esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 2 Materials and Methods
### Ex Situ Particle Trapping and Stabilization Study  
The potential of isolated cyanobacterial strains to trap and stabilize airborne dust particles was examined in specially designed rectangular glass chambers with a surface of 3.5 m\({}^{2}\) each. The chambers comprised two sections. The front section for the \"dust storm\" generation was equipped with a fan that simulates a wind speed of 12 m/s and had an opening for the insertion of loess particles on the top. The fan directed the particles towards the back section, which was used to study the biocrust development, particle trapping and the stabilization mechanism of cyanobacterial biocrusts. The back section was equipped with fluorescent tubes to provide irradiation ranging from 560 to 700 lux at different points.  
A mixed cyanobacterial culture comprising isolates from the BLCs collected at the Ruma loess section (N 45\({}^{\circ}\)00\({}^{\prime}\)43.8\({}^{\prime\prime}\), E 19\({}^{\circ}\)51'28.8\({}^{\prime\prime}\)) was inoculated over the glass surface of the back section in one chamber (hereinafter referred to as experimental chamber). Another chamber was used as a control, where cyanobacteria were not inoculated (hereinafter referred to as the control chamber). Upon inoculation, the cyanobacterial biofilm was developing in the following manner: it was watered with approximately 250 mL of distilled water five days a week and with the same volume of BG11 medium (0.9 g/L NaNO3) once a month, shifts of wet and dry seasons were performed by desiccation once a week. After the cyanobacterial biofilm had developed in such a way that it completely covered the back section of the experimental chamber, dust storms were simulated in both chambers in order to examine the model of particle trapping and stabilization ex situ. The experiment lasted 10 months and during that time 37 dust storms were simulated. Dust storms were simulated every third week, using 400 g of fine loess particles, one hour after watering. Loess sediment used in this study was collected at the Titel Loess Plateau, and stored in a dark and dry place until use. Prior to application in the chambers, the sediment was disaggregated in a grinder to provide a dust-like application material. During the ex situ study, the back sections of both chambers were watered and desiccated in the same manner as the biofilm during its development. This amount of water proved to be optimal for the growth of cyanobacteria and did not contribute to an excess of water on the test surface in the control chamber. By subtracting the amount of sediment remaining in the front section of each chamber from the amount of the loess sediment used for dust storm generation, the sediment quantities that were blown into the back section of the experimental chamber (3225 g) and the control chamber (2850 g) were calculated. Sampling was performed after 10 months of biocrust growth under the described conditions and the biocrusts development, dust particle trapping, and stabilization were examined.
# Enhancements and Challenges in CoAP--A Survey
## 6 Open Challenges  
Unlike HTTP CoAP operated over UDP and thus significantly reduces the overhead. However, the number of devices attached to the Internet is continuously increasing, and thus using the existing CoAP architecture may perform inefficiently in dense network scenarios. The CoAP is designed with keeping the message overhead as low as possible along with less usage of fragmentation option. However, still there exist a number of challenges and issues in the existing CoAP architecture. In this regard, we come up with the following main challenges existed in the current architecture of the CoAP.
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.3 Optimal Configurations for Recall  
Table 5 details the configurations maximizing recall, a key metric for ensuring all true positives are detected, across the seven models. Recall scores and training times are provided. ConvNeXt, MobileNetV3, and ViT-B-16 achieve perfect recall of 1.0000, using Adam (learning rate 0.01 for ConvNeXt and MobileNetV3, 0.001 for ViT-B-16 with AdamW), StepLR (ConvNeXt and MobileNetV3) or CosineAnnealingWarmRestarts (ViT-B-16), and CrossEntropy (ConvNeXt and MobileNetV3) or LabelSmoothing (ViT-B-16).  
EfficientNet-B2 records 0.979021 with Adam (learning rate 0.0001), StepLR, and CrossEntropy, where a lower learning rate ensures cautious weight updates, slightly reducing recall. ResNet101 achieves 0.972028 with Adam (learning rate 0.0001), CosineAnnealingLR, and LabelSmoothing, balancing sensitivity with regularization. ResNet50, at 0.965035, uses SGD (learning rate 0.01), StepLR, and FocalLoss, where FocalLoss's focus on hard examples may trade off some recall for precision gains.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **Precision** \\ \hline ResNet50 & AdamW & 0.0001 & CosineAnnealingLR & FocalLoss & 0.958333 \\ DenseNet121 & SGD & 0.01 & StepLR & CrossEntropy & 0.957295 \\ MobileNetV3 & SGD & 0.01 & CosineAnnealingWarmRestarts & CrossEntropy & 0.947552 \\ ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.945392 \\ ConvNeXt & Adam & 0.0001 & ReducelLROnPlateau & CrossEntropy & 0.939502 \\ EfficientNet-B2 & SGD & 0.01 & CosineAnnealingWarmRestarts & FocalLoss & 0.938983 \\ ViT-B-16 & SGD & 0.001 & CosineAnnealingWarmRestarts & FocalLoss & 0.936842 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal hyperparameter configurations for maximizing precision across deep learning models.
## 2 Materials and Methods
### Method
#### 2.3.1 Land Use Change  
The analysis of land use change is conducted from two perspectives: temporal series variation and differences inside and outside PAs. The ArcGIS 10.8 software (ESRI (Environmental Systems Research Institute). 2018. ArcGIS Desktop: Release 10.8. Redlands, CA, USA: Environmental Systems Research Institute. [[https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview](https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview)]([https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview](https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview)), accessed on 9 April 2023) is used to make statistics on the land use area and transfer matrix characteristics to analyze the process and degree of change among different land use types in the study areas. The dynamic degree model of  
Figure 2: The framework of evaluating the conservation effectiveness based on land use, remote sensing, and landscape metrics: \(K\) represents land use dynamic index, PD represents patch density, LPI represents largest patch index, MPS represents main patch size, ED represents edge density, LSI represents landscape shape index, CONTAG represents contagion, SHDI represents [PERSON]’s diversity index, SHEI represents [PERSON]’s evenness index.  
land use is used to analyze the change degree of various types of land use [21; 70; 71]. The dynamic degree index (\(K\)) is calculated as follows:  
\[K=\frac{U_{a}-U_{b}}{U_{b}}\times\frac{1}{T}\times 100\%\]  
where, \(K\) refers to the dynamic degree of a certain land use type during the study period; \(U_{b}\) and \(U_{a}\) are the areas of a certain land use type before and after the study period, respectively (unit: ha); and \(T\) is the number of years between the study period.
## 2 Materials and Methods  
### EM Simulation and Optimization  
The objective of the EM simulation study was to optimize the elliptical birdcage design by evaluating the parameters presented in the introduction section. The initial values of the capacitors were selected from the birdcage builder toolbox [36]; however, they failed to generate the desired resonance mode and homogenous B\({}_{1}\)+-field for the large-sized shielded birdcage RF coil. Several numerical approaches have been reported to calculate the optimum capacitor values; however, they require extensive numerical calculations and precise accuracy in self- and mutual inductance calculations. The accuracy of the inductance calculation degrades in asymmetric and curvy structures because of ideal assumptions in the theoretical analysis. Thus, an RF coil co-simulation approach was implemented in this study to calculate the optimal capacitor values. First, all the lumped elements were replaced with excitation ports, and the full port S-parameters were generated via EM simulation and then exported to MATLAB (The MathWorks, Natick, MA, USA) to calculate the optimal capacitor by minimizing the cost function for the desired mode at the resonant frequency (further details are provided in Supplementary Materials Section A), as Equation (1):  
\[arg\;min\Big{\{}\Big{|}S_{i=j}^{port}+\lambda\;\Big{(}min\Big{(}S_{i\
eq j}^{ port}\Big{)}\Big{)}\Big{|}\Big{\}} \tag{1}\]  
where \(S_{i=j}^{port}\)= S-parameter of the individual \(i\) port; lambda (\(\lambda\)) = trade-off parameter between decoupling and fine tuning; \(min\Big{|}S_{i\
eq j}^{port}\Big{|}\) = S-parameter between the \(i,j\) port.  
In circular birdcages, same-value capacitors at the end-rings cause horizontal and vertical resonance modes to occur at the same frequency. However, in an elliptical birdcage, different-value capacitors are required at the end-rings such that the horizontal and the vertical resonance modes occur simultaneously at the same frequency. After the optimal capacitors were calculated, tuning and matching were performed through the RF circuit matching toolbox of the simulation software. All RF coil modeling and full-wave EM simulations for this study were performed using the commercially available software Sim4 Life based on the FDTD solver (ZMT, Zurich MedTech AG, Zurich, Switzerland). The simulations were performed on a cylindrical uniform phantom (diameter, 50 cm; length, 50 cm) with a low dielectric constant (electrical conductivity of 0.066 S/m; relative permittivity of 13.69) for B\({}_{1}\)+-field uniformity evaluation; furthermore, a multi-tissue three-dimensional (3D) human model, \"Duke\", obtained among virtual population models [37] was used to evaluated the SAR and the transmission efficiency. The B\({}_{1}\)+-field uniformity was evaluated in a 3D region of interest (ROI) measuring 45 cm \(\times\) 41 cm \(\times\) 35 cm to calculate the worst case uniformity in the cylindrical uniform phantom. The total input power for the B1\({}^{*}\)-field and the SAR fields was normalized to 1 \(\upmu\)T. Figure 1 shows the modeled circular and various elliptical birdcages evaluated in this study.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### The Mamdani-Type Fuzzy Inference Process  
A Fuzzy Inference System (FIS) is used to make decisions based on fuzzy logic. Here, we define an FIS to control a Bulk Service Queueing Model with Vacation, incorporating: A: High Bulk Service, B: Medium Single Service, and C: Low Vacation.  
The Mamdani-type fuzzy inference process consists of five steps:  
Step 1: Fuzzify input variables  
Step 2: Apply fuzzy operator  
Step 3: Apply the implication method  
Step 4: Apply the aggregation method  
Step 5: Defuzzify  
An example of \"Solar Power System Performance\" is presented to demonstrate the full fuzzy inference process. As depicted in Figure 4, the two input variables--solar irradiance and battery charge level--undergo fuzzy reasoning with three If-Then rules. The outputs from these rules are then aggregated and defuzzified into a precise numerical value that represents the overall performance of the solar power system.  
The linguistic terms and membership functions (MFs) for the Mamdani FIS were derived using a hybrid approach: expert knowledge defined initial labels (e.g., \"Low\", \"Medium\", \"High\"), while fuzzy c-means clustering refined MF boundaries based on historical system data. The rule base was then tuned iteratively via simulation. Sample fuzzy rules and MF plots.
## 5 Conclusions  
In summary, the high correlations between SIF and sugarcane yield and GPP indicate that SIF can be used as a proxy for sugarcane yield and GPP. The study area experienced an extremely severe drought event, which is detected by the significant negative anomalies of drought indicators such as SPEI and precipitation, from August to November 2009. During this drought event, satellite-observed SIF and SIFyield present earlier and more pronounced responses to drought stresses than the EVI in time and space, indicating that sugarcane SIF is much more sensitive to drought stress than EVI. The spatial patterns of SIF anomalies can closely monitor the spatial evolution of drought stress in the study area. Thus, SIF can closely capture the temporal and spatial dynamics of drought stress on sugarcane. We also found that drought negatively affected the SIFyield of sugarcane during the early stage of drought (late August). During the later stage, especially after late September, drought had a negative influence on the fPAR and fluorescence yield of sugarcane but had a larger magnitude on the fluorescence yield. The more pronounced negative responses of sugarcane SIF can be attributed to both physiological and structural variations induced by drought stress, ultimately reducing sugarcane yield. The results of this study are critical for developing early warning models for sugarcane drought monitoring, and suggest that SIF can provide a new approach to analyze the impact of drought on sugarcane.  
Methodology, N.Y.; Validation, S.D.; Data curation, Y.W.; Writing--original draft, N.Y.; Visualization, H.Q.; Supervision, S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundations of China (grant no. 42061071), Special Project for Technology Base and Talent of Guangxi (grant no. Guangxi Science AD20297027), Guangxi Natural Science Foundation Program (grant no. 2021 GXNSFBA220061), and 2021 annual young teachers basic capacity improvement project of Universities in Guangxi (grant no. 2021 KY0397). This work was also supported by Guangxi First-class Discipline Statistics Construction Project Fund.
# MpcNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
## 4 Experimental Results and Discussion
### Evaluation Metrics  
In this section, we present the three metrics used to evaluate the proposed MreNet, including 3D intersection over union (IoU), corner error and pixel error. We describe the three metrics next.  
IoU is the measurement of the accuracy of the test results. The object to be detected has a truth box, namely the ground truth. It is necessary to artificially mark the general range of the object with a rectangular box on the pictures in the dataset. In the evaluation of an algorithm, the algorithm first needs to be used to detect the image, then the prediction box of the corresponding object generated by the algorithm can be obtained, and then the IoU index between the prediction box and the truth box is calculated.  
The larger the value of the _IoU_, the better the consistency between the prediction box and the truth box, and the higher the accuracy of the algorithm. The corresponding mathematical formula is as follows:  
\[IoU=\frac{area\big{(}B_{p}\cap B_{gt}\big{)}}{area\big{(}B_{p}\cup B_{gt} \big{)}} \tag{21}\]  
where \(area\big{(}B_{p}\cap B_{gt}\big{)}\) denotes the area of the prediction box generated by the neural network and \(area\big{(}B_{p}\cup B_{gt}\big{)}\) denotes the area of the real box.  
The _3D IoU_ in this paper refers to the result of dividing the area of the intersection and the union area between the 3D layout and the marked real layout generated by the layout estimation based on the proposed method. The formula is as follows:  
\[3D\ IoU=\frac{V_{1}\cap V_{2}}{V_{1}\cup V_{2}} \tag{22}\]  
where \(V_{1}\) denotes the volume of the generated 3D layout estimated using the neural network and \(V_{2}\) denotes the volume of the actual 3D layout.  
Corner error denotes the average Euclidean distance between the predicted angle and the true angle. The calculation formula of the corner error is as follows:  
\[CE=\frac{\sqrt{\sum{D_{i}}^{2}}}{H^{2}+W^{2}} \tag{23}\]  
where \(CE\) denotes the value of the corner error, \(D_{i}\) denotes the average Euclidean distance, \(H\) denotes the distance between the predicted angle and the real angle and \(W\) denotes the width of the input panoramic image.  
Pixel error refers to the average pixel error between the predicted layout and the real layout, and its mathematical calculation formula is as follows:  
\[PE=\frac{\sum{(p_{i}\
eq g_{i})}}{H\times W} \tag{24}\]  
where \(p_{i}\) denotes the pixel values of the structural lines, such as walls and ceilings in the generated 3D layout, \(g_{i}\) denotes the pixel value of each structure line in a real 3D layout and \(PE\) denotes the pixel error.  
It can be inferred that if the predicted pixel value is equal to the real pixel value, the value of \(p_{i}\
eq g_{i}\) is zero. Conversely, if the predicted pixel value is not equal to the real pixel value, the value is one. \(H\) denotes the height of the input panoramic image and \(W\) denotes the width of the input panoramic image.
# Characterization of Asphalt Binders Modified with Bio-Binder from Swine Manure
## 3 Results  
### Penetration Test  
In Figure 1, the evolution of the penetration values of the bio-modified asphalt binder depending on the added bio-binder content can be observed. These values were obtained under homogeneous conditions. The figure shows an exponential increase in the penetration values as the bio-binder content increases, indicating a possible softening effect of the bio-binder. In the case of the 15/25 reference binder, with the addition of 10% bio-binder, a 41.9% increase in penetration was observed, while with the addition of 20%, values 114.9% higher than those of the reference binder were observed. In the case of the 50/70 reference asphalt binder, with the addition of 10% bio-binder, a 57.5% increase in penetration was observed, while with the addition of 20% bio-binder, values 90.9% higher than those of the reference binder were obtained. The increase in penetration values of the bio-binder is consistent with those obtained by [PERSON] et al. [55]. Higher penetration values indicate lower binder consistency, which is an indication of better binder performance under low-temperature conditions.  
During the penetration test on the bio-binder, in the absence of reference binder, a bleaching phenomenon of the top layer of the sample in contact with water was observed, changing from the original blackish colour to a brownish colour. (Figure 2). However, once  
Figure 1: Evolution of bio-modified asphalt binder penetration (at 25 \({}^{\circ}\)C) according to the percentage of bio-binder added.  
the surface in contact with water was dried and a period greater than 180 min elapsed, the bio-binder returned to its original blackish colour. This indicates that, in the short term, water influences the properties of the bio-binder, and to a lesser extent, the bio-modified asphalt binder. Although this process may be reversible, during the period in which the bio-binder is in the presence of water, the bond between the aggregate and the binder could be affected by the water. However, the results of the UCL test (Section 3.6) on the asphalt mixture subjected to moisture damage indicate the opposite.
## Materials and Methods
### Data Analysis  
#### 2.4.1 Mean Volume Backscattering Strength (MVBS) Computation  
ADCP instruments have significant advantages in detecting zooplankton because of their wide coverage area of three or four transducers [27]. The consistency of the backscatter value from each transducer was analyzed by using cross-beam calibration [17; 20]. Aside from that, the acoustic waves from ADCP instruments still have sound attenuation from the geometrical spreading and sound absorption in the water column. Sound attenuation due to scatterers consists of scattering suspended particles, viscous, chemical, and thermal absorption [28]. By considering these sound attenuations effects, the echo intensity values obtained from the moored and mobile ADCP were processed based on the modified [PERSON] [18] and [PERSON] [19] equation for ADCP, with the range (\(R\)) parameter in the original formula replaced by the slant range (m) as stated in the [PERSON] [22] equation.  
Figure 2: The zooplankton acoustic acquisition setting on (**a**) moored ADCP and (**b**) mobile ADCP. The mobile ADCP moving through the pre-defined cross-section showed with arrows and passed moored ADCP location, turned back in the same track, and repeated for 3 hours of each session (daylight, twilight, and night).  
The MVBS data from mobile ADCP were extracted and grouped into three categories based on time. Sunset and sunrise times at observed area were extracted from the astronomical database of the Indonesian Meteorological, Climatological, and Geophysical Agency ([[http://www.bmkg.go.id/](http://www.bmkg.go.id/)]([http://www.bmkg.go.id/](http://www.bmkg.go.id/))). Raw acoustics data in echo intensity units and the vertical velocity profile were extracted using Argonaut Data Post-Processing v3.71 (Xylem, Inc.) for moored ADCP and WinRiver II v2.12 (Teledyne Technologies, Inc.) for mobile ADCP. Extracted echo intensity data, velocity profiles, and numerical modeling were then processed using MATLAB 2016a (Mathwork, Inc.).
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### IOTA Block Generation and Retrieval Costs  
For this experiment, 1000 iterations have been recorded. Figure 3 shows the results of the experiment. The block generation cost does not vary significantly for payload sizes from 2 Kb to 8 Kb, staying between 11.67 s and 13.97 s. However, when the payload size increases to 10 Kb, the block generation cost slightly increases from 13.97 s to 18.83 s. IOTA  
Figure 2: Effect of different lambda threshold values on node trustworthiness.  
is utilized in the proposed scheme to securely store trust values. The trust values are saved in JSON format with three entries: node ID \(ID_{node}\), previous average trust score \(trust_{t-1}\), and current trust score \(trust_{t}\). These data result in a small payload size. However, we experiment for up to 10 Kbs of payload size. It is noted that the block generation cost stays within reasonable bounds.  
Similarly, the cost of block reading stays consistent across all payload sizes, as shown in Figure 3. The block retrieval time for a 2 Kb payload size is 1.52 s, and that of 10 Kb stays at 1.87 s. This shows that the nodes retrieving trust values from the IOTA Tangle do not require extensive computing resources. This makes the proposed scheme cost-efficient in terms of resource consumption for different operations.  
The error bars in Figure 3 represent the variability in the measured times across 1000 iterations, quantified here as the standard deviation. For block retrieval times, which range from approximately 0.25 s to 0.29 s depending on the payload size, the corresponding error rates are extremely low, between 0.00044 s and 0.00068 s. This demonstrates highly consistent retrieval performance. Similarly, the block creation times vary from roughly 9.67 s to 11.97 s, with associated errors between 11.84 s and 14.93 s. Although these error values for block creation appear large relative to the averages, they reflect occasional spikes in processing time due to network latency and computational overhead during block formation. Together, these error bars provide a clear depiction of the stability and reliability of both block creation and retrieval operations under varying payload sizes. This confirms that the proposed trust evaluation scheme maintains predictable performance despite natural system fluctuations.