esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To eliminate stripe distortion caused by uneven reflectivity and large curvature changes on the surface of the object, [PERSON] [18] proposed a stripe preprocessing method to adjust the stripe distortion grayscale. Additionally, the center of the stripes is extracted by the curve-fitting method of preprocessing. The integrity and reliability of stripe center extraction are ensured for the measurement of surfaces with large curvature variations. The stripe center locating methods mentioned above optimize the center extraction accuracy. However, when the distribution of the stripe degenerates, it is difficult for the improved center locating methods to handle the measurement results with large error due to the serious degeneration. The large error data is typically recognized manually, which is not objective. Therefore, an automatic objective method for recognizing large errors in data is needed. [PERSON] [19] proposed a method for evaluating the quality of structured light stripe images. This method evaluates stripe quality based on the third-order center distance and the Human Vision System (HVS) without reference. It can recognize large errors in data by setting thresholds of evaluated results. This method needs two steps, which are calculating the evaluation parameters and setting a threshold for the parameters. Therefore, it is complex and not fast enough. Deep neural network algorithms have achieved remarkable results in many engineering fields in recent years [20; 21; 22; 23], and residual neural networks (ResNet) have been increasingly used in detection tasks. [PERSON] et al. used deep ResNets for defect detection in reinforced concrete to achieve the intelligent classification of images of single concrete defects [24]. [PERSON] et al. proposed a ResNet classifier applied to bearing fault detection, and the trained classifier can achieve 100% accuracy [25]. [PERSON] et al. proposed a parallel ResNet-based method for the automatic detection and measurement of pavement cracks and demonstrated the reliability of the method through experiments [26]. From the above analysis, it can be seen that the traditional method of image processing-based structured optical stripe image quality evaluation requires the manual selection and adjustment of threshold parameters, which is complex and non-objective. Therefore, in order to automatically and objectively identify large error data, this proposed a solution, the flow chart of which is shown in Figure 2. Section 2 of this paper introduces the basic principles of structured light measurement systems and residual neural networks. The evaluation criteria for defective and non-defective stripes are given in Section 3.1. Section 3.2 introduces the method of establishing the datasets. Section 4.1 describes the building of the structured light measurement system. Section 4.2 presents the details of the parameters during the training of the residual neural network and shows the training results. Section 4.3 describes the testing and evaluation of this method, and also compares this method with conventional methods. Finally, a conclusion is given in Section 5.
## 4 Discussion
### Merging of Patches  
For the detection and delimitation of treetops, a Mask R-CNN was used, whose input are patches of images with significantly smaller areas (limit of 256 \(\times\) 256 or 512 \(\times\) 512 pixels) than the complete field. This approach is common in studies involving object detection or instance segmentation in remote sensing images [13; 15; 19; 20; 25], as they are representations with variable dimensions and are generally superior to conventional images. However, unlike the studies mentioned, in our study an algorithm for automated mosaicking of the results is proposed, which presents a significant performance and eliminates the need for manual post-processing of the results obtained in each image fraction.  
There are few studies that address some similar processing for applications in instance segmentation. Ref. [29] proposed a similar approach for detecting crowns in dense forests, joining any two trees at the edges of patches with polygonal intersections among them. Ref. [28] proposed a moving window mosaicking method applied in an analogous region as the overlapping of the patches in this study. From a modification of the non-maximal suppression algorithm, these authors perform the deletion of redundant objects with a methodology similar to condition 2.3 of the proposed algorithm but considering only the bounding box of the masks. Nevertheless, these techniques are not robust enough to contemplate the possibility of a real intersection in two neighboring crowns and, above all, the filtering of large false positives that overlap more than one real crown (filtered by condition 2.2 of the proposed approach), would increase errors by omission and commission, respectively, if they were applied for the same purpose of this study. Therefore, the proposed method has a strong potential to allow the analysis focused on detection and delimitation of canopies in plots with high planting density--where the overlap between canopies is natural from images of large areas applied to the Mask R-CNN architecture.
## 5 Results
### Statistical Characterization of Network Traffic
A statistical summary of payload sizes grouped by session is presented in Table 8, confirming a consistent traffic generation behavior across sessions, although with variable dispersion due to the dynamic nature of the protocols. To quantify the temporal regularity of the captured network traffic, we computed the inter-arrival time (IAT) between flows and derived the coefficient of variation (CV = \(\sigma/\mu\)) for each experimental session, where \(\sigma\) is the standard deviation and \(\mu\) is the mean of \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Session** & **Flows** & **Mean** & **Std** & **Min** & **Max** & **Median** \\ \hline
1 & 38 & 3035 & 14,001.63 & 84.0 & 86,184.0 & 128.0 \\
2 & 95 & 14,471.41 & 117,323.18 & 1.0 & 1,138,103.0 & 86.0 \\
3 & 82 & 23,923.40 & 148,302.42 & 2.0 & 1,138,112.0 & 86.0 \\
4 & 38 & 1289.05 & 4590.80 & 86.0 & 23,858.0 & 86.0 \\
5 & 144 & 16,665.53 & 114,018.47 & 1.0 & 1,139,957.0 & 86.0 \\
6 & 115 & 19,581.75 & 124,462.67 & 2.0 & 1,147,051.0 & 86.0 \\
7 & 154 & 38,948.31 & 304,693.03 & 2.0 & 3,530,678.0 & 86.0 \\
8 & 87 & 24,760.83 & 145,785.03 & 1.0 & 1,147,041.0 & 86.0 \\
9 & 21 & 80,373.57 & 256,965.51 & 96.0 & 1,147,055.0 & 344.0 \\
10 & 135 & 4886.79 & 42,429.40 & 1.0 & 487,032.0 & 86.0 \\
11 & 115 & 5414.64 & 43,812.40 & 1.0 & 460,128.0 & 86.0 \\
12 & 101 & 5515.85 & 41,928.94 & 2.0 & 412,812.0 & 86.0 \\
13 & 151 & 12,638.07 & 99,475.79 & 1.0 & 1,104,883.0 & 86.0 \\
14 & 35 & 16,538.82 & 73,637.35 & 1.0 & 429,804.0 & 200.0 \\
15 & 105 & 4852.41 & 37,277.29 & 1.0 & 374,196.0 & 86.0 \\
16 & 316 & 2353.20 & 30,005.28 & 1.0 & 521,388.0 & 126.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Payload distribution (bytes). Figure 9: Aggregate network throughput per 15 min (Bytes/s). The shaded area represents the variability band, calculated as \(\pm\)1.96 times the standard deviation of the throughput, providing an approximate 95% confidence interval around the observed values. the sample. As shown in Table 9, the CV values ranged between 1.12 and 1.59 across all sessions, indicating a moderate-to-high degree of burstiness in traffic patterns. A CV greater than 1 suggests that flows are not evenly spaced in time, but instead tend to arrive in clusters, a common behavior in IoT scenarios where data are often generated in response to physical events or time-triggered batches. These results confirm the suitability of the testbed to emulate realistic traffic conditions in constrained environments, and provide a baseline for detecting temporal anomalies in future experiments. Temporal variability is an important feature in anomaly detection models, as deviations from typical burstiness patterns can indicate malfunctioning nodes, denial-of-service conditions, or unexpected external interactions. This analysis helped characterize baseline activity patterns in rural IoT scenarios, providing a reference for detecting anomalies or evaluating the impact of protocol and scheduling strategies in future work.
## 4 Results and Discussion
### Evaluation Matrices  
We used the most common distance matrices as follows:  
_Mean squared error (MSE):_  
Mean squared error quantifies the average squared difference between predicted values and actual values. It is defined as:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} \tag{11}\]  
where:  
\(n\) is the number of samples or data points;  
\(y_{i}\) are the actual values;  
\(\hat{y_{i}}\) are the predicted values.  
_Root mean squared error (RMSE):_  
Root mean squared error is the square root of MSE, which gives a measure of the average magnitude of the error. It is calculated as follows:  
\[\text{RMSE}=\sqrt{\text{MSE}}=\sqrt{\frac{1}{n}}\sum_{i=1}^{n}(y_{i}-\hat{y_{i }})^{2} \tag{12}\]  
_[PERSON]'s correlation coefficient:_  
[PERSON]'s correlation coefficient measures the linear correlation between two variables. For a sample, it is calculated as:  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i=1 }^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{13}\]  
where:  
\(n\) is the number of samples;  
\(x\) and \(y\) are the individual data points;  
\(\overline{x}\) and \(\overline{y}\) are the average means.  
[PERSON]'s coefficient \(r\) ranges from \(-1\) to 1:  
\(r=1\): Perfect positive correlation;  
\(r=-1\): Perfect negative correlation;  
\(r=0\): No correlation.
## 5 Simulation Results
### Impact of Buffer  
Figure 4a-d show the comparison in terms of network performance with respect to different buffers. From these figures, we can find that the network performance increases with the buffer. Specifically, as the buffer increases, the message delivery ratio increases, and the average delivery latency, the overhead, and the hop count decrease. This is because the LQFC protocol adopts the multi-copy transmission scheme, nodes transfer copies of all the messages they have to all the other nodes they become in contact. The message is discarded when it expires or a destination delivery acknowledgment is received. However, due to the limited buffer (storage space), the protocol performance (the message delivery ratio, the average end-to-end delay, and the overhead) degrades significantly when the number of nodes is large. In order to overcome this problem, the LQFC protocol define a utility function with the NEF and the message TTL taken into account, based on which a redundant copy-deleting approach is devised to limit the flooding of messages to a certain number of copies. Increasing the buffer allows for more messages to be stored, and therefore, appropriately increasing the node buffer can effectively improve the network performance of UAV-assisted VANETs.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method for Real Observations Analysis  
#### 3.3.1 Evaluation Sites  
The temporal consistency of TOC products was evaluated over a large set of globally distributed and spatially homogeneous land sites. To this purpose, the BELMANIP-2 (BEnchmark Land Multisite ANalysis and Intercomparison of Products) list of sites was considered [34]. These sites were originally proposed as benchmark for intercomparison of satellite-derived coarse resolution biophysical products [34]; to this end, the sites were selected to be homogeneous at kilometric scale, almost flat and with a minimum proportion of urban area or permanent water bodies. This site selection is well suited for the purpose of the present study, since it allows verifying the presence of orbit-induced drifts and their correlation with latitude, biome type or environmental conditions. The location of the considered BELMANIP-2 sites is displayed in Figure 3 together with the corresponding global land cover class, derived from the ESA Climate Change Initiative (CCI) Land Cover Version 2.0 product [35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Blue** & **Red** & **NIR** & **SWIR** \\ \hline \(f_{iso}^{MODIS}\) & 0.0204 & 0.0296 & 0.4108 & 0.2108 \\ \(f_{Vol}^{MODIS}\) & 0.0196 & 0.0299 & 0.2835 & 0.1845 \\ \(f_{Gop}^{MODIS}\) & 0.0042 & 0.0064 & 0.0723 & 0.0495 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BRDF parameters used for the simulation.
## 2 Materials and Methods
### Adsorption of As(V) and Cr(III) in IJ  
The experimentation carried out was based on previous work [14] conducted with synthetic potassium jarosite, but in this case, the focus was on the adsorption of both the As(V) and Cr(III) in IJ. Known solutions of As(V), Cr(III), and As-Cr (1454.2 ppm As and 1099.3 ppm Cr) were prepared. Subsequently, the adsorption process was carried out, and the remaining liquid was analyzed by ICP, while the solid was characterized by DRX, SEM-EDS, FTIR, and XPS. Figure 1 shows the experimental setup, which consists of 3 phases as described below.  
Firstly, to carry out the decomposition of the IJ, 3 g of material was added to 500 mL of a NaOH ([PERSON] brand, Mexico City, Mexico) solution until a pH of 10 was reached, which was kept constant for 10 min at room temperature. At the end of the 10 min reaction, 5 g of Na\({}_{2}\)HAsO\({}_{4}\cdot\)7H\({}_{2}\)O (Sigma-Aldrich, Burlington, MA, USA) was added, corresponding to 0.03 M. Finally, after a reaction time of 120 min, nitric acid was added to attain a pH of 1.1. After 5 min, the solution was filtered, washed, and dried for subsequent characterization to determine the amount of As(V) adsorbed. The same procedure was carried out with the addition of 3 g of IJ to a NaOH solution until a pH of 10 was attained (adding about 5-6 drops of a 1 M NaOH solution) for 10 min at room temperature. After this time, 4 g of CrCl\({}_{3}\cdot\)6H\({}_{2}\)O ([PERSON] brand, Mexico City, Mexico) was added, which corresponds to 0.03 M. Finally, after 120 min of reaction, drops of 1M HNO\({}_{3}\) were added until the pH reached a value of 1.1. In a third experimental series, both reagents (As and Cr) were added simultaneously in a process like that described above.  
The monitoring of As(V) and Cr(III) adsorption was carried out using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Solutions with known concentrations of As(V) and Cr(III) were prepared, and IJ samples were placed into these for 1 h with  
Figure 1: Experimental setup for the adsorption of As(V) and Cr(III) in the IJ and the ACC + IJ.  
moderate stirring. At the end, the remaining liquid was analyzed by ICP, and the percentage of As(V) and Cr(III) adsorbed in the JJ samples was determined by the difference.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 6 Discussion  
The following subsections explore the benefits of PHY tuning for reduced latency and BER. They are based on the results presented in Section 5.
## 3 Results and Discussion  
We performed untargeted lipidomics of microplasma-irradiated cells in order to investigate the effects of microplasma irradiation on cellular lipids, particularly cell membrane lipids. The microplasma irradiation conditions employed in this study were based on our previous research, wherein a voltage of 4.0 kV was identified as optimal for facilitating the absorption of larger molecules into cells [12].  
In this study, two independent sets of samples, labeled as set A and set B, were subjected to LC/MS analysis (Figure 1). Set A consisted of three groups of cell samples: (i) non-irradiated (control); (ii) subjected to microplasma irradiation at 4.0 kV, followed by 0 h of incubation (A4K0H); and (iii) microplasma irradiation at 4.0 kV, followed by 1 h of further incubation (A4K1H). On the other hand, set B samples were divided into four groups: (i) non-irradiated (control); (ii) microplasma irradiation at 4.0 kV, followed by 0 h of incubation (B4K0H); (iii) microplasma irradiation at 4.0 kV, followed by 12 h of further incubation (B4K12H); and (iv) microplasma irradiation at 4.5 kV, followed by 0 h of incubation (B4.5K0H).  
According to the LIPID MAPS ([[http://www.lipidmaps.org/](http://www.lipidmaps.org/)]([http://www.lipidmaps.org/](http://www.lipidmaps.org/)), accessed on 2 May 2024), lipids have been divided into eight categories as follows: fatty acyls (FA), glycerolipids (GL), glycerophospholipids (GP), sphingolipids (SP), sterol lipids (ST), phenol lipids (PL), saccharolipids, and polyketides [19]. In this study, we used LipidSearch software to annotate and quantify all available lipid classes detected in the control and treated cells. After filtering the annotations (parameters for filtration are listed in Table S1), a total of 1724 and 1577 lipid species were identified in set A and set B samples, respectively (Table 1). These lipids belong to five categories (GL, GP, SP, ST, and PL) and twenty-one classes. The four most abundant lipid classes (in terms of the number of individual species) detected in both sets of samples were phosphatidylcholine (PC), phosphatidylcholine (PE), triglyceride (TG), and ceramide (Cer) (Table 1).
## 2 Materials and Methods  
### Fabrication  
MoTaNbVW reinforcement has been produced by vacuum arc melting. Details on the fabrication route and attained microstructures can be found elsewhere [48; 49; 50; 51]. Afterwards, HEA samples were crushed into fine particles using a planetary mill (Fritsch Pulverisette 7 Premium Line, Idar-Oberstein, Germany). The samples were ground in a bowl with a capacity of 20 mL and 10 grinding balls with 10 mm diameter, the grinding time was 10 min at 850 rpm. The process took place in intervals of 20 s grinding and 80 s of pausing to prevent the samples from overheating. The achieved fineness of the HEA powder was approximately 40 \(\upmu\)m. Commercial aluminium AA-1050 powder (purity 99.5%) was used as the matrix and the equiatomic refractory high entropy alloy MoTaNbVW as the reinforcement in order to produce composites of various compositions (0, 1, 3, and 5 vol% reinforcement). A powder metallurgy route was followed as the fabrication technique.  
For each configuration, the powder of Al and HEA were weighted and stirred into a beaker with methanol to avoid agglomeration and left to dry into a hot plate stirrer. The final powders for the monolithic alloy and Al-HEA composites were compacted into a cylindrical pellet of 14 mm in diameter using a hydraulic press at 7.6 tons of force. Each specimen had a weight of approximately 5 g. Due to the variation of volume and density of the reinforcement, the height of each pellet ranged from 12 mm for the monolithic alloy to 11.2 mm for the 5 vol% composition. Then, the pellets were carried into a steel die with a diameter of 15.3 mm (slightly bigger from the cold press specimens to avoid a breakdown during the insertion), covered with graphite powder for lubrication, and sintered into a hot press furnace (manufactured by Termolab, Agueda, Portugal). The final height for specimens ranged from 10.2 mm for the monolithic alloy to 9.2 mm for the 5 vol% composition. Sintering was executed in three steps. Initially, the temperature was increasing at a rate of 50 \({}^{\circ}\)C/min until it was stabilised at 550 \({}^{\circ}\)C under vacuum conditions. Afterwards, after 1 h, the loading process began gradually at a rate of 1000 N/min until the press obtained the desired load of 6500 N. The load was applied to the specimens for 30 min. Then, the specimens were left to cool in the furnace.  
Afterwards, specimens have been subject to standards metallographic procedures for further analysis. More specifically, each specimen was cleaned after extraction from the die to remove impurities. The surface of the samples was ground with SiC sandpapers with final grit of 1200. Polishing was carried on with the use of a fine polishing cloth and diamond spray of 1 micron. After polishing, each specimen was rinsed with ethanol to remove contamination. The final outcome was a mirror-polished surface.
## 3 Triangle Metric Based Multi-Layered Routing Protocol
### Network Architecture
Therefore, utilizing supernodes can help in reducing energy consumption and further optimize the network lifetime. It is highlighted that we utilized a supernode to identify one-layer among the supernodes. The nodes placed in the identified layer can forward the data packet directly to the supernode, more precisely, supernodes responsible for determining the NSN. On the other hand, nodes placed on the bottom of NSN identify its location locally (i.e., depth information). The residual energy and TM are utilized to calculate the route cost for underwater communication networking. This technique results in overall network performance and greener communication considering higher energy resources and dedicated operation areas without creating communication overheads in the overall underwater network environments. It is also highlighted that anchor and beacon node-based approaches lead to less energy consumption and overhead communication reduction in terms of beaconing messages. This is very well presented considering 3D localization in wireless networking [44; 45]. However, the supernode based approach is about deploying special nodes for particular purpose operations, which results in overall network performance and greener communication considering higher energy resources and dedicated operation area in the network without creating communication overheads in the overall underwater network environments. Figure 2: The proposed supernode layering based underwater network architecture. It is highlighted that the non-location-based consideration is a step closer toward reducing information exchange overhead in underwater networking by focusing on supernode consideration in the networking scenarios. Theses nodes have more information about the surrounding local network environment resulting in a lower number of hello packet information exchange in the overall network environment. This further reduces energy consumption toward green computing scenario in underwater networking environments. Regarding neighbor node consideration, we want to clarify that the accurate neighbor node information helps in making better forwarding decisions. For example, in the case that a higher number of neighbor node lesser packets should be transmitted considering duplication probability, whereas in the case of sparse scenario with lower number of neighbor nodes, more packets might need to be transmitted for increasing reachability probability.