esa-sceva/satcom-chunk-collection huggingface dataset:
# NMR Evaluation of Shale Oil Mobility: Combined Pyrolysis and CO\({}_{2}\) [PERSON]
###### Abstract  
The occurrence and mobility of shale oil are critical issues in exploration and development. Shale reservoirs exhibit a complex fluid state, with oil and water present in various forms. The presence of organic matter and clay minerals within the reservoir framework further complicates the fluid's occurrence and mobility. Utilizing two-dimensional nuclear magnetic resonance (NMR) experiments, in this study, core samples from the Shengli Oilfield's shale oil reservoirs were analyzed. We conducted pyrolysis-NMR and CO\({}_{2}\) huff-n-puff-NMR joint measurement experiments to assess the shale oil mobility. The results indicated that CO\({}_{2}\) huff-n-puff was the most effective in the initial cycle, with diminishing returns in subsequent cycles, and NMR signal changes were predominantly observed in the movable oil fraction. The selected samples showed an average recovery rate of 26.9%, suggesting good mobility of shale oil in the study area. Based on the experimental results, a fluid component identification template for the study region was established, which mainly consists of the following five parts: movable oil, adsorbed oil, asphaltene, clay-bound water, structural water, and kerogen. This research provides valuable insights for the efficient development of shale oil reservoirs.  
shale oil; nuclear magnetic resonance (NMR); CO\({}_{2}\) huff-n-puff; pyrolysis; mobility +
Footnote †: journal: applied sciences
# A New Instrument Monitoring Method Based on Few-Shot Learning
## 2 Methods and Dataset
### Data Annotation Methods  
In addition to generating instrument images with different permutation patterns for the simulation, FADGAM can automatically calculate and generate annotation files (.txt format) for each simulation image. Each annotation file is named the same as the simulation image name, where each row has the information of one annotation box, in the order by column are: category, \(x_{c}\), \(y_{c}\), \(w\), and \(h\). Since the \(x_{n}\) and \(y_{n}\) parameters of each generated number are pre-known parameters during the image simulation. Furthermore, the category is the class of the generated number, which should be equal to the image name, all parameters in the annotation file can be calculated automatically as Formulas (3)-(6):  
\[x_{c}=x_{n}/W_{image} \tag{3}\]  
\[y_{c}=y_{n}/H_{image} \tag{4}\]  
\[w=w_{box}/W_{image} \tag{5}\]  
\[h=h_{box}/H_{image} \tag{6}\]  
in which, the \(W_{image}\) represents the width of the whole image, the \(H_{image}\) represents the height of the whole image, the \(w_{box}\) represents the width of the annotation box, the \(h_{box}\) represents the height of the annotation box, \(x_{c}\) represents the relative scale of the x-center of annotation box in the image, \(y_{c}\) represents the relative scale of the y-center of annotation box in the image, \(y_{n}\) represents the y-coordinate of the center of each annotation box, and \(x_{n}\) represents the x-coordinate of the center of each annotation box. The simulation images generated by FADGAM and their corresponding annotation files will be input together  
Figure 3: The flow chart of FADGAM.  
as the pre-training files of Improved YOLOv5, which can greatly reduce the reliance on human labor since no manual annotation is required in this process.  
A comparison of the annotation time between FADGAM and human labeling is shown in Figure 4. Since the FADGAM is designed to automatically generate labels based on simulated images, the FADGAM takes an average of only 0.03 s per image to label. For the human labeling time or human detection time, we invited four human experts to conduct the test and calculated an average labeling time of 22.925 s per image. Compared with pure human labeling, FADGAM effectively improves labeling efficiency by more than 99.87%, which is calculated by \(\left(Time_{Human}-Time_{FADGAM}\right)/Time_{Human}\)[24].
## 3 Experimental Setup and Result Analysis
### Experimental Results
#### 3.4.1 Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline MobileNetV3 & 87.282 & 85.26 & 86.006 & 85.041 \\ MobileNetV3\_CA & 92.768 & 91.834 & 90.946 & 91.298 \\ MobileNetV3\_SCA & 93.267 & 93.226 & 92.011 & 92.5 \\ MobileNetV3\_SK & 87.78 & 86.5 & 84.49 & 84.24 \\ MobileNetV3\_Strategy & 90.773 & 88.89 & 90.05 & 89.177 \\ MobileNetV3\_All & 94.015 & 92.338 & 93.537 & 92.777 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ablation results of the dataset at 400\(\times\) magnification factor (%). model has 0.32 million more parameters than the benchmark model, but it significantly improves performance metrics such as accuracy. Specifically, the benchmark model has 233.56 million FLOPs and 4.21 million parameters. The introduction of the CA mechanism increased the number of parameters by only 0.05 million. After incorporating the improved SCA mechanism, the number of parameters increased by just 0.11 million compared to the benchmark model. The use of the multi-stage information fusion strategy added only 0.13 million parameters. Consequently, the total number of parameters for the proposed model is 4.53 million, which maintains a good balance between computational complexity and the number of parameters. The experimental results demonstrate that the various improvement mechanisms implemented in the proposed model enhanced its accuracy without significantly increasing the computational complexity or the number of parameters, thus keeping the model lightweight and efficient.
## 4 Conclusions  
The results clearly demonstrate the application potential of an immobilized _Ochrobactrum_ sp. CUST210-1 biosystem and an immobilized ChrR biosystem. The _Ochrobactrum_ sp. CUST210-1 biosystem demonstrated good adaptability to pH variation and was able to operate at a long LRT with relatively concentrated Cr(VI), but it had a relatively high cost. The _chrR_ gene was identified in _Ochrobactrum_ sp. CUST210-1; thus, the ChrR protein is responsible for Cr(VI) reduction and results in large amounts of Cr(OH)\({}_{3(8)}\). The ChrR biosystem exhibited good adaptability to the operating temperature and was able to operate at a short LRT with relatively diluted concentrations, and it was relatively cheap. The _Ochrobactrum_ sp. CUST210-1 biosystem is recommended for tannery wastewater treatment, especially for a posttreatment system. The immobilized ChrR biosystem is recommended for electroplating wastewater treatment as a posttreatment system or in preparation for meeting stricter water quality standards in the future. Through economic analysis and a comparison of operation characteristics, we provided insight into strategies for removing Cr(VI) from various environments.  
All authors collaborated to carry out the work presented here. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] wrote the paper; and [PERSON], [PERSON], and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and Technology of Taiwan, grant numbers MOST 107-2313-B-157-001-MY2 and MOST 109-2313-B-157-001.  
We thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for helping with partial paperwork and data processing. This manuscript was edited by Wallace Academic Editing.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Bacterial Culture**} & \multicolumn{1}{c}{**Enzyme Extraction \& \& \& **Reactor Installation**} & \multicolumn{1}{c}{**Operational Costs**} & \multicolumn{1}{c}{**Application Scope**} \\  & **(USD/y)** & **Immobilization (USD/y)** & **(USD/y)** & **1 (USD/y)** & **Application Scope** \\ \hline \hline \multirow{4}{*}{CUST210-1} & \multirow{4}{*}{350} & \multirow{4}{*}{-} & \multirow{4}{*}{6000 2  } & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & & & & (2) Inlet Cr(VI) conc: \\ \cline{1-1} \cline{
## 3 Evaluation Results and Discussion
### Evaluation of Smart System Using Product Quality Model  
In this section, the product quality model is used to analyze the performance of the smart system. The feedback from the testing was systematically used as input information for the product quality model. This input includes observations of the experts participating in testing the tool (see Table 9). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Functional Features**} & \multirow{2}{*}{
\begin{tabular}{c} **Main Actor Respons** \\ **for the Activity** \\ \end{tabular} } & \multicolumn{3}{c}{**Satisfaction Level (Ease of Use of the** **Tool and Precision of Results) * **} \\ \cline{2-5}  & & **1** & **2** & **3** & **4** & **5** \\ \hline Data entering (uploading) onto & & & & & \\ web-based platform & Biomass producer & & & & \\ \hline Data recording by smart box & Transporter & & & & \\ \hline Data transfer from smart box to & & & & & \\ central database & & & & & \\ \hline Searching for available product & & & & & \\ and ordering for purchase & & & & & \\ \hline Whole pruning supply chain & & & & & \\ management support & & & & & \\ \hline Product quality monitoring and & & & & & \\ traceability capability & & & & & \\ consumer & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Analysis results of overall satisfactory level of major functionalities. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Column Header**} & \multicolumn{3}{c}{**Correctly Recorded Values of Registered Products for Which Route**} \\ \cline{2-4}  & & & \multicolumn{3}{c}{**Planning Was Done (\%)**} \\ \cline{2-4}
**as Implemented in** & **Sweden** & **Spain** & **Germany** & **Relevance Level of the Parameter** **for Traceability Information** \\ \cline{2-4}
**Central Database** & **N = 21** & **N = 31** & **N = 4** & **Relevance Level of the Parameter** **for Traceability Information** \\ \hline title & 100 & 100 & 100 & Mandatory \\ \hline Status & 100 & 100 & 100 & Supporting information \\ \hline providerID & 100 & 100 & 100 & Mandatory \\ \hline transporterID & 100 & 100 & 100 & Mandatory \\ \hline consumerID & 100 & 100 & 100 & Mandatory \\ \hline ergologSerialNo & 67 & 0 & 75 & Supporting information \\ \hline deliveryCode & 91 & 68 & 100 & Very important \\ \hline lotNumber & 100 & 100 & 100 & Mandatory \\ \hline pickupDate & 67 & 0 & 75 & Supporting information \\ \hline deliveryDate & 67 & 0 & 50 & Very important \\ \hline modifiedByUserID & 100 & 100 & 100 & Supporting information \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Major parameters for traceability information continuity and their recording performance
## Appendix A  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Delay Increase \(\geq\)1 min**} & \multicolumn{3}{c}{**Delay Decrease \(\geq\)1 min**} \\ \cline{2-7}  & **Estimate** & **Std. Error** & **z Value** & **Estimate** & **Std. Error** & **z Value** \\ \hline (Intercept) & -0.64 & 0.001 & -494.79 & -0.51 & 0.001 & -427.69 \\ \hline Trackwork (No = 0) & 0.36 & 0.007 & 54.55 & -0.12 & 0.006 & -18.94 \\ \hline Track type & Quadruple & -0.45 & 0.002 & -209.33 & 0.21 & 0.002 & 136.90 \\ (Double = 0) & Single & 0.05 & 0.001 & 52.71 & 0.06 & 0.001 & 72.75 \\ \hline \multirow{7}{*}{Train type (Freight train = 0)} & Unspecified passenger & 0.03 & 0.005 & 5.52 & -0.40 & 0.004 & -97.97 \\  & InterCity & -0.09 & 0.002 & -44.60 & -0.36 & 0.002 & -188.71 \\ \cline{1-1}  & Airport & 0.41 & 0.003 & 122.82 & -1.81 & 0.004 & -479.89 \\ \cline{1-1}  & Commuter & -0.61 & 0.002 & -358.20 & 0.26 & 0.001 & 183.37 \\ \cline{1-1}  & Regional & -0.19 & 0.001 & -131.76 & -0.29 & 0.001 & -226.32 \\ \cline{1-1}  & High-speed & 0.01 & 0.002 & 3.75 & -0.71 & 0.002 & -437.15 \\ \hline Departure status & Late & -0.59 & 0.001 & -509.74 & 0.67 & 0.001 & 635.42 \\ (Early = 0) & On time & -0.64 & 0.001 & -463.72 & 0.47 & 0.001 & 387.51 \\ \hline Day time (Day = 0) & Night & -0.01 & 0.001 & -5.62 & -0.05 & 0.001 & -42.09 \\ \hline \hline \end{tabular} All coefficients are significant at the 0.1% level.  
\end{table}
Table 1: Multiple logistic regression model summary. Response variable: train running time delay increase/decrease \(\geq\)1 min (0; 1).  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Delay Increase \(\geq\)1 min**} & \multicolumn{3}{c}{**Delay Decrease \(\geq\)1 min**} \\ \cline{3-8}  & & **Estimate** & **Std. Error** & **z Value** & **Estimate** & **Std. Error** & **z Value** \\ \hline (Intercept) & & -1.01 & 0.005 & -206.13 & -0.99 & 0.005 & -216.53 \\ \hline Trackwork (No = 0) & & 0.15 & 0.011 & 13.20 & -0.04 & 0.010 & -4.10 \\ \hline Track type & Quadruple & -0.05 & 0.010 & -4.90 & -0.25 & 0.010 & -26.39 \\ (Double = 0) & Single & 0.06 & 0.004 & 14.55 & 0.05 & 0.003 & 13.85 \\ \hline \multirow{4}{*}{Train type (Freight train = 0)} & Unspecified passenger & -0.09 & 0.008 & -10.63 & -0.14 & 0.007 & -19.41 \\  & InterCity & -0.17 & 0.006 & -30.17 & -0.12 & 0.005 & -23.25 \\ \cline{1-1} Train type & Airport & 0.03\({}^{\text{ms}}\) & 0.024 & 1.25 & -1.00 & 0.023 & -42.86 \\ (Freight train = 0) & Commuter & -0.49 & 0.007 & -69.58 & 0.14 & 0.006 & 21.69 \\ \cline{1-1}  & Regional & -0.28 & 0.005 & -58.86 & -0.08 & 0.004 & -17.98 \\ \cline{1-1}  & High-speed & -0.16 & 0.006 & -25.05 & -0.26 & 0.006 & -43.18 \\ \hline Departure status & Late & -0.27 & 0.004 & -65.72 & 0.23 & 0.004 & 58.85 \\ (Early = 0) & On time & -0.35 & 0.005 & -73.25 & 0.17 & 0.004 & 38.67 \\ \hline Day time (Day = 0) & Night & -0.02 & 0.004 & -4.47 & 0.00\({}^{\text{ms}}\) & 0.004 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Negative binomial regression model summary. Response variable: train running time delay increase/decrease count \(\geq\)1 min.
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 2 Quasi-Static Approach for the Evaluation of Tire Forces
### Aerodynamic Forces and Moments  
Based on the quasi-steady theory, aerodynamic forces and moments induced by side winds can be expressed as shown in Equations (15) and (16).  
\[F_{D}\ =\ C_{D}A\frac{\rho V^{2}}{2}F_{S}\ =\ C_{S}A\frac{\rho V^{2}}{2}F_{L}\ =\ C_{L}A\frac{\rho V^{2}}{2} \tag{15}\]  
\[M_{R}\ =\ C_{R}Al_{tc}\frac{\rho V^{2}}{2}M_{P}\ =\ C_{P}Al_{tc}\frac{\rho V^{2}}{2}M_ {Y}\ =\ C_{Y}Al_{tc}\frac{\rho V^{2}}{2} \tag{16}\]  
In Equations (15) and (16), \(C_{D}\), \(C_{S}\), \(C_{L}\), \(C_{R}\), \(C_{P}\), and \(C_{Y}\) are the aerodynamic coefficients for drag force, side force, lift force, rolling moment, pitching moment, and yawing moment, which can be determined from wind tunnel tests or by computational fluid dynamic analysis; \(\rho\) is the air density (=1.225 kg/m\({}^{3}\)); \(V\) is the apparent wind speed; and \(A\) is the front area of the vehicle.  
All aerodynamic coefficients are functions of the effective wind direction, \(\psi\), which can be defined from the true wind speed vector \(\mathbf{w}\) and the vehicle's own speed vector \(\mathbf{v}\), as shown in Figure 3. When the true wind direction is \(\beta\), the apparent wind speed vector \(\mathbf{V}\) and effective wind direction \(\psi\) can be defined as shown in Equations (17) and (18).  
\[|\mathbf{V}|^{2}\ =\ (|\mathbf{v}|+|\mathbf{w}|\cos\beta)^{2}+|\mathbf{w}|^{2}\sin^{2}\beta \tag{17}\]  
Figure 2: Applied actions and corresponding tire forces of trailers.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 3 Methodology  
### Overall Dehazing Framework  
Figure 1 illustrates the overall framework of the proposed zero-shot dehazing network. The hazy image, \(I\), is initially fed into the _DCP_ module to obtain the transmission map, \(t\), the atmospheric light, \(A\), and the initial dehazed result, \(J\). Subsequently, \(t\) and \(J\) are further refined by two CNN-based refinement networks to obtain \(t^{\prime}\) and \(J^{\prime}\). Using the haze imaging model [37], we regenerate a hazy image \(I^{\prime}\) using \(t^{\prime}\), \(A\), and \(J^{\prime}\). We ensure that \(I\) and \(I^{\prime}\) are consistent using the mean square error (MSE) loss function, allowing the entire network to be trained in a zero-shot manner. Once \(t^{\prime}\) and \(A\) are obtained, the haze-free image can be output by reversing the haze imaging model. Overall, the zero-shot learning strategy guarantees the network's generalizability without requiring dense training data, while the CNN-based RefineNets compensate for the flaws of prior-based dehazing methods.
## 5 Biological Activities of Rice Phytolexins
### Bioactivities of Diterpeneoid Phytolexins
#### 5.1.3 Phytocassanes  
Despite their known role as phytoalexins, literature regarding phytocassanes and their biological activity is surprisingly scarce. In the last decade of the 20 th century, known members of this class of diterpenes (i.e., phytocassanes A-E) were found to effectively inhibit spore germination when rice plants were infected with the rice blast fungus _M. grisea_, the rice sheath blight fungus _Rhizoctonia solani_, and the pathogenic potato fungus _Phytophthora infestans_[29; 30]. Over the last decade, [PERSON] and co-workers [31] confirmed these observations, including the recently discovered phytocassane F in their tests. During this last work, an increase in the production of phytocassanes after exposure of rice leaves to UV light was also observed [31], providing insights into the role of these compounds in response to abiotic stress. Considering biotic factors, increases in phytocassane production have been observed after exposure to fungal inoculation [212], _Tricoderma viride_-derived xylanase [213; 214], cerebrosides A, B, and C [215; 216], cholic acid [217; 218], and mannan oligosaccharidesad [219], demonstrating their implication in response to a wide array of biological challenges. More recently, phytocassanes have been confirmed to play an active role in plant response to stress in general. Knock-out lines with deletion of biosynthetic gene clusters from chromosome 2, associated with phytocassane biosynthesis, were shown to be more susceptible to fungal blast and bacterial leaf blight than lines with deleted biosynthetic gene clusters from chromosome 4, associated with momilactone biosynthesis. These mutants also exhibited a drought and temperature-sensitive phenotype [126].
###### Abstract  
This article presents a dataset obtained from the deployment of an IEEE 802.15.4g SUN (Smart Utility Network) single-hop network (11 nodes) in a large industrial scenario (110,044 m\({}^{2}\)) for a long period of time (99 days). The dataset contains \(\sim\)11 M entries with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment), and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in the average RSSI (i.e., between \(-\)82.1 dBm and \(-\)101.7 dBm) and CCA (i.e., between \(-\)111.2 dBm and \(-\)119.9 dBm) values, which is caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, these values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are needed to meet the link reliability requirements of industrial applications. Hence, the presented dataset will allow researchers and practitioners to propose novel mechanisms and evaluate their performance using realistic conditions, enabling the dependability vision of the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force).  
IEEE 802.15.4g; smart utility networks; low-power; wireless; communications; dependable; predictable; reliable; available; industrial; dataset +
Footnote †: journal:  
_Acticle_  
A Dataset to Evaluate IEEE 802.15.4g SUN for Dependable Low-Power Wireless Communications in Industrial Scenarios
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology
### Network Model  
Figure 2 illustrates our network model. The network consists of \(n\) sensor nodes, one underwater sink node (UW-Sink), \(m\) backup UW-Sink nodes and one surface sink node. All the underwater nodes move freely within the same underwater stream. The sensor nodes send the sensed data to the UW-Sink periodically. The UW-Sink is connected to the surface sink node using a long-haul connection which is used to transfer the data collected from the underwater sensor node to the surface sink node. We assume that the UW-Sink nodes have abundant energy resources for long-haul communication. The backup UW-Sink nodes, which also act as ordinary sensor nodes, take the role of UW-Sink in case of failure of the main UW-Sink. Every UW-Sink is equipped with movement tracking apparatus using which it can estimate its distance from its initial position at the time of deployment and thus, its distance from the surface sink node at any given time. This distance information is necessary for range adjustment, as the nodes move away from initial deployment position.  
Figure 2: Network model.  
**Transfer of Control among UW-Sink nodes:** The transfer of control may be required if the energy level of the active UW-Sink node reaches a certain threshold. In such a case, the UW-Sink node exclusively instructs one of the backup UW-Sink nodes to take control from the next iteration. This can be done in many ways. For instance, the backup node can be informed through a special bit piggy-backed in a routine beacon. The message can be protected using forward error correction (FEC). As the number of control transfers is normally negligible, addition of FEC bits to a \"transfer of control\" message has a very insignificant effect on the protocol performance. Other options, such as sending a message on an exclusive control channel or sending a \"transfer of control notification\" on the same channel either through multihop communication or directly with increased range, also exist.
###### Abstract  
Cross-laminated timber (CLT) floors with supplementary layers or floating floors comprise a common solution in new multistory timber structures. However, bare CLT components provide poor sound insulation, especially in low frequencies during structure-borne sound propagation. Thus, floor configurations in wooden buildings deploy more layers for improved acoustic behavior. Twelve contemporary CLT floors were analyzed after laboratory measurements of airborne sound reduction and impact sound transmission utilizing the following indicators: \(R_{w}\), \(R_{w}\), \(100\), \(R_{w}\), \(50\), \(L_{w,2}\), \(L_{w,2}\), \(L_{w,2,100}\), and \(L_{w,w,50}\) (per ISO 10140, ISO 717). An increase in sound insulation was achieved thanks to added total mass and thickness, testing layers of the following: elastic mat for vibration isolation, wool insulation, gypsum boards, plywood, concrete screed, and wooden parquet floor. The results indicate that multilayered CLT floors can provide improvements of up to 22 dB for airborne sound and 32 dB for impact sound indicators compared with the bare CLT slab. Floating floor configurations with dry floor solutions (concrete screed) and wooden parquet floors stand out as the optimal cases. The parquet floor provides a 1-2 dB improvement only for impact sound indicators in floating floor setups (or higher in three cases).  
Keywords:cross-laminated timber; airborne sound; impact noise; sound insulation; timber +
Footnote †: journal: Applied Sciences