esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Applications for VR and AR in Medical Education and Surgical Planning
[PERSON] et al. [48] validated through cadaveric experiments that a robot-assisted AR navigation system can improve surgical accuracy and safety in mandibular reconstruction surgeries.
## 4 OTA Control Methods
### Chemical methods  
OTA detoxification with chemical compounds such as activated charcoal, solubilosamine, sodium and calcium aluminium silicates (mainly zeolites), continite, wood fragments or yeast [262-272] has been tested. Some of these compounds were tested in vivo; however, their activity was not as high as expected except for the activated charcoal [269]. Activated charcoal use, however, has been rejected due to the essential nutrient retention and animal poisoning [270]. Wine fining agents such as potassium caseinate or activated carbon have shown positive effects on OTA detoxification (reduction up to 82%) but they have also damaged some quality [271, 272]. A new insoluble vegetal fibre has been developed in order to adsorb the OTA present in liquid food products or, in the case of beer, present during the brewing step [274]. Currently, the most promising adsorbent materials are modified zeolites (reduction up to 72%) [274-277].
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 5 Performance Evaluations
### Results and Discussion
#### 5.2.2 Network Throughput Calculation  
The GSO-based CH selection process in our proposed CLEA-AODV routing protocol reduced the network's energy consumption. This led to achieving more TP during the process of communication. Hence, the TP produced by the proposed protocol was high compared to earlier works such as the AODV, SOC-GSO, and EENFC-MRP methods. Figure 8 explains the analysis of the network TP of the proposed CLEA-AODV protocol with the earlier models. The graph showed that the existing AODV, SOC-GSO, and EENFC methods completed their simulation with limited TP. The results prove that the CLEA-AODV protocol provided efficient TP values compared with the earlier protocols. The protocol created a cross-layer that provides route optimization and stability data. Finally, the CLEA-AODV method provided the maximum TP of 577 K  
Figure 6: The PSR with time.  
Figure 7: The PSR with the number of UAVs.  
amount throughout such as 296 kbps, 365 kbps, and 385 kbps was received by the earlier AODV, SOC-GSO, and EENFC-MRP methods.  
Figure 9 shows the TP calculation's comparative results in the number of UAVs with various scenarios such as 30, 60, 90, 120, 150, and 180 UAVs. The graph proves that the proposed CLEA-AODV protocol produced a very high TP in all of the scenarios compared with the earlier approaches. The proposed protocol maintained the range of 120 kbps to 160 kbps in all scenarios, whereas the others had a range of 50 kbps to 80 kbps.
## 2 Materials and Methods
### Data and Field Work
#### 2.2.2 Field Work  
Crop ground truth information was collected in the field from 15 to 30 April 2015. A total of 56 eligible parcels were mapped during the campaign (see Figure 1 for parcel location). The crop types identified in the field were mainly maize, but soybean, common (dry) bean (hereinafter designated as bean) and pasture were also observed (Table 2). Other crop types observed in the field were not considered in this study due to the small number of plots. The size of the parcels differs significantly, varying from a minimum of 0.43 ha to a maximum of 110.03 ha.  
During the field campaign, for each parcel, the crop type and its phenological growth stage according to the BBCH (\"Biologicalse Bundesanstalt, Bundessortenamt und CHemischeIndustrie\") scale [27] were registered. For maize, most of the parcels were in the senescence (Sen) stage, while 1 parcel was in the flowering (Fl) stage and another one in the leaf development (Lf) stage. All of the soybean parcels were in the senescence phase, while for bean, 1 parcel was in Lf; 3 parcels were in Fl; and 1 parcel was in the fruit development (Fr) stage. Pasture is harvested several times during the growing season; therefore, this crop is always in the Lf stage. Whenever possible, the sowing date was also collected for each parcel.  
In Figure 3, a crop calendar of the main crops observed in the Wako-Kungo irrigation perimeter is presented. These dates are the most common dates for these crops in this region. The sowing and harvesting periods for maize, bean and soybean are shown, while pasture is omitted because it is always in the crop development stage. According to Figure 3, the period covered by the satellite images does not cover the entire growing season.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop Type** & **No. of Parcels** & **Min. Area (ha)** & **Max. Area (ha)** & **Phenological Growth Stage** \\ \hline Maize & 28 & 4.48 & 110.03 & 1 Lf, 1 F1 and 26 Sen \\ Soybean & 13 & 4.47 & 48.95 & Sen \\ Pasture & 5 & 9.64 & 14.79 & Lf \\ Bean & 5 & 0.43 & 42.31 & 1 Lf, 3 Fl and 1 Fr \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crop type and number of parcels identified in the test area during the field work. Sen, senescence; Fl, flowering; Lf, leaf development.  
Figure 3: Crop calendar of the main crops for the Wako-Kungo irrigation perimeter.  
Additionally, the boundaries of each parcel were determined in the field using a GPS receiver, which permitted the creation of a polygon vector file in the WGS84/UTM 335 coordinate reference system. The local weather conditions were recorded at a local weather station (11 deg57''S, 14 deg59''S0''E) managed by the _Instituto de Investigacao Agraria_ (IIA). However, a few days after the end of the campaign, the station went out of order and was able to register data again beginning 13 October.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Problem-Specific Objective  
Certain metrics, either as extra terms or objective functions, only make sense for specific problems. This is valid for thematic areas, such as energy and routing problems, but also for different formulation types, such as non-linear and multi-objective approaches.  
Some approaches consider **multiple objectives**, yet many combine all into a single objective function. In this scenario, it is common to represent each in different weighted terms of the reward function [113; 161; 193; 249; 289; 296]. True multi-objective approaches [18; 132; 196; 257] consider each objective variable separately. Multi-objective goals are adversary: improving one will deteriorate the others.  
**Domain-specific** requirements might call for problem-specific rewards, which provide more meaningful performance evaluations by considering the context. This, however, hurts the generality of the approaches.
## 2 Modeling of Low-Carbon Scheduling Problem for Ship Segment Painting
### Illustrative Example  
The proposed low-carbon scheduling model for ship segmental painting results in different carbon emissions due to the different energy utilization rates of different painting groups in a given stage. If we prioritize lower carbon emissions, the work may be biased towards painting groups with higher energy utilization, while painting groups with lower energy utilization may be idle, which obviously reduces productivity and prolongs completion time. To illustrate this issue more clearly, the two stages where the main energy use is concentrated in a ship's segmented painting workshop, i.e., sandblasting and painting, are taken as examples. Below are the relevant production data.  
\[p_{i,j} =\left[\begin{array}{cc}10&10\\ 10&20\\ 30&20\\ 20&10\end{array}\right]f_{m,mr,1}=\left[\begin{array}{cc}1&1\\ 1&1\end{array}\right]v_{m,i}=\left[\begin{array}{cc}0.8&0.6\\ 0.6&0.8\end{array}\right]\] \[p_{i}^{\mathrm{W}} =\left[\begin{array}{cc}6&5\end{array}\right]p_{i}^{\mathrm{F}}= \left[\begin{array}{cc}1&1\end{array}\right]p_{i}^{\mathrm{F}}=2\] \[t_{j,j\prime,1} =\left[\begin{array}{cccc}5&10&5&10\\ 15&10&10&5\\ 10&5&5&5\\ 10&20&10&10\end{array}\right]t_{j,j\prime,2}=\left[\begin{array}{cccc}10&5& 5&5\\ 10&5&5&5\\ 10&5&10&5\\ 5&10&5&5\end{array}\right]\] \[T_{j,j\prime,1}^{\mathrm{S}} =\left[\begin{array}{cccc}10&20&20&10\\ 30&20&10&20\\ 20&10&10&10\\ 10&20&10&10\end{array}\right]T_{j,j\prime,2}^{\mathrm{S}}=\left[\begin{array} []{cccc}20&20&10&10\\ 20&10&10&10\\ 20&10&10&20\\ 10&20&10&10\end{array}\right]\]  
The EC and MS of the ship segmental painting low-carbon scheduling workshop model using CPLEX12.1 are assigned weight vectors of size 0 to 1 with uniform distribution. It can be seen from Figure 2 that the total carbon emission of the painting workshop decreases with the increase in the maximum completion time, which indicates that EC and MS cannot be optimized at the same time; in addition, we can get the best EC and MS in different periods, so the relationship between these two objectives can be explained.  
To illustrate the total carbon emission calculation, the carbon emission factor is taken as an example of scheduling in Figure 3, where the white rectangles indicate the adjustment time, the dashed rectangles indicate the transportation time, and the colored rectangles indicate the processing time. In the first stage of painting team 2, the total processing time is 30 + 10 = 40, and the processing carbon emission is 40 \(\times\) 6 \(\times\) 0.7559 \(\div\) 0.6 = 302.36. The adjustment carbon emission is (10 + 20) \(\times\) 0.7559 = 22.677. The idle time is 0, and its carbon emission is also 0. Similarly, the carbon emission generated by the processing of the painting team in the other stages can be calculated. In addition, the carbon emission for transportation is 8 \(\times\) 2 \(\times\) 0.7559 = 12.0944. Thus, the final total carbon emission is 980.4023.  
Figure 2: Completion time versus carbon emissions for the segmented painting workshop.
## 3 Results and Discussion
### Influences of the Processing Parameters on Cutting Quality
#### 3.2.2 Kerf Width  
Stents' struts are very small, and their widths are about \(150-250\) um. To ensure the accuracy of strut width, path skewing of about one half the kerf width from the design path is necessary in the laser cutting process. To study the kerf width quantitatively, three positions, the entrance, middle, and exit of the kerf, were measured. To eliminate the impacts of machine rotating precision and measuring error, three kerfs around the ring were measured and then averaged. The samples were processed with acid before measuring. Figure 10 shows the influence of the process parameters on the kerf width.  
Typical horn-kerrs were obtained in this research. All kerfs' middle widths were narrower than the entrance and exit widths. Kerf width gradually increased with increasing repetition rate and pulse width. Increasing the repetition rate or pulse width increased the power density, as well as the mass of melt. Accordingly, the kerf width was enlarged. The increased power also made a wider kerf. However, the effect was not as obvious as increasing pulse width or repetition rate.  
Figure 10: Influence of the process parameters on the kerf width: (a) repetition rate; (b) pulse width; (c) power; (d) cutting speed.  
Increasing cutting speed resulted in a widening of kerf width. However, when the speed exceeded a specific value, the kerf was narrowed. Cutting speed influenced the mass of the melt, the cooling effect, combustion-supporting effect, as well as the ejecting strength. A low cutting speed would lead to a higher power density and a greater mass of melt, but the cooling effect was strong, which would take away the amount of heat and limit the mass of the melt. Thus, the cutting kerf was narrow. With a high cutting speed and low power density, the mass of molten melt was limited. Weakening the ejecting-strength with a high cutting speed also contributed to narrowing the cutting kerf, which is why the cutting kerf was narrow with both low and high cutting speeds.
## 4 The Structure Fidelity Data Collection Framework
### Nodes Scheduling Scheme Based on the Structural Similarity Index
: _Let \(Y_{i}^{k}\) be the nodes data sequence after inserting the reading \(x_{ij}\) of the \(j\)-th node at the time \(i\) into \(Y_{i}^{k-1}\), the contribution rate of node \(j\) to the structure of reference node data sequence \(X_{i}\) is defined as:_  
\[\alpha_{j}=SSIM(X_{i},Y_{i}^{k})-SSIM(X_{i},Y_{i}^{k-1}) \tag{12}\]  
_where_  
\[SSIM(X_{i},Y_{i}^{k})=\frac{4\mu_{X_{i}}\mu_{Y_{i}^{k}}\sigma_{X_{i}Y_{i}^{k}} }{(\mu_{X_{i}}^{2}+\mu_{Y_{i}^{k}}^{2})(\sigma_{X_{i}}^{2}+\sigma_{Y_{i}^{k}} ^{2})}\]_and_  
\[SSIM(X_{i},Y_{i}^{k-1})=\frac{4\mu_{X_{i}}\mu_{Y_{i}^{k-1}}\sigma_{X_{i}Y_{i}^{k-1 }}}{(\mu_{X_{i}}^{2}+\mu_{Y_{i}^{k-1}}^{2})(\sigma_{X_{i}}^{2}+\sigma_{Y_{i}^{k-1 }}^{2})}\]  
In order to statistically assess the node contribution rate, the deviation and SSIM index can be extended over multiple time instances. We still use \(X\) to denote the data sequences of all nodes as described in Equation (13). The cluster head generates the first data matrix \(Y^{1}\) represented by Equation (14) where the elements for each row are identical and equal to \(x_{iC}\) (\(1\leq i\leq M\)). \[X= \begin{bmatrix}X_{1}\\ X_{2}\\ \vdots\\ X_{M}\end{bmatrix} = \begin{bmatrix}x_{11}&x_{12}&\cdots&x_{1(N-1)}&x_{1C}\\ x_{21}&x_{22}&\cdots&x_{2(N-1)}&x_{2C}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ x_{M1}&x_{M2}&\cdots&x_{M(N-1)}&x_{MC}\end{bmatrix}  \tag{13}\]  
\[Y^{1}= \begin{bmatrix}Y_{1}^{1}\\ Y_{2}^{1}\\ \vdots\\ Y_{M}^{1}\end{bmatrix} = \begin{bmatrix}x_{1C}&x_{1C}&\cdots&x_{1C}&x_{1C}\\ x_{2C}&x_{2C}&\cdots&x_{2C}&x_{2C}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ x_{MC}&x_{MC}&\cdots&x_{MC}&x_{MC}\end{bmatrix} _{M\times N} \tag{14}\]  
If the MSD of a candidate node and the cluster head node is maximal among all such nodes, then the descriptive power of the active nodes set will maximally grow if the candidate is added into it. The values of MSD for all member nodes and the cluster head are known to the cluster head based on the cluster head selection procedure, without any increase in the computing load. We can easily obtain the value of \(SSIM(X,Y)\) defined in Equation (8) to evaluate the fidelity of the data from active nodes set. We named the active nodes selection as the \(Max_{-}MSD\) approach. The detailed algorithm for finding the set of active nodes with a minimal number of nodes in a cluster is presented in Algorithm 2:  
```
0: predefined fidelity threshold \(\delta\)
0: a set of active nodes \(A\)
1:\(A\)={CH node}, \(\bar{A}\)={all member nodes}
2:\(k=1\)
3:while\(SSIM(X,Y^{k})<\delta\)do
4: Candidate node \(j=\{i|\max(MSD(i,CH)),i\in\bar{A}\}\)
5: Put \(j\) into \(A\) and remove from \(\bar{A}\)
6:\(k=k+1\)
7:endwhile
8:Output the node set \(A\)
```  
**Algorithm 2** Active nodes selection
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 3 Simulation Study
### Simulation Study 1: Two Items per Testlet
#### 3.1.2 Results  
Table 1 presents the average absolute bias, average standard deviation, and average coverage rate of item discriminations \(a_{i}\) and item intercepts \(b_{i}\). The condition \(f=0\) corresponds to the situation of local independence, while \(f>0\) indicates situations of local dependence.  
First, it can be concluded that all five estimation methods, MML1, PML1, MML2, PML2, and PML3, provided nearly unbiased estimates for item discriminations and item intercepts. Notably, there is a small bias for a sample size of \(N=500\), but this bias vanishes with an increasing sample size. As expected, the standard deviation of the item parameter estimates decreases with the increasing sample size. In the case of \(f=0\), the efficiency of the different estimators can be defined by the standard deviation. We define a percentage efficiency loss by relating the standard deviations of PML1, MML2, PML2, and PML3 to the standard deviation of the MML1 method. It was found that PML based on all items (i.e., PML1) had a very small average efficiency loss of 0.5% for item discriminations \(a_{i}\). In contrast, the PML2 and PML3 methods had a larger efficiency loss of 2.7%. MML under the normal copula model (i.e., MML2) had a slightly smaller efficiency loss of 2.4% for the \(a_{i}\) parameters. The efficiency loss for item intercepts \(b_{i}\) was smaller than for item discriminations \(a_{i}\). It was 0.3% for PML1, 0.5% for MML2, 0.7% for PML2, and 0.7% for PML3.  
In the conditions with locally dependent item responses (i.e., \(f=0.35\), and 0.7), MML1 and PML1 produced strongly biased estimates, while MML2, PML2, and PML3 were nearly unbiased in all conditions. The bias was larger for larger residual correlations \(f\). Notably, PML2 resulted in estimates almost identical to those of PML3.  
The coverage rates were satisfactory for item parameters that were unbiased. In line with the bias findings, the coverage rates were too low for MML1 and PML1 (i.e., assuming local independence) if the item response data were simulated under local dependence.
###### Abstract  
Pulsars' signals reaching the atmosphere can be considered being stable under certain assumptions. In such a case the ionosphere remains the main factor distorting signal from the extraterrestrial sources, particularly if we observe them at long radio waves. In this article we present the results of the analysis of relative peak flux changes for two selected pulsars: PSR J0332+5434 (B0329+54) and PSR J1509+5531 (B1508+55), observed with the long radio wave sensor (The PL612 Low Frequency Array (LOFAR) station in Baldy), together with the analysis of Rate of TEC (ROT) parameter changes measured with the Global Navigation Satellite Systems (GNSS) sensor (IGS LAMA station (IGS: International GSSN Service)). The main objective of the work is to find if the rapid plasma density (observed with the Rate of Total Electron Content (TEC)) has a counterpart in the pulsar observation characteristics. This focuses the attention on ionosphere influence during pulsar investigations at low radio frequencies. Additionally, what was the aim of this work, our results give reasons for using pulsar signals from LOFAR together with GNSS data as multi instrumental ionosphere state probes. Our results show a clear anti-correlation between the ROT and the pulsar profile's peak flux trends.  
radio astronomy; LOFAR; pulsar; ionosphere; space weather; GNSS +
Footnote †: journal: Sensors
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 4 Discussion
### Daily and Monthly Evaluation of IMERG Products
According to the authors, the spatial resolution (2.5\({}^{\circ}\)) and dependence on passive microwaves and infrared precipitation recoveries used by satellites may imply the worst performance of representing precipitation in locations with orographic-type precipitation. Despite presenting higher spatial resolution (0.1\({}^{\circ}\)), IMERG products also showed less accuracy for estimating rainfall in orographic regions. The estimative of the precipitation in the mountainous region tends to be underestimated towards the ocean (east area), where orographic rains occur. On the opposite side, after the sudden elevation change, it tends to overestimate the precipitation. Corroborating these results, [55] also observed trends of underestimation of precipitation by the TRMM (2A25 version 7) product in regions of orographic rainfall and overestimation in regions of valleys or flat areas in the southeastern Appalachchains. According to the authors, this behavior occurs due to the spatial resolution and the correction of soil disorder made by the satellite. Another possibility for underestimating precipitation in the coastal region is that precipitation occurs while the top of the cloud is still relatively warm. Satellites are unable to fully identify rain, as heat exceeds infrared thresholds and the lower amount of ice in the air makes detection by passive microwave sensors difficult, and thus satellite products detect only part of the precipitation [2; 56; 57]. In addition, in mountainous regions, precipitation is extremely variable and there are changes in rainfall distribution over short distances [58], which can result in a representation of precipitation with less accuracy in these areas, since satellite products have the limitation of estimating precipitation considering the pixel size, when compared to gauges that measure the precipitation in-situ. In the coastal area, the highest peak of precipitation occurs in the summer and is related to the predominant role of the Atlantic Tropical Mass [59], which finds the mountain as a physical barrier, culminating in orographic rains, with great volume over a short duration interval. Such an event may not be fully captured by satellites, as observed by [60] on the west coast of the United States, in the analysis of the precipitation of six satellite products (AFWA, TMPA-3B42, TMPA-3B42 RT, CMORPH, PERISANN, and NRL) and [33] in China, with IMERG version 5 products. Despite notable improvements in version 6 of IMERG's algorithm over previous versions as detailed by [30; 58] observed lower accuracy of IMERG version 6 for estimating rainfall on the coastline of the Adriatic Sea in Europe, due to complex orography terrain. Additionally, [61] related the limitation of estimating precipitation in the Ebro River basin in Spain, in an area where the weather was dominated by the advection of wet maritime air masses. Thus, the measurement of the precipitation over coastal locations continues to be a challenge and deserves further research.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 2 Classification of Experimental Methods  
Developments in experimental methods mainly reflect specific research objectives, related to multiphase flow. This largely determines the diversity of methods used to characterize particle-laden gas flows.
## 5 Analysis Techniques for WebAssembly
### Detecting Vulnerabilities in WebAssembly Smart Contracts
#### Dynamic Analysis  
**EOSFuzzer**. EOSFuzzer [18] uses black-box fuzzing to detect vulnerabilities in EOSIO smart contracts. To this end, EOSFuzzer first performs static analysis on the WebAssembly code and Application Binary Interface (ABI). The results of this analysis are then used to generate fuzzing inputs, which are applied to the smart contract through the Cleos [75] command-line client. Finally, EOSFuzzer performs vulnerability analysis based on test oracles. Although EOSFuzzer was found to be relatively efficient, it has the lowest precision and recall of all methods proposed. Additionally, EOSFuzzer uses random seeds for fuzzing, resulting in low code coverage.  
**WASAI**. WASAI [64] uses concolic fuzzing to detect vulnerabilities in EOSIO smart contracts. To address the weaknesses of EOSFuzzer, it strategically generates seeds to aid the fuzzing in exploring as many feasible paths as possible. This is achieved by performing symbolic execution to feedback on the seed mutation. This results in double the code coverage than EOSFuzzer. WASAI was able to detect the most vulnerabilities out of all the proposed techniques. It additionally has high precision and recall and was found to be resilient to code obfuscation.