esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 2 Data and Methods
### Matchup Criteria  
In this section, we describe how profiles were collocated in time and space (matchups) in a way that temperature profiles from NUCAPS and COSMIC are comparable. We examine all NUCAPS profiles within 150 km of the retrieved COSMIC. This radius was selected to represent the maximum FOR diameter of NUCAPS, which is \(\sim\)150 km at the scan edge and \(\sim\)50 km at nadir in the Tropics. As a result, there will be more matches profiles at the smaller scan angles. The study performed by [26] shows relative insensitivity in the bias and root mean square error between sounding and COSMIC temperature retrievals when compared using a variety of horizontal matching techniques. CAA events can last from hours to days [3], so we matched all profiles retrieved within the same calendar day. Figure 2(a) shows an example of NUCAPS temperature retrievals along the 200 hPa isobar on 26 February 2018 over the study domain (40-80\({}^{\circ}\) N, 169\({}^{\circ}\) E-131\({}^{\circ}\) W). For clarity, these temperature values were projected onto a 0.5\({}^{\circ}\times 0.5^{\circ}\) grid in this figure. Blue coloring shows where the air temperature is below 208 K and indicates the presence of CAA, which can extend for thousands of square kilometers. These isobaric slices are displayed using the same procedure as Gridded NUCAPS [7], which is the primary visualization for CAA in AWIPS during operational forecasting. Figure 2(b) is the same as Figure 3a, but only showing the matchups between NUCAPS and COSMIC that meet the criteria that we defined for this study.  
Figure 4: (**a**) NUCAPS profiles showing the matchups between COSMIC (black dashed line) and NUCAPS (blue solid lines), where the shaded blue box indicates the temperature and pressure criteria for CAA. The profile locations are annotated in Figure 3. (**b**) The mean (solid line) and standard deviation (error bars) of the averaging kernel matrix (AKM) diagonal vectors are a function of pressure in the study domain on 26 February 2018.  
Figure 3: A National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) horizontal slice along the 200 hPa isobar on 26 February 2018 over the study domain, 40–80\({}^{\circ}\) N, 169\({}^{\circ}\) E–131\({}^{\circ}\) W. (**a**) Several combined overpasses between 1100 and 1700 UTC and (**b**) matchups between with Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) profiles within 150 km for the same time period. Blue coloring indicates where the air temperature is below 208 K (\(-\)65 \({}^{\circ}\)C) to distinguish the presence of a CAA event. Profiles annotated in (**b**) are shown in Figure 4.
## 2 Notation and Background
### Euclidean Diffusion Bridges and Simulation  
Suppose a strong solution exists to an SDE of the form  
\[dx_{t}=b(t,x_{t})dt+\sigma(t,x_{t})dw_{t},\]  
where \(b\) and \(\sigma\) satisfy certain regularity conditions and where \(w\) denotes an \(\mathbb{R}^{n}\)-valued Brownian motion. In this case, \(x\) is a Markov process, and its transition density exists. Suppose we define the function  
\[h(t,x)=\frac{p_{T-t}(x_{t},v)}{p_{T}(x_{0},v)},\]  
for some \(x_{0},v\in\mathbb{R}^{n}\). Then, it is easily derived that \(h\) is a martingale on \([0,T)\) with \(h(0,x_{0})=1\), and [PERSON]'s \(h\)-transform implies that the SDE of the conditioned diffusion \(x|x_{T}=v\) is given by  
\[dy_{t}=\tilde{b}(t,y_{t})dt+\sigma(t,y_{t})dw_{t}\]  
where \(\tilde{b}(t,y)=b(t,y)+(\sigma\sigma^{T})(t,y)\
abla_{y}\log p_{T-t}(y,v)\). In the case that the transition density is intractable, simulation from the exact distribution is infeasible. [PERSON] and [PERSON] [5] suggested substituting the latter term in \(\tilde{b}\) with a drift term of the form \(-(y_{t}-v)/(T-t)\), which equals the drift term in a Brownian bridge. The guided process obtained by making the above substitution yields a conditioning, and one obtains  
\[\mathbb{E}[f(x)|x_{T}=v]=C\mathbb{E}[f(y)\varphi_{T}(y)], \tag{1}\]  
where \(\varphi_{T}\) is a likelihood function that is tractable and numerically computable, \(y\) is the guided process, and the constant \(C>0\) depends on \(x_{0}\), \(v\), and \(T\).
# Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
## 3 Results
### Yield Decrease Prediction Using the Varieties Tolerance to Soil Drought and the CWB  
Based on the differentiation of varieties into groups with different tolerances to soil drought and the CWB, it is possible to predict the level of yield decline in years with different moisture contents. The conducted research shows that the average yield decreases due to drought for the group of resistant varieties that ranges from 17 to 29% (average 21.8%), for the group of varieties with an average resistance from 25% to 37% (average 31%), and for the group of sensitive varieties from 39 to 48% (41.6% on average).  
Taking into account the CWB, the average yield decrease for very dry years is from 21% to 37%, for dry years, from 24% to 41%, for normal years, from 28% to 42%, and for wet years, from 18% to 25%.  
When analyzing both parameters at the same time, it can be expected that the yield of the resistant cultivars will decrease on average: in a very dry year, 19.6%, in a dry year, 22.8%, in a normal year, 30.4%, and in a wet year, 17.0%. In the case of cultivars with medium resistance, these will be the following decreases: in a very dry year, 28.3%, in a dry year, 27.9%, in a normal year, 34.7%, and in the wet year, 29.8%. In the case of low-resistance varieties, the decreases will be as follows: in b-year dry 39%, in the dry year, 45.6%, in a normal year, 42.6%, and in the wet year, 38.3%.
# Satellite-Aided Consensus Protocol for Scalable Blockchains
## 5 Throughput Analysis
### Network Capacity Limits
Essentially, high-quality multicast transmissions change the information flow of the satellite-aided blockchain, thereby significantly improving the throughput. As indicated by the above discussions, if the multicast channel is in poor quality, the satellite-aided consensus protocol cannot benefit from the nature of multicast transmissions. Furthermore, since the threshold \(y\left(\beta\right)\) grows with \(\beta\), better multicast channels with a larger \(p\) are thus required if the adversary is more powerful._
## 4 Experimental Results and Analysis
### Tests in Different Parameters
We can see that the offsets between the detected ports and the ground truth in Figure 10a3 and Figure 10b3 are small. The IoU indexes are listed in the fourth row of Table 3. The highest value is 75.94% when FAR is set to 0.02, but the value is 61.91% when FAR equals 0.09. The value of 75.38%, when FAR equals 0.05, is almost the highest value. Figure 10: Comparison of the proposed method under different PRDV thresholds in the Zhanjiang data: (**a1–a3**) FAR is set to 0.02; (**b1–b3**) FAR is set to 0.05; (**c1–c3**) FAR is set to 0.09; (**a1–c1**) the water and land segmentation results; (**a2–c2**) the ROI extraction results; (**a3–c3**) the ROI recognition results.
## 6 Energy-Efficient, Priority-Based Forwarding (EPF)
### System Model  
#### 6.4.1 Controller Announcement  
When the network starts, the controller announces its presence in the network by broadcasting the C_ID pkt. All the nodes update their LITs after receiving the packet, as shown in Figure 10.  
Figure 8: Controller side architecture in EPF.  
Figure 9: EPF packet formats.
## 3 Results
### Experiments for the SOHO Configuration
We can observe that under all security budgets, the proposed approach achieves the highest percentage of mitigated impact, with a margin ranging from 1.42% to 6.78% against the runner-up (which is the \(IMM_{\text{FFT}}\) algorithm in all cases) when comparing absolute magnitudes (i.e., \(impactMitigated(proposed)-impactMitigated(baseline)\)); when considering relative improvements (i.e., \(\frac{impactMitigated(proposed)-impactMitigated(baseline)}{impactMitigated(baseline)}\)), the effectiveness margin of the proposed algorithm ranges from 10.63% to 12.59%. The margin between the proposed approach and other algorithms is larger, ranging from 6.15% to 12.73% in absolute magnitudes, while the relative improvement ranges from 14.06% to 88.45%. This performance margin is again attributed to the capability of the proposed algorithm to direct the security budget to issues whose fixing will lead to the largest reductions in the residual risk. The \"largest fix times first\" approach again produces the worst results, because each fix applied consumes a large amount of security budget, leading to its depletion, without necessarily achieving a, respectively, high reduction of the residual risk. Detailed information on the security issues identified, the integer programming problem formulations used for the prioritization of security issue mitigations and the solutions of these problems is available at [[https://github.com/costasvassilakis/vulnerability-mana-gement-framework](https://github.com/costasvassilakis/vulnerability-mana-gement-framework)]([https://github.com/costasvassilakis/vulnerability-mana-gement-framework](https://github.com/costasvassilakis/vulnerability-mana-gement-framework)), accessed on 14 May 2023. Figure 4: SOHO topology used in the evaluation (adapted from [48]).
## 5 Results and Discussion
### Drag Force Sensitivity to Sampling Frequency and Period
In general, achieving stability or convergence in fluctuation measurements is challenging due to the chaotic and random nature of turbulent flow, which would require the ensemble averaging of multiple datasets. However, the mean value of friction drag converges more quickly than that of pressure drag. After approximately 1 s (\(T^{*}\approx 140\)), the friction drag signal shows some stability, with fluctuations reduced to the fifth decimal place. Both the RMS of friction and pressure drag display a switching behavior, where the RMS values initially decrease with \(T^{*}\), then abruptly increase, and subsequently decrease again, eventually becoming asymptotic. These fluctuations, highlighted in red boxes in the figures, are likely related to bimodality, although a more detailed explanation requires further investigation.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 2 Related Works
By doing so, they provide a granular view of traffic dynamics, offering insight into the temporal progression and spatial distribution of traffic congestion, vehicle speeds, and density. However, the application of these models is not without challenges. One critical issue is the computational and storage inefficiencies that arise from the redundant calculations within tensor windows, which often result in increased processing time and memory requirements [29]. This redundancy is particularly problematic when dealing with large-scale traffic datasets, where the efficiency of computation can significantly impact the timeliness and usability of the analysis. To address these inefficiencies, there has been a push toward optimizing tensor calculations, such as employing more sophisticated tensor decomposition techniques that reduce redundancy and accelerate computation. These techniques include the use of block term decomposition, which partition the tensor into smaller, more manageable blocks, and the implementation of parallel computing strategies that distribute the workload across multiple processors [30]. Furthermore, advancements in hardware, such as the use of GPUs for tensor operations, have also contributed to alleviating the computational burden [30]. In summary, while existing tensor models have laid a solid foundation for traffic data analysis, there is a continual need for innovation to overcome the computational challenges associated with these complex models. Our research contributes to this field by proposing a novel tensor model that not only captures the high-order correlations and spatiotemporal dynamics of traffic flow but also improves computational efficiency through optimized decomposition techniques and algorithmic enhancements. To combat the aforementioned deficiencies, particularly with large volumes of ETC gantry data, our study introduces an innovative method for missing data completion that leverages an enhanced dynamic tensor decomposition technique. This method not only acknowledges the high-dimensional nature of ETC gantry data but also pioneers the use of a high-order tensor model, refined through an improved tensor Tucker decomposition process. The main contributions of our study are manifold: (1) A dynamic tensor flow model was proposed for decomposing the high-order tensor model of ETC gantry data after analyzing the characteristics of ETC gantry data. (2) The issue of high sparsity in ETC gantry data was addressed through the introduction of an adaptive tensor window to analyze incremental tensor blocks in dynamic tensor flow. Furthermore, an improvement was made to the incremental processing method of the dynamic tensor flow by incorporating a Laplacian matrix. (3) An approximate computation approach was adopted to handle the decomposition of neighboring tensor blocks within the same group, leveraging the characteristics of the gantry data, leading to the computational speed enhancement of the high-order tensor Tucker decomposition.
Then, the executive, in conjunction with the preceptor, attempts to bring the BER below the threshold and the CDS will switch back to the BER improvement mode. These will be discussed in more detail in Sections 3-5. The proposed CDS can improve the BER and/or enhance the data rate based on the intelligent processing of the received data, which includes the extraction of a statistical model of the fiber optic channel or the use of the previously extracted models in the model library. Then, the perceptor sends the BER to the executive through the main feedback channel to caculate the internal reward. The executive produces actions on the fiber optic system/signal to ensure that the BER is continuously under the FEC threshold. Thus, the proposed CDS is an intelligent and adaptive system that can tackle a disturbance in the fiber optic link (especially in the optical network) using prediction in the perceptor and/or taking proper actions in the executive based on BER and the internal reward. The proposed technique based on the CDS concept has low computational cost, is a software-defined technique, and provides a significant improvement in the BER and/or the data rate. For example, in the example calculationsdone in Section 5, we found that the Q-factor improves by 2.74 dB and the data rate is enhanced by 12.5% using the proposed CDS system as compared to the conventional system. Additionally, unlike DBP, the proposed CDS does not require the fiber optic system parameters, such as the number of fiber spans, span length, dispersion, and nonlinear coefficients of each fiber span. Intelligent perception processing by CDS can extract a statistical model of a fiber optic channel. Moreover, the CDS can learn and recognize a disturbance in the optical network, such as a variation in data rate, fiber length, or power fluctuation. Then, the CDS using proper actions, such as data-rate tuning or probabilistic shaping of constellations, can automatically tackle network fluctuations. The paper is organized as follows. In Section 2, the basics of CDS is reviewed. In Section 3, the concept of CDS is applied to a fiber optic system, which is nonlinear and the noise is non-Gaussian. The design of perceptor and executive for fiber optic application is discussed in detail. In Section 4, the detailed algorithm for the proposed CDS is discussed. In Section 5, the proposed CDS is implemented for a long-haul fiber optic system based on OFDM. Numerical simulations of the fiber optic system with and without CDS are carried out and the improvements resulting from the use of CDS are discussed. Additionally, the computational complexity associated with CDS is compared with other digital techniques used for fiber optic communication systems. Finally, the conclusions are presented in Section 6.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 7 Conclusions  
While most theory and research has focused on indoor test-cases and reception limitations, there are limited larger WAN capability surveys. As part of this paper our results have far exceeded the theoretical communications capabilities in a WAN setting, boasting above a 70% throughput rate still at radial distances of 20-30 km. Although the mean SNR at these distances was found to be \(-7.54\) dB, the LoRa modulation techniques have proven the ability to detect severely attenuated LoRa packets up to 20 dB below the noise floor  
From the literature reviewed above, and works laid out within this paper, a strategy has been formed for future works to implement a large, scalable wireless sensor LTN utilising commercially available radar devices for measurement. Utilising fixed radar level sensors in SPS, and continued improvement in SCADA data collection and prediction has allowed for the plotting of standard, expected diurnal inflows and outflows at critical wastewater sites. This gives the ability to alarm in real-time lower than (or greater than) expected flows at specific time-slices of a day, identifying whether there is a potential problem either upstream or downstream from the corresponding SPS site. The proposed IoT radar sensor network will greatly supplement the current capabilities of this diurnal management tool; providing significantly more WCN data sets to further improve proactive location identification of potential system problems.  
The major aim of this is to provide a solution to eliminate dry-weather sewer overflows completely from not only Shoalhaven Water's network, but to assist in providing a future framework for all water utilities to implement.
# Blockchain Technology and Artificial Intelligence Together: A Critical Review on Applications
## 7 AI Applications Powered by a Blockchain
### Supply Chain  
Due to its decentralization, high dependability, and immutability, the blockchain has become a significant technological way to overcome the development restrictions of conventional supply chains [54]. Information asymmetry between downstream and upstream firms in the supply chain may be successfully resolved via the use of the blockchain network to publish the data held in the database, therefore facilitating the accurate and speedy exchange and cooperation of logistical data [55].  
Applying AI to the blockchain system has the potential to completely automate the supply chain, hence redefining its very nature. Integrating with the blockchain allows the AI platform to learn from historical purchase data, point-of-sale sales data, etc., allowing for the identification of data characteristics and the implementation of predictive analysis such as demand forecasting, sales model forecasting, route planning, and network management (Figure 9) [56]. The solution [PERSON] et al. [57] suggested in their study for the secure distribution of pharmaceuticals throughout the whole supply chain leverages a blockchain and AI. Using an event request-response method, each product within the chain may be moved between chain members who have been verified. Smart contracts are used to record all transactions between entities onto the blockchain, allowing a product to be tracked back to its original source. The results of the experiments demonstrate that our technique is workable and somewhat more secure than current solutions.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Number** & **Objective** & **Reference** \\ \hline
1 & Blockchain-based agriculture 4.0 strategy & [51] \\ \hline
2 & Investigating supply chain management using blockchains, AI, and the IoT & [52] \\
3 & Using blockchains to focus on Food Industry 4.0 & [53] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Agriculture using blockchains and AI.  
Figure 8: Blockchain applications in smart agriculture.
# Effect of Surface Roughness on Early Stage Oxidation Behavior of Ni-Base Superalloy IN 625
## 3 Results
### Post-Exposure Analyses  
#### 3.2.1 GD-OES Depth Profiles  
Polished and ground IN 625 were air exposed at 950 \({}^{\circ}\)C for 24 h and analyzed using GD-OES. Figure 3 shows GD-OES profiles obtained on polished and ground IN 625. Profiles show the elemental concentration at given sputtering time, which in turn indirectly represents the distance from the surface (starting from 0 s which represents oxide scale surface). In general, one can observe that the sputtering time of the oxide scale formed on polished sample is higher that on ground (110 s and 70 s respectively). On both depth profiles a co-enrichment of Cr and O is observed during the first 100 s of measurement, which indicates that the majority of the oxide scales formed on both surfaces is Cr\({}_{2}\)O\({}_{3}\). Moreover, on both profiles at the oxide scale/alloy interface (place where oxygen concentration drops) one can observe the enrichment of Nb. One can also observe a presence of the Ti in the outer oxide scale and below the oxide scale, Ti-depletion zone is observed. However, a difference in the depth profiles also exists between polished and ground material, namely, for the polished sample in the very outer part of the oxide scale (0-20 s), a strong enrichment of Mn (up to 10 at. %) and minor enrichment of Ni (up to 5 at. %) and Fe (up to 0.5 at. %) is observed. Conversely, on ground material, at the very outer part of the oxide scale, weaker enrichment of Mn is present (up to 5 at. %) and Ni is enriched up to about 0.9 at. %, while no enrichment of Fe is observed. Moreover, at the surface of the oxide scale together with Mn, enrichment of Ti is clearly visible (up to 1 at. %). In summary, in the case of a polished sample, a higher concentration of Mn and Ni is observed, while in the on ground sample, in the very outer part of the scale, a small peak from Ti is observed (up to 1 at. %). Moreover, plots show higher time of sputtering of the oxide scale on polished sample, which indicates that the oxide scale formed on polished surface is thicker than on the ground one.