esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Burrowing differences between the distinct salinity levels in each treatment were assessed using an ANOVA test with repeated measures. As our data did not meet the sphericity assumption ([PERSON]'s test with \(p<0.05\)), values with a Greenhouse-Geisser correction were considered (Table 3). Overall, the percentage of buried individuals was significantly different between salinity levels on treatments A and B (Table 3): (_a_) higher values (\(>\)80%) at salinity levels 20 and 15; (_b_) medium values (38% to 75%) at salinity 10; (_c_) lower values (\(<\)38%) at salinity 5. Pairwise comparisons using post hoc tests (Bonferroni correction) revealed no significant differences (\(p=1\)) on the burrowing percentage observed between salinity levels 20 vs. 15, while significant differences (\(p<0.05\)) were found between salinity levels 20 vs. 5 and 15 vs. 5, both on treatments A and B. Additionally, the observed burrowing percentage showed linear relationships with salinity (Figure 4A,B) on Treatment A (R\({}^{2}\) = 0.8565; \(p<0.001\)) and B (R\({}^{2}\) = 0.792; \(p<0.001\)). On both treatments, normality of the data (Treatment A: \(p=0.201\); Treatment B: \(p=0.375\)) and homogeneity of the variances (Treatment A: \(p=0.578\); Treatment B: \(p=0.921\)) were verified. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Treatment A**} \\ \hline
**Mauchly’s Sphericity** & \(W\) & **Sig** & \\
**Greenhouse–Geisser** & \(\varepsilon\) & & \\  & 0.196 & & \\  & **df, df error** & **F** & **Sig** \\  & 2.354, 11.769 & 61.414 & \(<\)0.05 \\ \hline  & \multicolumn{2}{c}{**Treatment B**} & \\ \hline
**Mauchly’s Sphericity** & \(W\) & **Sig** & \\  & 0 & \(<\)0.05 & \\
**Greenhouse–Geisser** & \(\varepsilon\) & & \\  & 0.241 & & \\  & **df, df error** & **F** & **Sig** \\  & 2.890, 14.451 & 39.516 & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the one-way analysis of variance (ANOVA) with repeated measures results for treatments A and B.
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 2 Materials and Methods  
### Plant Material and Chemicals  
The plant materials used in this stage were represented by the dried floral tips of the species _Lythrium salicia_ L. collected in August 2019 and 2021 from Constanta, Romania. A voucher specimen of _L. salicaria_ L. plant was registered at the Pharmacognosy Department of the Pharmacy Faculty, Ovidius University of Constanta, under an individual number.  
The aqueous extract, obtained by refluxing for 2 h and filtering with millipore filter paper, was concentrated using a Buchi R-215 rotavapor and dried via cryo-drying using an Christ Alpha 1-2 B lyophilizer. At the end of the lyophilization process, approximately 12 g of freeze-dried _Lythri herba_ aqueous extract (Ly) was obtained, which was transferred to a glass bottle with a tight stopper and stored in a desiccator. To prepare the concentrations of herbal aqueous extract solutions that would be incorporated into the studied membranes, a stock solution of 10 g/L aqueous extract was prepared.  
The chitosan powder (CS) with medium molar mass and a deacetylation degree (DDA) \(\geq\) 75% (product number 448877) and glacial acetic acid ReagentPlus(r) \(\geq\)99% (product number A6283), were purchased from Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 5 Energy-Saving Policy and Consumption Estimation  
This section covers two relevant characterizing themes of the IoT sensor node effectiveness assessment: the description of the energy-saving policy and the estimation method adopted to evaluate the overall consumption and therefore the lifetime of the four 1.5 V AA lithium batteries supplying the node.
## 2 Materials and Methods
### Household Questionnaire and Field Survey  
To understand farmers' attitudes towards afforestation choice and the income, we conducted a household questionnaire survey in 2019, and collected a total of 150 questionnaires from different households of 10 villages by face-to-face interview. In the questionnaire survey, we investigated the questions about demographic factors (e.g., respondent's age, income source, and involved area of converting farmland to forest), attitudes of farmers towards the GGP (e.g., choice of afforestation types, tree species), and economic income from forest management of the GGP (e.g., seed yield, price per kilogram, cost and benefit from economic forest). We also collected the statistical data about afforestation area and types of the GGP in 2017 and 2018 from the local forestry administration. All the above information supported the reasonable scenario development of the GGP afforestation.  
To obtain carbon storages, we conducted a field survey, and sampled soil and litter of 138 plots for the GGP-related land use types. Three soil cores were taken from three soil depth ranges (0-10 cm, 10-20 cm, 20-30 cm) at each site in study area, and the samples were naturally air dried before soil organic carbon density was measured. Furthermore, we collected litter within each plot, and oven-dried at 85 \({}^{\circ}\)C to measure its carbon density.
## 2 Materials and Methods
### Real-Time DIC Implementation
The position vector \(\overset{\rightarrow}{\mathbf{r}}_{\mathbf{k}}(\mathbf{t})\) is consequently calculated from the initial position \(\overset{\rightarrow}{\mathbf{r}}_{\mathbf{k}}(\mathbf{0})\), i.e., the center of the template \(\mathbf{T}_{\mathbf{k}}\), plus the displacement \(\overset{\rightarrow}{\mathbf{d}}_{\mathbf{k}}(\mathbf{t})\):  
\[\overset{\rightarrow}{\mathbf{r}}_{\mathbf{k}}(\mathbf{t})=\overset{\rightarrow}{\mathbf{r} }_{\mathbf{k}}(\mathbf{0})+\overset{\rightarrow}{d}_{\mathbf{k}}(\mathbf{t})\text{ with }\overset{\rightarrow}{d}_{\mathbf{k}}(\mathbf{t})\equiv\left( \begin{array}{c}\mathbf{u_{max}}\\ \mathbf{v_{max}}\end{array} \right)_{\mathbf{k}}(\mathbf{t})-\left( \begin{array}{c}\mathbf{u _{max}}\\ \mathbf{v_{max}}\end{array} \right)_{\mathbf{k}}(\mathbf{0}). \tag{6}\]  
This algorithm is a rather straightforward implementation of the DIC problem sometimes referred to as fast Fourier transform-based cross-correlation (FFT-CC). It is path-independent with deterministic execution time and complexity \(O(log_{2}(n))\) if Equations (3) and (4) are executed on the GPU. Equation (5) describes a zero-order shape function measuring translation and disregarding shear and rotation--which is sufficient for 2D-DIC systems and typical fatigue strains in the range of 1%. Figure 3 illustrates these evaluation steps. Images (a) and (b) are examples of a reference image with the templates \(T_{k}\) and of a measurement image with search areas \(S_{k}\). The templates are positioned in the diffuse reflection area of the polished steel specimen. Although it looks quite specular, the surface exhibits a periodical structure caused by the polishing process. The cross-correlation results \(C_{k}\) are given in image (c). The polishing structures cause some periodic noise, but on top there is a significant correlation peak originating from the incidental microstructure, i.e., the non-periodic patterns. On all investigated samples (steel, aluminum and nickel-base superalloys), this peak was sufficient for tracking the \(3\times 3\)-pixel neighborhood enlarged in image (d) using a polynomial as given in Equation (5). Therefore, no further surface treating like the application of marker structures was necessary.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Precision
#### 3.1.1 Data Reduction  
Because of the large amount of data that was collected (15 targets \(\times\) 8 instruments \(\times\) 7 spectral bands), it is necessary for this presentation to slightly reduce the scope. Therefore, only results from three Landsat 8 spectral bands will be presented as follows: Green (525-600 nm), NIR (845-885 nm), and SWIR2 (2100-2300 nm). Results from these three bands are nearly identical and representative of all OLI bands. Furthermore, only a subset of targets will be used in the analysis. For each band, a set of 3-4 brightest and a set of 3-4 darkest targets were selected. These targets represent the end points in the reflectance range often observed by satellite sensors, and differences in repeatability can be scaled between these extremes.  
Repeatability is important at several different levels. First, given the same instrument and operator, how repeatable is the measurement? Secondly, given that one instrument/operator cannot cover all locations, is there a decrease in repeatability when multiple instruments/operators are used? Third, how do UAS-based measurements compare to ASD-based measurements? Do they exhibit the same degree of precision?  
All units in the following tables are in absolute reflectance units. While percentages are often easier to evaluate and understand, units based on percentages can be misleadingfor low reflectance values where percentage-based differences can become quite large. Secondly, for the purposes of the evaluation of land surface products, the aim is to know the absolute deviations from the true values in order to properly evaluate the accuracy of the product.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 2 Model Formulation  
_The notations_: \(\mathbb{R}\) and \(\mathbb{C}\) represent the real and complex space, respectively. The superscript \((\cdot)^{\mathcal{H}}\) denotes the conjugate transpose. \(|\cdot|\) represents the absolute value if applied to a complex number or the cardinality if applied to a set. And \(\|\cdot\|\) represents the \(l_{2}\)-norm of a vector. \(\text{Unif}[a,b]\) denotes uniform distribution in \([a,b]\). \(\mathcal{N}(0,\sigma^{2})\) and \(\mathcal{CN}(0,\sigma^{2})\), respectively, denote the Gaussian distribution and circularly symmetric complex Gaussian distribution with mean 0 and covariance \(\sigma^{2}\). \(\gamma^{N}=(Y_{1},Y_{2},\cdots,Y_{N})\). \(E(\cdot)\) and \(\text{Var}(\cdot)\) represent statistical expectation and variance for random variables, respectively. The Gaussian Q-function is defined by \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{\sigma^{2}}{2}}dt\), and \(Q^{-1}(\cdot)\) is its inverse function. The log function takes base 2 in this paper.
## 3 Proposed Methods  
This section is divided into three subsections, where the content of each subsection is:  
1. The coupling relationship between the satellite observation, data transmission, and electricity subsystems is analyzed. Next, the different coupling states where each subsystem is the main limiting factor are identified, followed by the extraction of the coupling characteristic variables reflecting different coupling states;
2. The description of the RS algorithm including the satellite state prediction, reasoning of the target selection strategies, target weight calculation, target selection, and scheduling;
3. Three heuristic scheduling methods are given on the basis of three scheduling strategies for a comparison with RS.
## 3 Experiment and Analyses
### Analyses of Static Experiments
#### 3.1.3 Results of AFM and the Exploration of Efficiency and Reliability
The other way to remove wrong peaks will be introduced in the dynamic experiment. As to the initial accuracy of LAMBDA method in the indoor pseudolite system, it has been explained in detail in the literature [8], whose conclusion can be directly referenced. That is,  
Figure 7: The effect of AFM step length on multi-peaks. the LAMBDA method needs the initial value at the centimeter level of precision. In this paper, the precision of the coordinates corresponding to AFVs will be compared with the exact coordinates. In the zero baseline static experiment, all the coordinates corresponding to the large AFVs are shown in Table 2. In the experiment, the exact coordinates of the rover station are, in accordance with the base station, about (0.60, 0.00). According to Table 2, we can find that all the coordinates corresponding to the larger AFVs in the zero baseline static experiment are nearly exact. Moreover, the coordinates corresponding to the highest AFV are close to the exact coordinates. The zero baseline experiment fully reflects that the proper expansion of the step length can effectively reduce the error coordinates near the real peak. It also verifies that if the step length is too large, it may cover the real peak. In the short baseline static experiment, all the coordinates corresponding to the larger AFVs are shown in Table 3. In the experiment, the exact coordinate of the rover station is about (0.628, \(-\)0.424). Since AFM is a mathematical operation of the DD observations, the quality of the data directly affects the conspicuousness of the peak. Therefore, the highest AFV obtained in the zero baseline experiment is more remarkable than the short baseline experiment. According to Table 3, the coordinates corresponding to the highest AFV are less different from the exact coordinates. If we increase the step length appropriately, the larger AFVs will decrease, which is similar to the zero test. We have reason to draw the conclusion that the precision can reach the centimeter level and meet the initial requirement of the LAMBDA method. There are also some wrong peaks that exist even if we increase the step length and reduce the search space appropriately. Some other strategies will be introduced in the dynamic test. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Search Step of 1 cm** & \\ \hline
**X Coordinate** & **Y Coordinate** & **AFV** \\ \hline
0.6 & 0 & 0.9997 \\
0.6 & \(-\)0.01 & 0.9926 \\
0.6 & 0.01 & 0.9906 \\
0.61 & \(-\)0.01 & 0.9771 \\
0.59 & 0.01 & 0.9767 \\
0.59 & 0 & 0.9739 \\
0.61 & 0 & 0.9724 \\
0.6 & \(-\)0.02 & 0.9693 \\
0.61 & \(-\)0.02 & 0.9657 \\
0.6 & 0.02 & 0.9657 \\
0.59 & 0.02 & 0.9636 \\ \hline \hline \multicolumn{3}{c}{**Search Step of 2 cm**} \\ \hline
**X Coordinate** & **Y Coordinate** & **AFV** \\ \hline
0.6 & 0 & 0.9997 \\
0.6 & \(-\)0.04 & 0.8767 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of 1 m search scope under different step lengths (zero baseline).
# Simulation Analysis of Fracture Process of High Moisture Content Corn Kernel Carpopodium
###### Abstract  
Corn is an important economic and food crop, and the corn threshing process is an important link in the processing of corn, but the damage rate in the threshing process has always been a problem, causing difficulties in subsequent processing and storage. To address the high damage rate in corn ear threshing, a texture analyzer was used to measure the fracture force of Boyun 88 and Zhengdan 958 corn varieties in the triaxial direction, and a CT scanning imaging system was used to analyze the connection mode between the carpopodium and the corn cob. The connection between the carpopodium and corn cob, as well as the fracture process of the carpopodium, was simulated. Finally, high-speed photography was used to study the corn ear threshing process. The results indicated that the fracture force of the carpopodium under radial tension was significantly greater than that under axial and tangential shear. Additionally, the simulated fracture stress value of the carpopodium exceeded its actual fracture stress value. Under radial stress, the fracture force between the carpopodium and corn cob exhibited more uniformity on the contact surface. When a tangential load was applied, it was observed that the force chain shifted and dissipated along the axis during corn kernel extrusion. High-speed photography on a discrete test bench revealed that corn kernel dispersion, extrusion, and force transfer facilitated the movement and migration of surrounding kernels, with the force transfer process resembling a \"trapezoid\". This study offers theoretical guidance for corn threshing with low damage and an analysis of the threshing process.  
corn kernels; connection mode; carpopodium fracture; threshing process; simulation Article
## 3 Results and Discussion
### Elastic Modulus and Ductility Index  
Elastic modulus is one of the primary parameters for the study of the elastic deformation of concrete. Elastic modulus is the ratio between yield stress and yield strain. In concrete, the stress-strain curve until yield is not linear. It is not easy to estimate the modulus of elasticity by calculating the yield stress and strain ratio. Codes and standards have recommended an approach based on estimating the secant modulus. In the method described in ASTM C469/C469M and BS EN1992-1 [38], the secant modulus is the gradient of the line joining the origin with a point on the stress-strain curve as it crosses at 40% of the ultimate strength. In this research, the modulus of elasticity was estimated according to the procedure described in BS EN 1992-1 [38]. A 100 mm diameter and 200 mm long concrete cylinder was used. A strain gauge attached to the cylinder monitored the strain with the continuous increment in load.  
Table 6 contains the ultimate stress-strain data obtained for all concrete mixes. Figure 9 shows the secant modulus estimate's value according to BS EN 1992 using the stress-strain results shown in Table 6. The addition of PVA fibers significantly enhanced the modulus of elasticity of 100% cement concrete, which was obtained as 47.6 GPa, 40% higher than that of the control mix (CM). In comparison, CCS fibers caused an increase in secant modulus to slightly more than 10% than the control mix (CM). For fly ash-based concrete, PVA and CCS fibers increased the secant modulus by 8% and 6%, respectively, compared to the control mix (CF). Another critical property of FRC, the ductility index, can also be determined from the stress-strain curve by dividing the ultimate strain and yield strain [39; 40]. Table 6 also contains the ductility index calculated for all mixes. With the addition of PVA and CCS fibers, the ductility index (dI) of 100% cement concrete was increased by 25% and 87%, respectively, compared to the CM. In the fly ash concrete, PVA and CCS fibers enhanced ductility to 37% and 91% compared to the CF.
## 5 Results and Discussion
### Lightweight Performance
For the other methods, MoLFI requires more memory space than Drain, IPLoM, and CFTL regardless of the dataset size. The reason for this is that MoLFI applies an iterative evolutionary strategy, which is memory-consuming. The memory consumption of the different methods was almost the same when the dataset size was less than 100 KB. However, as the size increased, the memory consumption of CFTL increased significantly more slowly than that of the other methods. Figure 7: Running times of different log parsing methods. CFTL significantly reduces the runtime and memory consumption of log parsing compared with the other methods, particularly for large-scale log files. CFTL runs faster for the following reasons. On the one hand, its clustering strategy has fewer traversal runs, which reduces processing time. On the other hand, it is an offline log parser with linear time complexity and requires less processing time than online methods. Consequently, as the dataset size increases, the advantages of CFTL become more obvious.
# Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate
## 3 Results
### Ionosphere
The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake. the anomaly detected on 15 June 2018 by Swarm Alpha and Charlie seems too intense (more than 8 nT peak-to-peak) to be possibly induced by an earthquake. The geomagnetic conditions on 5 February 2018 were quiet, as depicted by geomagnetic indexes (Dst = 4 nT, ap = 3 nT). The AE index presents some activity during the day of 5 February 2018, but at the acquisition time and in the two hours before, there was no such particular activity. The solar wind speed was about 400 km/s. The solar flux at 10.7 cm wavelength was 65 SFU, indicating low solar activity. The signal aligned well with [PERSON]'s area but was a little more intense on the Arabian plate side. This could be due to different factors: it could be an influence of the land with respect to the sea or the tectonic, which is an extensional context. However, it's also possible that the source of the anomaly could be something other than the earthquake. Figure 9 shows an example of an ionospheric magnetic anomalous track before the Bandar-e Lengeh (Iran) 2022 earthquake doublet. It was acquired by Swarm Bravo satellite about 44 days before the seismic events. This track shows anomalous signals in all three components, but the larger one is in the Y-East one. This is expected for internal signals (independently if induced by earthquake or other sources) [85]. It was also identified as the most promising one for searching seismo-induced anomalies [84]. During the magnetic anomaly, the geomagnetic conditions were quiet, as underlined by the geomagnetic indexes (Dst = 4 nT, ap = 7 nT, and AE was low at the time of the track and in the hours before). Solar wind speed was slightly higher than in previous cases and about 440 km/s, but still not at an active level. This could be because the solar cycle phase is closer to the maximum. In fact, the solar flux was also 165 SFU, which is quite normal for this phase of Sun activity. Furthermore, the anomaly is well within [PERSON]'s area and crosses the whole region of plate interaction involving the Gulf of Aden.
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 4 Experiments
### Implementation Details
#### 4.1.4 Sampling Details  
Our model inference process is simple and efficient. The feature maps are extracted using the image decoder, and adaptive noise is obtained from the smallest feature map, allowing the denoising decoder to predict the bounding boxes. Unlike DiffusionDet, which uses numerous noisy boxes and multiple steps during sampling, our model favors single-step sampling, aligning with the accelerated sampling approach proposed by DDIM [49]. Additionally, our model can still achieve performance improvements by adjusting the number of iterative layers and sampling steps without retraining.