esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
This study optimizes the main electrical properties of the HIT solar cell from its physical and geometrical modeling and compares the calculated efficiencies when the cell is irradiated from the back \(\eta_{back}\) and front \(\eta_{front}\) sides. Bifaciality is defined by \(B_{f}=\eta_{back}/\eta_{front}\), with both efficiencies calculated under the same conditions. The front and back surfaces show a pyramidal structure in the plane direction \(\mathrm{c}-\) Si \(\langle 111\rangle\). Series resistance in the solar cell is 0.3 \(\Omega\) cm\({}^{2}\) when the front transparent conductor oxide is ITO. To optimize the solar cell, the AM1.5 standard spectrum and the solar spectrum determined for the Atacama Desert were used. Here, calculations were made at a temperature of 300 K. The geometrical structure order is ITO/a \(-\) Si : H(\(p^{+}\))/a \(-\) Si : H(i)/c \(-\) Si(n)/a \(-\) Si : H(\(p^{+}\))/ITO from the front layer to the back one.
## 2 Materials and Methods
### Development of CFD-VR Interface
#### 2.2.2 Adding Filters to Make Flow Elements  
The CFD-produced data are stored on millions of nodes and manipulating this volume of data is impossible. The major role of ParaView is converting the node-based data to 3D graphics information by adding different filters. Five sets of flow elements including velocity vectors, contours, streamlines, particle traces, and point clouds, all located inside vessel walls, were extracted from CFD analysis data to be visualized in the virtual reality environment. As an indication of risk of aneurysm rupture, wall shear stress distribution was selected to color the parent artery, aneurysm, and stent walls.  
Figure 7 shows the velocity vectors inside the artery walls--colored by wall shear stress--in the ParaView viewport. The order of filters can be found in the Pipeline Browser. The calculator filters were used to color the velocity vectors by velocity magnitude.  
Similar filters were added to create the other fluid elements. To display the streamlines in the ParaView viewport, the points on the inlet surface were extracted and used as the source of the StreamTracer filter.  
The animation of fluid particle trace is a beneficial tool to explore the fluid path during the cardiac cycle. As the point source of the ParticleTracer filter, 300 points were distributed randomly, 1.5 mm above the inlet surface, and entered every 5 time steps, repetitively. The glyph arrow type was added to the particles to show the change in flow direction more clearly inside the aneurysm with and without stenting. The ParticleTracer filter and created particles can be seen in Figure 8.  
Figure 8: Particle tracer created in ParaView viewport, without-Stent: (**a**) the created filter; (**b**) particles colored by velocity magnitude at the first and tenth time steps.  
Figure 7: Filters to display the vessel walls colored by shear stress magnitude and velocity vectors colored by velocity magnitude in the ParaView viewport, \(t=\Omega\).
## 5 Experiments
### Evaluation of Computational Time  
We also conducted experiments to evaluate the running time of the proposed LPLC-HDL and other representative DL methods on the two face datasets and the Caltech-101 dataset, the comparison results are listed in Table 7. \"Train\" denotes the running time of each iteration, and \"Test\" is the average processing time for classifying one test sample. All the experiments are conducted on a 64-bit computer with Intel i7-7700 3.6 GHz CPU and 12 GB RAM under the MATLAB R2019b programming environment. From Table 7, we can see that, although slower than CLSDDL-LC [19], the training efficiency of LPLC-HDL is obviously higher than that of D-KSVD [5], LC-KSVD [6], FDDL [13] and DL-COPAR [16]. In the testing stage, the proposed LPLC-HDL has similar testing efficiency as D-KSVD and LC-KSVD, and the testing process of LPLC-HDL is much faster than that of FDDL and DL-COPAR. Specifically, the average time for classifying a test image by LPLC-HDL is always less than that of CLSDDL-LC on the experiments.  
Figure 8: The classification accuracy versus parameter \(\delta\) on (**a**) the Yale face dataset, and (**b**) the Caltech 101 object dataset by fixing the parameters \(\lambda\), \(\gamma\), \(\eta\) and \(k\).  
Figure 7: The classification accuracy versus parameters \(\lambda\), \(\gamma\) and \(\eta\) on the Caltech 101 object dataset; (**a**) parameter \(\eta\) is fixed; (**b**) parameters \(\lambda\) and \(\gamma\) are fixed.
## 1 Introduction
The advantage of this method is that it provides continuous monitoring of the well over its entire lifetime, enabling early detection of any potential issues. Figure 1: (**a**) A schematic description of the various components of the DAS system, (**b**) modifications in the phase difference between the points along the fiber of the signal, (**c**) the phase-lag between the beat signals (S1 + S2) formed from two scattering points in the fiber in the unstrained (blue) and strained (red) state is proportional to the strain in that section of the fiber. Another method of DAS deployment in wells is through a \"temporary\" system, which involves deploying the fiber-optic cable downhole for a specific period of time to conduct a monitoring campaign [20]. This method is typically used in situations where there is a need to assess the performance of the well or identify potential issues, but where a permanent installation is not necessary. The temporary deployment can be achieved through various techniques, including coiled tubing or slickline deployment. Several papers provide a detailed overview of the use of DAS for permanent wellbore monitoring [21; 22; 23]. They discuss different installation methods, including embedding the fiber-optic cable in the cement sheath, attaching it to the production tubing, and suspending it on a cable, and provide case studies to illustrate the effectiveness of each approach. However, each of the installation approaches has yet to be discussed in terms of the optimum performance, sensitivity and accuracy of data acquired. The focus of this study is on the dedicated downhole deployments of DAS. We conducted a field study to evaluate the effectiveness of DAS in a test well by deploying sensors in a way that is not possible with traditional geophones. Specifically, we analyzed a DAS Vertical Seismic Profiling (VSP) conducted onshore in 2019. The well was located in the middle of a Vibroseis sweep line, nearly vertical (with a very low incline angle) and was air-filled. This study is important for understanding the most optimal and cost-effective DAS VSP fiber deployment for well completion without compromising data quality. One of the key advantages of this study was the examination of fiber maintenance and access in the event of cable complications. There were concerns that the data quality from a fiber deployed on tubing or wireline may be inferior compared to fiber directly linked to a formation. However, this paper addresses and resolves this concern by providing a detailed analysis and discussion of the results. The study highlights the potential of DAS in providing a unique and superior method of seismic measurement and its ability to record a dense amount of seismic data over several kilometers.
## 3 Results and Discussion
### Method Validation
#### 3.2.6 Limit of Detection (LOD) and Quantification (LOQ)  
The limit of detection and quantification of the three analytes were calculated (Table 2) according to the following Equations (1) and (2):  
\[\text{LOD}=3.3\times\text{SD/Slope} \tag{1}\]  
\[\text{LOQ}=10\times\text{SD/Slope} \tag{2}\]  
where SD is the standard deviation of the intercept of the calibration curve.
## 2 Materials and Methods
### Volatile Compound Analysis
The linear retention indices (LRI) (calculated with reference to the n-alkanes) were used to evaluate the retention times of the VOCs. Compound identity was confirmed by the comparison of the ion fragmentation patterns and the LRI with that of the authentic compounds. For quantitation, a single-point external standard method was applied. External standard reference compounds (Sigma Aldrich, Saint Louis, MO, USA) were delivered in solutions (1 ng/\(\upmu\)L) of pentane or toluene. The quantitative ion abundances of sample runs were compared with the quantitative ion abundances of standard runs of known concentration [28]. After analysis eighteen, \"marker compounds\" were selected to represent a number of the mechanisms of formation and quantified (ng/g cooked sample) from the headspace of beef subjected to two different cooking methods.2.5. Statistical Analysis  
An individual muscle was the experimental unit. The physicochemical characteristics and volatile compounds were analyzed using the General Linear Model (GLM) procedure with a fixed effect of cooking method (roasting vs. stewing) and muscle type (SM, BF, and RF). Random terms included batches. Each treatment was triplicated (three independent batches) with three observations for each analysis. The results were analyzed by a two-way analysis of variance together with their interaction (cooking method \(\times\) muscle type). [PERSON]'s test was used at the 5% level to make comparisons between sample means when significant differences were found. All the results are presented as mean values with their standard error of mean (SEM). External preference mapping was conducted on the volatile data and the consumer and compositional data were correlated to the resulting principal components using GenStat version 18.1.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Access Time Comparison  
The Read Access Time (RAT) and the Write Access Time (WAT) indicate how quickly the memory cell operates. RAT is usually the time from WL hitting 50% of VDD until there is a voltage difference of 100 mV (the criterion used for all cell comparisons in this study) between BL and BLB for the sense amplifier to reliably detect the state. WAT is typically the time from WL reaching 50% of VDD until the internal nodes (Q and QB) cross each other's voltage levels, signifying the state has flipped.  
Simulation results (Figures 8 and 9) show that RMLP-18T has a RAT of 105.09 ps and a WAT of 46.68 ps. The RAT is approximately 2.5 times longer than the fastest competitor (HPHS12T at 41.8 ps), and the WAT is approximately 4.6 times longer than the fastest (NRHC14T at 9.87 ps), indicating a significant speed disadvantage. The slower RAT might be attributed to the design approach using WWL to separate read and write paths, potentially combined with limited current drive from the read path transistors (N5, N6). The slower WAT may result from the cell's relatively long internal feedback loop structure. This reflects a design choice that prioritizes stability, particularly read static noise margin (RSNM), and low power consumption over access speed.  
Figure 8: Read Access Time comparison between the proposed RMLP-18T cell and previous works.  
Figure 9: Write Access Time comparison between the proposed RMLP-18T cell and previous works.
## 3 Results and Discussion
### Effect of PSC and Zeolite on the Activity Concentration of Radioseium in Soil  
Shown in Figure 2 are the effects of PSC and zeolite on the activity concentrations of radiocesium in soil, which, in turn, are expressed as their decontamination degrees. The decontamination degrees of PSC and zeolite #70 alone were similar and increased with increasing dosages. However, when the dosage was higher than 15% (w/w on soil), the decontamination capability of zeolite #70 became negative whereas that of PSC continued positively although its decontamination degrees at 13.3% and 20% were almost similar taking into account the statistical error in radioactivity measurements.  
When the mixtures of soil and PSC at an initial dosage at 13.3% were at the end of their ten days, zeolite #70 was added to make final total dosages of 14.6%, 15.9%, and 19.7%, and prolonged the treatment for ten more days. Figure 2 shows that a small addition of less than 2% of zeolite #70 would  
\begin{table}
\begin{tabular}{c c c} \hline
**Constituents** & **PSC** & **Zeolite \#70** \\ \hline C (\%) & 31.7 & \\ Si (ppm) & 9625 & 30,970 \\ Al (ppm) & 2261 & 5610 \\ Ca (ppm) & 137,115 & 1186 \\ K (ppm) & 178 & 1519 \\ Fe (ppm) & 31,536 & 937 \\ Mg (ppm) & 11,655 & 229 \\ P (ppm) & 189 & 4 \\ Na (ppm) & 754 & 1706 \\ Mn (ppm) & 40 & \\ Zn (ppm) & 404 & \\ Ba (ppm) & 89 & \\ Cu (ppm) & 130 & \\ S (ppm) & 144 & \\ Ti (ppm) & 375 & \\ Cd (ppm) & ND \({}^{*}\) & \\ Pb (ppm) & ND \({}^{*}\) & \\ \hline \end{tabular} \({}^{*}\)ND: not detected. Detection limit of Cd and Pb: 0.01 ppm.  
\end{table}
Table 3: Composition of the PSC and zeolite #70.  
Figure 2: Decontamination degrees of the PSC, zeolite (Z), and a combination of PSC and Z.  
increase the decontamination degrees of the mixtures of the PSC and zeolite. However, when the addition dosage of zeolite #70 was higher than 2%, the synergistic effects of both PSC zeolite #70 were decreased and then became negative at an addition dosage of zeolite #70 higher than 3%. The results suggest that addition of the hydrophilic zeolite #70 to the hydrophobic PSC would be detrimental to their combined decontamination effects when the addition dosage of the former is higher than 3% based on soil.
## 2 Problem Statement  
We consider the problem of classifying students according to a higher-order enrollment strategy as opposed to their enrollment status during any given semester. For many students the distinction between enrollment strategy and actual semester enrollment is minor. Undergraduate students are considered full-time in a given semester at the University of Central Florida (Orlando, FL, USA) if they enroll in more than 12 credits in that semester. Approximately 35% of the student body at the University of Central Florida consistently enroll full-time throughout their academic career, implying they employ a strategy of enrolling full-time. In contrast, the case for so-called part-time students it is not as clear. In any given semester, about 30% of enrollments are part-time, yet only 7% of students consistently enroll part-time over their academic careers. Enrolling part-time in a single semester over an academic career is not equivalent to the strategy of consistently enrolling part-time. It follows that a student who enrolls in a single semester part-time may not share similarities with other students who consistently enroll part-time.  
This paper aims to recognize and report the distinction between a student's semester-by-semester enrollment status and enrollment strategy, and to establish a more meaningful way to classify students' enrollments over their academic careers. In other words, this paper develops an advanced machine learning model that uses a sequence of enrollment statuses, and returns a sequence of estimated strategies applied over the same time-frame. In recognition that students use a greater diversity of strategies than just a full-time enrollment strategy (FES) or part-time enrollment strategy (PES), we introduce the notion of a mixed enrollment strategy (MES). For a mixed enrollment strategy, students may occasionally alternate between part-time and full-time enrollment.  
Table 1 provides examples of the enrollment status of four different students over their academic careers along with the corresponding enrollment strategy. For instance, enrollment strategies for students 1 through 3 are FES, MES, and PES, respectively. Student 1 has registered as a full-time student for five semesters and just one semester as part-time. Based on the model proposed in this paper, this student is classified as an FES student. From the example, one part-time semester among many full-time semesters does not affect this student's overall enrollment strategy. The traditional methods for classifying students based on their enrollment status cannot capture this distinction. Student 2 frequently alternates enrollment status between full-time and part-time enrollment semester by semester. Therefore, the corresponding enrollment strategy for this student is MES. Finally, student 4 applies two different enrollment strategies over their academic career.
# ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things
## 3 ECU-IoFT Dataset Development
### Cyber Attacks Launched
#### 3.2.1 Wi-Fi Deauthentication Attack  
A Wi-Fi deauthentication (DEAUTH) attack is a form of Denial of Service (DoS) attack that denies communication between the access point (Ryze Tello) and the Client (UAV Control Phone). Within this attack, the attacker will send crafted packets, where the access point thinks that the client has sent a packet that wishes them to disconnect from the network. A Wi-Fi DEAUTH attack is achievable because the DEAUTH command is within the Wi-Fi control frames, hence unencrypted, regardless of whether a WPA2-PSK is configured. This attack does not require the attacker to be a member of the network, the attacker just needs to be within the range of the Wi-Fi Access Point [26].
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion
### Modeling for Prediction
#### 5.1.3 MOPSO Model  
The GA-LSSVM model and blasting cost formula (Equation (7)) were used as objective functions in MOPSO. This dual-objective optimization strategy requires consideration of four variables: spacing (\(S\)), burden (\(B\)), specific charge (\(q\)), and subdrilling (\(H_{0}\)). Combining with the literature [48] and based on multiple repeated experiments, the parameter values for MOPSO were determined as shown in Table 3.  
Among the variables, \(n_{V}\) is the number of decision variables, \(n_{P}\) is the number of particles in the swarm, \(n_{R}\) is the size of the external archive, \(n_{G}\) is the number of grids, \(c_{1}\) is the individual learning coefficient, \(c_{2}\) is the global learning coefficient, \(w_{d}\) is the decay factor of the inertia weight, and \(\omega\) is the inertia weight.
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 4 Results
### Experimental Dataset  
To verify the proposed radar imaging algorithm experimentally, the experimental data were collected at the Radar Imaging Lab, Villanova University. The parameters of the antenna array and the positions of the concrete walls are the same in the simulation. In particular, the difference is that the stepped-frequency signal contains 801 frequency steps evenly distributed from 1 GHz to 3 GHz. An aluminum pipe (length of 61 cm and diameter of 7.6 cm) was set upright on a foam pedestal (height of 1.2 m) at a 3.67 m downrange and 0.31 m crossrange. The protruding corner causing multipath is located at the upper right area at 3.4 m crossrange and 4.57 m downrange. We put RF absorbing material on the surfaces of the side walls to eliminate multipath from these two walls; however, the  
Figure 4: Comparison of reconstructed performance corresponding to different undersampling Rate and SNR.  
surfaces of the back wall and protruding corner are uncovered. The wall clutter is well removed so that only target echoes are considered in the experiments [20]. The overall experimental scene is shown in Figure 5.  
We first present the conventional beamformed result using all available data in Figure 6a. It can be observed that the ghosts caused by the uncovered surfaces are clear and distinct. Therefore, we simply analyze the cases where the reflection points are on the uncovered surfaces in the following. To reduce the time of data collection, one fourth of the array elements and the frequencies are employed in the CS reconstruction. Since the sparsity number in this dataset is hard to know, we do not consider using the BOMP mentioned in the simulation above for comparison. As depicted in Figure 6b, There exist a lot of separated pixels in the reconstruction result of the BSBL algorithm. From Figure 6c,d, it is evident that the overlapping GS and the proposed algorithm yield fewer cluttered images and better target depictions. However, it appears that there are numerous fake pixels around the real pixels due to the overlapping partition. Similar to [20], the metric of relative clutter peak (RCP), which is defined as the maximum target amplitude divided by the maximum clutter amplitude, is introduced to evaluate the imaging quality. The clutter region is defined as the space inside the indoor scenes, excluding a circle with a radius of 10 cm at the known target position. The RCP, respectively, evaluates 44.3 dB, 40.39 dB, and 48.62 dB for the BSBL, the overlapping GS methods as well as the proposed algorithms.  
Figure 5: Experimental setup and room layout.  
Figure 6: _Cont._
## 4 Conclusions  
Based on continuous measurements performed in two double-cropping paddy fields using the EC technique in southern China from 2018 to 2020, this study investigated the variations and drivers of CH\({}_{4}\) fluxes at diurnal, seasonal and inter-seasonal scales. A significant single-peak diurnal pattern was observed during the vegetative stage of all four rice growing seasons and the reproductive stage of early rice, and wavelet analysis indicated that GPP, rather than T\({}_{\text{a}}\), drove diurnal CH\({}_{4}\) fluxes. Large seasonal variations in daily CH\({}_{4}\) emissions were observed in the two double-cropping paddy fields, and T\({}_{\text{S}}\) was  
Figure 7: Total CH\({}_{4}\) emissions (black bars), number of rainfall events greater than 10 mm (square dots), and total precipitation (circle dots) from 24 July to 27 August of 2019 and 2020.  
able to explain most of the seasonality of the CH\({}_{4}\) fluxes, followed by GPP and SWC. At the inter-seasonal timescale, the CH\({}_{4}\) emissions for the total growing season ranged from 14.52 to 23.95 g C-CH\({}_{4}\) m\({}^{-2}\). The double-cropping paddy field in southern China is a relatively large source of CH\({}_{4}\) emissions. The results of this study indicated that reducing the amount and frequency of irrigation during the vegetative stage under direct-seeded combined with traditional irrigation modes could decrease CH\({}_{4}\) emissions in southern China. Choosing rice varieties that exhibit early tillering could reduce the length of time before mid-season drainage and then reduce GHG emissions from late rice. Our study on the pattern of CH\({}_{4}\) fluxes and their linkages with biophysical controls at multiple timescales will be helpful for estimating GHG budgets at regional and global scales and offers useful information for field managers and policymakers.  
Conceptualization, L.M. and Y.C.; methodology, B.L.; software, Y.S.; writing--review and editing, L.M. and B.L.; supervision, Y.C. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the NSFC-MWR-CTGC Joint Yangtze River Water Science Research Project (No. U2040213) and the National Natural Science Foundation of China (51709180).  
Not applicable.  
The data in this study are available from the corresponding authors upon request.  
The authors declare no conflict of interest.