esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 4 Joining Procedure and Time Synchronization Problems  
As already mentioned, the TSCH medium access mechanism improves the reliability of communications considerably, since it is specifically designed to work in industrial environments where this type of interference hinders access to the radio environment. However, this is only valid for steady-state operation, since its behaviour during the initial moments of the network is more complicated when compared with other contention-based asynchronous mechanisms that use only one transmission channel.  
Moreover, the RPL protocol has proven to be a fundamental tool for establishing communications and links in mesh networks with low capacities. However, if we analyse the behaviour of these two protocols (IEEE802.15.4e-TSCH and RPL) in early phases where synchronization has not yet been performed, it is difficult to form the network rapidly [14; 16; 17; 24; 28]. The missing factor in the operation of RPL and TSCH is that the beacon transmission period, which is necessary to carry out the synchronization, has been modified with the RPL Trickle Timer, which does not take into account the needs of the synchronization layer. For a node to be able to restart the transmission period of beacons and DIO messages, it is necessary for the node to receive a DIS message. However, DIS messages cannot be sent by those nodes that are not yet synchronized. For instance, in situations where the network is already stable, the new nodes that wish to join the network request information through DIS (DODAG Information Solicitation) messages, but when using TSCH, the new nodes are not yet synchronized and the transmission of DIS messages will not be possible since these messages are discarded. This is a key concept, as synchronizing successfully does not mean that the node is able to transmit data, as it does not know any routes.  
Therefore, this initial deployment phase can be subdivided into two stages. In the first one, the nodes that wish to join the TSCH network begin to scan in all of the channels in search of the synchronization EBs that already-connected nodes are transmitting. Then, in the second stage, nodes have already synchronized (they know the ASN value of their temporal reference and the channels they must use in each slot), but still need to join the RPL network. These nodes start sending DIS messages to request routing information, while already-connected nodes reply or send periodic DIO messages with the required information. The following content of this section explains in detail these two processes or stages.
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 3 Results and Discussion
### Selection of the Optimal Algorithm
These metrics were used to evaluate how well the model classified the two classes: roof and background. As shown in Table 3, when the performance metrics were calculated, the Global Accuracy was approximately 82.3%, indicating that approximately 82.3% of the pixels were correctly classified. The _Mean Accuracy_  
\[Global\ Accuracy=\frac{\sum TP}{\sum(TP+FP+FN)}\]  
was approximately 80.7%, representing the average accuracy  
\[Mean\ Accuracy=\frac{1}{2}\sum\frac{TP}{TP+FP}\]  
across classes, which was approximately 80.7%. Mean Intersection over Union (_IoU_)  
\[Mean\ IoU=\frac{1}{2}\sum\frac{TP}{TP+FP+FN}\]  
was approximately 68.0%, demonstrating how well the model separated the classes. The _Weighted IoU_  
\[Weighted\ IoU=\frac{1}{2}\sum\frac{(TP+FN)\frac{TP}{TP+FP+FN}}{TP+FP} \tag{1}\]  
was approximately 70.2%, calculated by assigning weights to each class based on their size and then calculating the _IoU_. Table 3 presents the confusion matrix for ResNet50. Figure 7 displays the misclassified regions, highlighting the areas where incorrect predictions occurred. Specifically, Figure 7a represents false negatives, and Figure 7b illustrates false positives, both drawn to identify their distinct characteristics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Global Accuracy** & **Mean Accuracy** & **Mean IoU** & **Weighted IoU** \\ \hline UNet & 0.77065 & 0.75067 & 0.60549 & 0.63206 \\ SegNet & 0.75993 & 0.70205 & 0.56705 & 0.60776 \\ ResNet18 & 0.78701 & 0.75992 & 0.62369 & 0.65181 \\ ResNet50 & 0.82275 & 0.80698 & 0.67971 & 0.70237 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance results of the test network. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Expected**} \\ \cline{2-3}
**Confusion Matrix** & **Roof** & **Background** \\ \hline \multirow{2}{*}{Labeled} & Roof & 0.8615 & 0.1385 \\  & Background & 0.24754 & 0.75246 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of ResNet50. Upon reviewing the results of the initial test in the first test file, ResNet detected roofs more accurately, as shown in Figure 8. The results indicated that SegNet did not consider morphological aspects, whereas ResNet did consider the morphological model. Due to the high rainfall in the area, green waterproofing materials are commonly used, resulting in predominantly green roofs. While ResNet correctly identified green roofs, there was no significant difference compared to ResNet18. However, SegNet was unsuitable for this study because it did not consider morphological aspects.
## Materials and Methods
### Analysis of Used Engine Oil Degradation  
The _Trichoderma_ strains were tested for their ability to degrade used engine oil by analyzing its composition before and after the fungal action. A 5 mm disk of each _Trichoderma_ strain was collected from the outer part of the fungal colony grown for seven days on PDA Petri dishes, and each was aseptically inoculated into a 20 mL sterile glass bottle with a crimp seal filled with 5 mL of Bushnell Haas mineral medium (BH) and 1% (_v/v_) of used engine oil. The degradation study bottles were incubated at room temperature and 80 revolutions per minute (rpm) for 45 days. The amounts of hydrocarbon were determined by extracting the residual oil at the inoculum moment (T0) and after 45 days (T45), with four replicates for fungal strain each time. T0 was considered as control. At the moment of analysis, dietlyl ether was added to the degradation bottles in a ratio of 1:1. The samples were shaken vigorously for 1 min, and the organic layer was allowed to separate for 5 min. At this point, 1 mL of the supernatant was collected from the degradation bottles, placed in a 1.5 mL Eppendorf tube, and centrifuged for 3 min at 3000 rpm. The supernatant was then transferred to a 1 mL GC/MS glass vial. The extracted oil was evaluated by GC/MS carried out on a Thermo Scientific DSQII single-quadrupole GC/MS system. The injection in the GC/MS system was performed at 250 \({}^{\circ}\)C in split mode. The oven temperature program was 70 \({}^{\circ}\)C for 1 min, 70-120 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, 120-260 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, and hold at 260 \({}^{\circ}\)C for 5 min. A Restek Rai-5 Sil MS 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness capillary column was used with helium as the carrier gas at a constant flow rate of 1.0 mL/min. The transfer line temperature was 270 \({}^{\circ}\)C and the ion source temperature was 250 \({}^{\circ}\)C. Electron ionization mode was used with 70 eV, and the ions were registered in full scan mode in a mass range of _m_/z 35-800 amu. The chromatogram acquisition, detection of mass spectral peaks, and waveform processing were performed using Xcalibur MS Software Version 2.1 (Thermo Scientific Inc., Waltham, MA, USA). The assignment of chemical structures to chromatographic peaks was done based on comparison with the databases for the GC/MS National Institute of Standard and Technology (NIST) Mass Spectral Library (NIST 08) and Wiley Registry of Mass Spectral Data (8 th Edition). The percentage content of each component was directly computed from the peak areas in the GC/MS chromatogram.
## 3 Optimal Design of Transmit Waveform and UAV Altitude
### Method for Obtaining the Optimal Solution Based on the PSO Algorithm
12:if\(p_{m}^{(j)}<H_{\min}\)then
13:\(p_{m}^{(j)}\gets H_{\min}\)
14:elseif\(p_{m}^{(j)}>H_{\max}\)then
15:\(p_{m}^{(j)}\gets H_{\max}\)
16:endif
17: Obtain \(\mathbf{X}^{\star}(p_{m}^{(j)})\) and \(\mathbf{\gamma}^{\star}(p_{m}^{(j)})\) by solving problem \(P_{2}^{\forall h}\) with a given \(h=p_{m}^{(j)}\)
18: Compute the fitness function value \(f_{\text{Fit}}(p_{m}^{(j)})\) from (28)
19:if\(f_{\text{Fit}}(p_{m}^{(j)})<f_{\text{Fit}}(p_{m}^{best})\)then
20:\(p_{m}^{best}\gets p_{m}^{(j)}\)
21:endif
22:endfor
23: Update \(g^{best}\leftarrow\arg\min_{[p_{m}^{best}]_{m=1}^{M}}f_{\text{Fit}}(p_{m}^{ best})\)
24:endfor
25: Obtain an optimal altitude \(h^{\star}=g^{best}\)
26: Obtain the optimal waveform \(\mathbf{X}^{\star}(g^{best})\) and scaling factor \(\mathbf{\gamma}^{\star}(g^{best})\) by solving problem \(P_{2}^{\forall h}\) with \(h=g^{best}\)
27:Output: Optimal solution to problem \(P_{1}\), i.e., \(\mathbf{X}^{\star}\), \(\mathbf{\gamma}^{\star}\), and \(h^{\star}\)
```  
**Algorithm 1** Proposed algorithm to obtain an optimal solution to problem \(P_{1}\)
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 2 Related Work
### Verifiable Random Function  
VRFs are cryptographic primitives that generate a random output along with a proof that the output was correctly computed [11]. This enables anyone to verify the correctness of the output without knowing the secret key. VRFs are characterized by uniqueness, pseudorandomness, and verifiability, making them particularly useful in blockchain protocols for ensuring fairness and security. In blockchain, VRFs are employed to enhance decentralization and security by preventing centralization and manipulation through verifiable randomness. For example, Algorand uses VRFs to randomly select block proposers and committee members, ensuring a fair and unbiased selection process.  
However, the advent of quantum computing poses a significant threat to the security of existing VRF implementations that rely on classical cryptographic assumptions. Quantum computers threaten classical VRFs because they can solve problems like discrete logarithms much faster than traditional computers. This could break the cryptographic assumptions VRFs rely on, leaving blockchain systems vulnerable to attacks. To address this, new quantum-resistant VRF constructions are being developed.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 4 Experiments and Performance Analysis
### Limitations  
Although our method effectively controls the network congestion and improves the QoS, it still has several limitations. First, in our designed approach, many parameters are assumed artificially. Instead, at present, deep learning has been widely used in many different fields, and has achieved remarkable results. Thus, we will use deep learning technology to optimize the relevant parameters in the SDN routing strategies in our future works. Secondly, due to the more complex network and more diverse types of data and equipment resources, it brings more serious security threats. How to build a secure and privacy-persevering mechanism for the SDN routing protocol in SGs is the next step to be considered.  
Figure 6: Performance comparison w.r.t. different strategies. The black line is our proposed approach. (**a**) Average delay comparison. (**b**) Packet loss comparison. (**c**) Average jitter comparison.
## 4 Conclusions  
In this study, the nuclear radiation shielding capabilities were thoroughly examined for eight borate glasses of Eu2O3 containing high ratios of PbO/Bi2O3. For this aim, MAC values of the glasses, encoded LPb50 BEu and LBi50 BEu, in the 0.015-15 MeV photon energy range were acquired by MCNPX simulation codes and Phy-X/PSD online software. While the MAC values for LPb50 BEu2 and LBi50 Eu2 glasses produced values of between 79.045 cm\({}^{2}\)/g-0.045 cm\({}^{2}\)/g and 89.442 cm\({}^{2}\)/g-0.048 cm\({}^{2}\)/g, the MAC values of LPb50B and LBi50B glasses change between 78.429 cm\({}^{2}\)/g-0.045 cm\({}^{2}\)/g and 89.281 cm\({}^{2}\)/g-0.048 cm\({}^{2}\)/g. Based on the MAC values, other vital photon-shielding parameters, T\({}_{1/2}\), \(\lambda\), \(Z_{\textit{eff}}\), EABF, and EBF, were also found for LPb50 BEu and LBi50 BEu glasses. When all parameters were evaluated, it was revealed that the LBi50 BEu glass system was more effective than LPb50 BEu in shielding gamma radiation. It was seen that the \(\Sigma_{R}\) values of both glass systems were greater than conventional neutron moderators. LBi50 BEu2 glass was also found to be superior to LPb50 BEu2 glass in stopping charged particle radiation, as in gamma radiation.  
Conceptualisation, G.A., H.O.T., H.M.H.Z., and S.A.M.I.; methodology, G.B. and H.O.T.; software, H.O.T., H.M.H.Z., and A.E.; validation, S.A.M.I., E.K., and A.E.; formal analysis, H.M.H.Z. and G.B.; investigation, H.O.T.; resources, G.B. and E.K.; data curation, S.A.M.I. and A.E.; writing--original draft preparation, H.O.T., G.B., and E.K.; writing--review and editing, H.M.H.Z., S.A.M.I., and A.E.; visualization, E.K.; supervision, H.M.H.Z. and E.K.; project administration, H.O.T. and S.A.M.I; funding acquisition A.E. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at Princess Abdulafrahman University through the Fast-track Research Funding Program. The APC was covered by \"Dunarea de Jose\" University of Galati, Romania.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program. The authors declare no conflict of interest.
## 3 Simulation of Typhoon Lekima  
### Overview of Lekima and WRF Model Settings  
Super Typhoon Lekima in 2019, designated as No. 1909, was a powerful and destructive tropical cyclone that struck East Asia in August 2019. It was the ninth named storm and the fourth typhoon of the 2019 Pacific typhoon season. Lekima originated from a tropical depression that formed east of the Northern Mariana Islands on 30 July 2019. The storm gradually intensified as it moved west-northwestward, reaching typhoon status on 6 August. It continued to strengthen rapidly and became a super typhoon on 7 August with maximum sustained winds of 62 m/s. Lekima then made landfall in Wenling, Zhejiang Province, China, on August 10. The impact of Typhoon Lekima was severe, causing widespread destruction and loss of life. It triggered torrential rainfall and strong winds, resulting in widespread flooding, landslides, and mudslides. The storm caused significant damage to infrastructure, including buildings, bridges, and roads. It also disrupted transportation and led to power outages in several areas. According to the news report, Lekima has affected over 4 million people in Zhejiang Province. Crop damage has occurred on 103,000 hectares of land, with 13,000 hectares suffering complete crop loss. Additionally, over 14,000 houses have been damaged or collapsed due to the disaster, resulting in a direct economic loss of CNY 7.44 billion.  
As demonstrated in Figure 4, two-way nested static downscaling domains are used in WRF simulation and the simulation time period is from 0000 UTC 07 Aug to 0000 UTC 13 Aug in 2019. For the initial and boundary conditions of the outermost domain, 6-hourly Final Operational Global Analysis data of \(1^{\circ}\) resolution from the National Centers for Environmental Prediction (NCEP) are used, while the boundary conditions of the inner domains are provided by the domain above. The spatial and temporal resolutions of each domain are listed in Table 1. The schemes and parameterizations of physical processes of the simulation are listed in Table 2.  
Figure 3: Cross section of conductor model with upper rivulet.
## 1 Introduction  
The class III growth pattern is characterized by disproportionate forward mandibular growth and/or deficient maxillary growth [1]. The treatment of class III patients can start as soon as the deciduous dentition appears. In growing patients, it is attempted to treat class III malocclusions using orthopedic techniques such as chin cups or protraction facemasks. Little long-term success has been documented with the use of these devices [2]. However, once the patient has stopped growing, the treatment options for skeletal class III range from orthodontic camouflage in non-severe cases [3] to orthognathic surgery in combination with orthodontic treatment in more severe cases [4, 5]. Nevertheless, non-surgical treatment generally falls short of expectations and rarely produces acceptable facial esthetics. Accordingly, orthognathic surgery is considered to be the first treatment choice in the majority of cases.  
Over the last few years, the authors have been trying to establish a reference that, moving beyond classical cephalometric analyses, and independently of the occlusal situation of the patient, could serve as an esthetic guide for the sagittal repositioning of the maxillomandibular complex [6]. Previous surgical strategies have been aimed at achieving class I occlusion by combining different levels of maxillary advancement and mandibular retrusion. However, the authors believed that most treated class III patients had deficient jawline and neck definition, and they also found that a significant proportion of them presented with posterior airway involvement, resulting in variable degrees of obstructive sleep apnea hypopnea syndrome (OSAHS) [7,8].  
In 2010, after studying a series of visually attractive individuals, the senior author proposed a new sagittal anterior repositioning reference. The latter related a plane tangential to the soft tissue nasion and perpendicular to the floor with the head in the natural head position (NHP) to the upper incisor. In 101 out of 112 cases (92%), the upper incisor was at this vertical plane or in front of it [6]. In 2022, the upper incisor to soft tissue plane (UI-STP), or the so-called \"Barcelona Line\" (BL), was first validated as a guide for the sagittal repositioning of the maxillomandibular complex [7,9], where, first, the mandible is brought to occlusion, and then the maxillomandibular complex is moved to the BL.  
After more than a decade using this vertical reference for both facial diagnostic analysis and surgical planning, the authors realized that most class III patients required large maxillary advancement and even the mandible was usually moved forward. Therefore, the previously mentioned deficient jawline and neck definition that characterizes the majority of these class III patients was improved. Thus, the authors aimed to demonstrate the fate of the mandible in the sagittal plane in a series of class III patients subjected to orthognathic surgery using the previously described UI-STP [6,7].
## 2 Methodology
### Optimal Estimation Theory  
Following the definition provided by [PERSON] (1965), \(\textbf{S}_{\alpha}\) is the error covariance matrix for the _a priori_ state vector, \(\textbf{x}_{\alpha}\), \(\textbf{S}_{\varepsilon}\) is the measurement error covariance matrix, and **K** is the weighting function matrix or Jacobian matrix, comprised of the partial derivative of each forward model output with respect to each retrieved parameter, i.e., \(K_{i,j}=\partial F_{i}/\partial x_{j}\) (where \(K_{i,j}\) represents the element in the \(i\)th row and the \(j\)th column in matrix **K**, \(F_{i}\) is the \(i\)th simulated measurement, and \(x_{j}\) is the \(j\)th element of the vector \(\boldsymbol{x}\)). The Jacobians identify the sensitivity of observations to the retrieved parameters. The sensitivity of the retrieved state to the true state may be described by the averaging kernel matrix **A**, which is provided by the gain function \(\textbf{G}_{y}\):  
\[\text{A}=\frac{\partial\hat{\textbf{x}}}{\partial\textbf{x}}=\text{G}_{y} \text{K}. \tag{2}\]  
Given \(\textbf{S}_{\alpha}\), \(\textbf{S}_{\varepsilon}\) and **K**, the gain function \(\textbf{G}_{y}\) is specified as follows:  
\[\textbf{G}_{y}=\left(\textbf{K}^{\text{T}}\textbf{S}_{\varepsilon}^{-1} \textbf{K}+\textbf{S}_{\alpha}^{-1}\right)^{-1}\textbf{K}^{\text{T}}\textbf{S }_{\varepsilon}^{-1}. \tag{3}\]  
The trace of the matrix **A** represents the DFS (i.e., the number of independent pieces of information yielded by the measurements). The closer **A** is to the identity matrix, the closer to perfect a retrieval will be, which means that the retrieved state changes completely with the true state without any interference. The averaging kernel matrix or DFS has been used to study the information content of the retrieved elements for trace gases or aerosol remote sensing [PERSON] (1965); [PERSON] (1965); [PERSON] (1965); [PERSON] (1965); [PERSON] (1965); [PERSON] (1965). After assessing the averaging kernel matrix **A** based on the _a priori_ and measure error assumptions, the elements in **x** with greater information, which could be better retrieved from satellite observations, are decided without the retrieval process. In this study, the traces of the elements in matrix **A** corresponding to the CO\({}_{2}\) profile and aerosol parameters are called CO\({}_{2}\) DFS and aerosol DFS, respectively. In addition to the information of each retrieved element, all the a posteriori error components from different sources could be estimated by this method, as described in the following subsection.