission from the \"Zero-Trust Server\" (refer to Step 1 to 2 in Figure 4). Upon completing the transmission, the server needs to be notified again (refer to Step 7 to 8 in Figure 4). The zero-trust server maintains a record and monitors the frequency of grant transmissions within the communication pair to identify any anomalous behavior, such as brute-force (replay) attacks attempting to compromise the IoT ecosystem. The  
Figure 4: An OTP\({}^{3}\) operation flow for transmitting stream data between two paired devices.  
deployment of the control plane in an OTP\({}^{3}\) IoT cybersecurity system, as illustrated in Figure 2, is optional.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Physical Implementation Model  
Corresponding to the platform architecture, a typically physical implementation model is presented, which has four principal parts: the Web UI, main controller, emulation node and SDN switch, as shown in Figure 2.
# Use of Anionic Polysaccharides in the Development of 3D Bioprinting Technology
## 2 Overview of Natural Anionic Polysaccharides from Plant, Bacteria and Algae
### Alginate from Brown Algae and Bacteria
#### 2.3.2 Alginate from _Azotobacter Vinelandii_ and _Pseudomonas_ spp.  
Alginates are also synthesized as acetylated exopolysaccharideaccharides by certain bacterial species such as _Pseudomonas aeruginosa_ and _Azotobacter vinelandii_[54; 55]. During its vegetative growth _Azotobacter vinelandii_ synthesizes alginate and intracellular polyesters (polyhydroxybutyrate) [56; 57]. This alginate production is part of the enkystement process, as a mechanism for promoting resistance to desiccation under unfavourable environmental conditions in order to maintain hydration of the cell [58; 59] constituting a structural element of the cell [60]. In addition to this structural role that alginate plays in _Azotobacter vinelandii_ and due to its galuronic acid content, it can also act as an oxygen diffusion barrier, limiting its transfer to the enzymatic complex of nitrogenase [61], which is very sensitive to oxygen [62]. In _Pseudomonas aeruginosa_, alginate is the causative agent of cystic fibrosis, and is associated with pathogenic power [59; 63; 64]. Alginate biosynthesis in _Pseudomonas_ spp. is induced under desiccation conditions and is probably a key component of stable biofilms in various environments [58; 59]. The main difference at the molecular level between algal and bacterial alginates is the presence of \(O\)-acetyl groups at C2 and/or C3 of mannuronic acid unit in the bacterial alginates (Figure 3) [65].
# Simultaneous Transmission of Photonic Services over One Fiber with an ITU 100 GHz Grid
## 2 Related Works
Similarly, we also built the sensory system based on the phase optical time domain reflectometer (\(\Phi\)-OTDR) principle. Another benefit of this work is the practical implementation of measurements based on the WDM grid. Generally, 100 GHz is considered to be a safe between-services interval (spacing). Our measurement, however, proves that such spacing is sufficient only for certain sensory systems (limited by the power of the system). By evaluating the bit error rate (BER), the degree of interaction between the photonic services and the sensor system is demonstrated. The BER value is evaluated using Coriant GrooveTM G30 (manufactured by Infinera Corporation, Sunnyvale, CA, USA). This device is the only commercial product in our experimental setup.