esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 3 Background
### Rpl  
The DODAG is constructed with the help of control messages. The Internet Control Message Protocol for IPv6 (ICMPv6) information messages that play a role in building the DODAG are: DODAG Information Solicitation (DIS) messages, DODAG Information Object (DIO) messages, destination advertisement object (DAO) messages, and destination advertisement object acknowledgment (DAO-ACK) messages. Figure 2 presents the message exchange during the construction of the DODAG. On the arrows between the nodes, the corresponding message with the number of the step is represented. The root initializes the construction of the DODAG by sending a DIO message to its neighbors via LL broadcast (step 1 in Figure 2). This message contains information about the state and parameters of the network. Upon receiving this message, the nodes may respond with a DAO message (step 2 in Figure 2) if they want to join the DODAG. The DAO-ACK may be sent by a root or parent to the child as a response to the DAO message. When a new node wants to join an already existing DODAG or a DODAG under construction, it sends a DIS message (step 1\({}^{\prime}\) in Figure 2), soliciting information about the DODAG. In response, the node receives a DIO (step 2\({}^{\prime}\) in Figure 2) message. Based on the information received in the DIO message, the path cost, and other local policies, it decides whether to join or not the graph, and if it chooses to, it sends a DAO message (step 3\({}^{\prime}\) in Figure 2). This DAO message is propagated towards the root node.
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 2 Literature Review
### Construction Risk Assessment in Onshore Wind Project and Its Challenges  
Installing a wind turbine onto its foundation and completing final assembly appears on the surface to be straightforward. However, constructing a wind farm involves a long list of civil engineering and electrical work that require high levels of project management and coordination [42]. Once permits, approvals, and project finance are secured, the rigorous management of a complex series of engineering, logistical, and electrical processes must occur to reduce uncertainties and risks [42]. While many studies have been conducted to enhance risk management in wind projects, most of these studies have focused on the exploration and identification of risk factors affecting onshore wind projects. For example, while several studies have investigated which risk factors affected the planning, construction, and operation phases of wind projects [43; 44], few studies have focused on methods for assessing the risk factors. [PERSON] [45] developed a methodology for assessing the overall risk severity in wind projects based on a linguistic subjective scale. [PERSON] [46] proposed a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis approach to assess the risk level in wind energy projects. [PERSON] and colleagues [47] proposed a simulation-based approach to assess the severity of risk factors on the cost and time of onshore wind projects. Because of the lack of historical data, triangular and uniform distributions were used to depict the cost and schedule impact of the identified risk factors [47]. Notably, none of the aforementioned studies were capable of (1) incorporating large amounts of expert knowledge into simulation-appropriate input distributions and (2) considering the dependency between cost and schedule.
## 3 Analysis of Experimental Results
The results show that this method can effectively extract the characteristic information of hyperspectral data and realize the accurate detection of potato leaf diseases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Accuracy and Recall**} & \multicolumn{3}{c}{**Disease Category**} \\ \cline{3-5}  & & **Anthrax** & **Blight** & **Early Blight** \\ \hline \multirow{2}{*}{Train} & Accuracy & \(1\) & 0.9969 & 0.9979 \\  & Recall & \(1\) & 0.9992 & 0.9923 \\ \multirow{2}{*}{Test} & Accuracy & 0.9987 & 0.9895 & 0.9842 \\  & Recall & 0.9997 & 0.9942 & 0.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy and recall of prediction results for different disease categories. Figure 11: Confusion matrix of the three disease identification results using 1D-CNN. In the two classification processes mentioned above, we classified four categories of healthy leaf pixels, diseased leaf pixels, background pixels, and whiteboard pixels, and the other for three diseases, respectively. The latter of these classifications is based on the classification task for the former. We labeled diseased leaf pixels from the first classification result as a new object of study. We performed a secondary classification using 1 DCNN in combination with the hyperspectral number of diseased leaf pixels. Both varieties use the hyperspectral data of the leaves. However, because the four categories of objects on the leaves--healthy leaf pixels, diseased leaf pixels, background pixels, and whiteboard pixels--have specific regional connectivity and need to consider the influence of the spectral information of the pixels in their neighborhood, the structure of the network is enriched by considering the spatial, spectral data in the first classification. The second classification does not influence surrounding pixels, and the variety is predicted only based on the tremendous spectral details of the diseased pixels of the leaves.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 3 Joint Iterative Decoding Design of a Cooperative SCMA System
We also define \(V_{k\to j}^{t}\) and \(R_{j\to k}^{t}\) as the probability distributions propagating to the \(k\)-th virtual RN from the \(j\)-th VN and the \(j\)-th VN from the \(k\)-th virtual RN at the \(t\)-th iteration, respectively. The updating steps of the standard logarithm domain MPA can be described as follows:  
(1) _Initialization_: Set the maximum iteration number as \(T\) and \(R_{j\to k}^{0}=\log 1/M\), i.e., the _priori_ log-likelihood probability of each transmitted symbol is identical. Calculate the logarithm domain conditional probability as  
\[P_{k}(\mathbf{x})=-\frac{1}{2\sigma^{2}}\bigg{|}y_{k}-h_{k}\sum_{m\in\mathcal{ V}_{k}}x_{k,m}\bigg{|}^{2},k=1,2,\cdots,2K, \tag{15}\]  
where \(\mathbf{x}=\{\mathbf{x}_{m}\},m\in\mathcal{V}_{k}\). (2) _Resource Node Updating_: At the \(t\)-th iteration, the message sent from the \(k\)-th virtual RN to the \(j\)-th VN is given by  
\[V_{k\to j}^{t}(\mathbf{x}_{j})=\underset{\mathbf{x}_{m}:u\in\mathcal{V}_{k} \backslash j}{\max}^{*}\Bigg{\{}P_{k}(\mathbf{x})+\sum_{u\in\mathcal{V}_{k} \backslash j}R_{u\to k}^{t-1}(\mathbf{x}_{u})\Bigg{\}},k=1,2,\cdots,2K, \tag{16}\]  
Figure 3: The virtual factor graph of \(2K=8\) and \(J=6\) for the downlink cooperative SCMA system. where \(\mathcal{V}_{k}\setminus j\) denotes the \(d_{c}-1\) VNs connected to the \(k\)-th virtual RN with the \(j\)-th VN excluded, and the operation \(\max^{*}(. )\) is given by  
\[\max^{*}(a_{1},a_{2},\cdots,a_{n})=\ln(e^{a_{1}}+e^{a_{2}}+\cdots+e^{a_{n}}). \tag{17}\]  
The computational cost of MPA detection mainly lies on the \(\max^{*}(. )\) exponential operation above. To this end, Jacobian approximation is often used to reduce the computation cost of this calculation, giving us  
\[\max^{*}\{a_{1},a_{2},\cdots,a_{n}\}\approx\max\{a_{1},a_{2},\cdots,a_{n}\}. \tag{18}\]  
In this way, the virtual resource node updating in (16) can be simplified into  
\[V_{k\to j}^{l}(\mathbf{x}_{j})\approx\max_{\mathbf{x}_{i}:u\in\mathcal{V}_{k }\setminus j}\left\{P_{k}(\mathbf{x})+\sum_{u\in\mathcal{V}_{k}\setminus j}R_{ u\to k}^{l-1}(\mathbf{x}_{u})\right\},k=1,2,\cdots,2K, \tag{19}\]  
(3) _Variable Node Updating_: At the \(t\)-th iteration, the message sent from the \(j\)-th VN to the \(k\)-th virtual RN can be computed as  
\[R_{j\to k}^{l}(\mathbf{x}_{j})=\sum_{m\in\mathcal{R}_{j}\setminus k}V_{m \to j}^{l-1}(\mathbf{x}_{j}),k=1,2,\cdots,2K, \tag{20}\]  
where \(\mathcal{R}_{j}\setminus k\) represents the \(d_{v}-1\) RNs connected to the \(k\)-th VN with the \(j\)-th virtual RN excluded. (4) _Probability Calculating and Symbol Judging_: When the algorithm is converged or the maximum number of iterations \(T\) is reached, a posteriori probability of virtual codeword \(\mathbf{x}_{j}\) can be calculated as  
\[R_{j}(\mathbf{x}_{j})=\sum_{m\in\mathcal{R}_{j}}V_{m\to j}^{T}(\mathbf{x}_{j}). \tag{21}\]  
Note that the transmit virtual codeword \(\mathbf{\hat{x}}_{j}\) of user \(j\) in the second group is the one maximizing \(R_{j}(\mathbf{x}_{j})\).
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### State Diagrams
#### 3.6.1 Server Node
If this margin did not exist, and new requests would be attended immediately, synchronization problems might happen if other nodes were detecting the new incorporations to the network in an incorrect order. * _Wait IP_return:_ In this state the node is waiting for the reception of an IP_RANGE_RETURN message. When it receives this message, in which a node in the network indicates it has a block that it can offer the client in waiting; it will send an IP_ASSIGNED message to the client. After this, the node will have ended its function as server, and will pass to the IDLE state. If the IP_RANGE_RETURN message is not received before the timer IP_RANGE_REQUEST_TIMER expires, it will turn to try the request sending again an IP_RANGE_REQUEST message a maximum number of times REQUEST_MAX_RETRY. These successive attempts are sent in every occasion to a different node. If the limit of attempts is exceeded, then the node will desist and change its state to IDLE. * _IDLE:_ This is the state of rest, or the one in which the node is idle. When it is in this state, the node does not undergo any operation related to the auto-configuration process. It is therefore treated as a waiting state. * _Node down time window:_ This node provides a time margin when the node must gather the IP address from a node that has left the network. More precisely, the node changes to this state on having detected that it has lost the route towards a node of whose address block it is responsible. This transition is done from any other state, be it of type ready or not_ready. After the time determined by the timer NODE_DOWN_ASSIGN_TIMER, the node will return to the state of rest IDLE. This timer is not to be confused with the NODE_DOWN_TIMER. Although they begin at the same time on having detected the same event, the processes involved are independent.
# Design and Experiment of Magnetic Antenna Vibration and Noise Reduction System
## 4 Test Verification and Analysis
### Function Comparison Test
The peak value of the vibration transmission curve is about 5 Hz, which is the natural frequency of the vibration reduction system. The vibration at this frequency point is amplified by nearly 20 dB, so the vibration reduction system must avoid this frequency band when working. The magnetic antenna is a newly developed underwater low-frequency communication antenna, and it is easily affected by motion-induced noise. Therefore, no additional electromagnetic interference can be added to the design of vibration isolation measures. In the early stage, we only used an ordinary rubber vibration isolation pad to reduce vibration;  
Figure 11: Site layout of the functional comparison test. Figure 12: Shell-antenna vibration transmission rate. with limited vibration isolation effect, the motion induced noise of the antenna is about 400 fT. Now, by adopting the new vibration isolation system introduced in this paper, the vibration in the communication frequency band is attenuated by more than 40 dB, and the antenna motion-induced noise is reduced to less than 60 fT, which significantly improved the communication performance.
# Aerodynamic Analysis of the Opening Hood Structures at Exits of High-Speed Railway Tunnels
###### Abstract  
As train operating speeds increase, the aerodynamic characteristics of the train within the tunnel become more pronounced, and effectively addressing the issue of micro-pressure wave (MPW) over-limits becomes especially crucial. This paper utilized the control volume method to investigate the key influencing parameters of tunnel exit hoods on the mitigation effectiveness of MPWs. Additionally, numerical simulation methods were used to validate these crucial parameters. The analysis considered various opening ratios, different opening forms, and the influence of hoods at tunnel entrances and exits on the amplitude and spatial distribution patterns of MPWs. A design methodology that comprehensively takes into account the advantages of tunnel entrance and exit hoods was proposed. The results showed that a higher opening ratio of tunnel exit hoods led to lower MPW amplitudes. Compared to without opening in the hood, when the opening ratio of the exit hood reached 90%, the maximum amplitude of MPWs at a distance of 20 m from the hood outlet decreased by 48.7%. Various opening forms of exit hoods resulted in distinct spatial distribution patterns of MPW amplitudes, with amplitudes near the openings notably higher than in other areas. There were differences in the mitigation mechanisms between entrance and exit hoods. In comparison to entrance hoods, exit hoods exhibited higher mitigation efficiency within a specific range of MPW amplitudes. Additionally, when both entrance and exit hoods were installed, they achieved the most effective mitigation of MPWs.  
high-speed railway tunnel; exit hood structure; micro-pressure waves; effective radiation solid angle; hood design optimization; controlled volume method +
Footnote †: journal: Applied
## 1 Introduction  
About 25 years after the first report of electroluminescence from a single crystal of anthracene under a high applied voltage [1], [PERSON] proposed an organic light emitting diode (OLED) composed of a double-layer structure of organic thin films [2]. Since then, various OLEDs with different structures have been developed. The device structures are designed based on the work functions of the electrodes and molecular orbital energy levels of the organic materials, which are key factors of the OLEDs' performance. Meanwhile, using ultraviolet photoemission spectroscopy and the Kelvin probe method, [PERSON] et al. discovered the vacuum level shift at various organic/metal interfaces, where band bending does not occur [3; 4], meaning that the Mott-Schottky model is not applicable to OLEDs. Furthermore, by using a time-resolved optical second harmonic generation measurement while applying a rectangular alternating voltage to typical OLEDs, [PERSON] et al. reported that charges accumulate at the organic/organic interface under forward bias with high frequency, and they proposed a model of emission induced by the accumulated charges at the organic/organic interface [5; 6; 7; 8]. Moreover, these studies signify that, to understand the OLED emission mechanism and control its performance, we must focus on the electronic states at the interfaces as well as the work functions and molecular orbital energy levels.  
We have been attracted by the report that the emission intensity for an ITO/\(\alpha\)-NPD/Alq\({}_{3}\)/Al device under an alternating reverse-forward bias becomes higher than that under an alternating zero-forward bias in the frequency region between 8 \(\times\) 10\({}^{5}\) and 10 Hz [7]. The change in the emission intensity is reported to be due to suppression of the charge injection caused by the charge accumulation at the organic/organic interface under the alternating zero-forward bias.  
In this study, we focused on the effect of reverse bias on the device properties. [PERSON] et al. reported that a reverse bias with a long application time (10\({}^{1}\)-10\({}^{4}\) s) induces the movement of ionic impurities in the active layer, leading to emission enhancement and lifetime improvement [9; 10]; however, this mechanism is not acceptable in the high frequency region because the ionic impurities could not follow the frequency. To investigate the generality of the higher emission intensity under an alternating reverse-forward bias than that under an alternating zero-forward bias, OLEDs with the structure ITO/TPD/Alq\({}_{3}\)/Al and ITO/CoPc/Alq\({}_{3}\)/Al were fabricated, where TPD is 4,4\({}^{\prime}\)-bis[N-phenyl-N-(m-tolyl)amino]biphenyl, Alq\({}_{3}\) is Tris(8-quinolinolato)aluminum, and CoPc is cobalt phthalocyanine. We propose another model in which an OLED works as a capacitor under reverse bias, and the accumulated charges at the organic/metal interfaces induce an enhancement of emission under the alternating reverse-forward bias.
## 3 Stability Analysis Based on Lyapunov Exponential Method
### MATLAB Simulation Analysis
#### 3.3.1 Takeoff Stage
When the tether cable density changes from \(\rho\) = 0.92 g/cm\({}^{3}\) to \(\rho\) = 1.84 g/cm\({}^{3}\), the overall stability of the tethered UAV decreases by 61.07%.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods
### Quantum Circuit Modeling  
In order to construct the datasets, a Python package performing high-level quantum computing tasks was used. The package qiskit developed by IBM was used to generate QPE circuits with either 2, 3, 4 or 5 qubits in the first register and 1 qubit in the second register. Aside from the number of qubits in the first register, the distinguishing features of a standard QPE circuit are the specifications of \(U\) and \(|u\rangle\). In our case, regardless of the number of qubits in the first register, the unitary operator whose phase was being estimatedwas the phase gate \(P(\phi)\) from Equation (6). The phase gate denoted in matrix form, and its eigenvectors \(|0\rangle\) and \(|1\rangle\) are shown in Equations (7)-(9). The corresponding eigenvalues are \(1\) and \(e^{i\phi}\).  
\[P(\phi)=\begin{bmatrix}1&0\\ 0&e^{i\phi}\end{bmatrix} \tag{7}\]  
\[|0\rangle=\begin{bmatrix}1\\ 0\end{bmatrix} \tag{8}\]  
\[|1\rangle=\begin{bmatrix}0\\ 1\end{bmatrix} \tag{9}\]  
The second register is initially in the state \(|0\rangle\), but an \(X\) gate is applied to the qubit transforming it into the \(|1\rangle\) state. Thus, when the unitary operations are applied, \(U|u\rangle=e^{2\pi i\theta}|u\rangle\) becomes \(P(\phi)|1\rangle=e^{i\phi}|1\rangle\), so we have \(\theta=\phi/2\pi\). The values of \(\theta\) estimated by the QPE circuits were chosen uniformly from the interval \([0,1)\), and thus the values of \(\phi\) in each Phase gate were chosen uniformly from the interval \([0,2\pi)\). For each value of \(\theta\), \(21\) circuits were generated with different levels of noise, the specifications of which are detailed in Section 2.2.
# Fine Characterization Method of Concrete Internal Cracks Based on Borehole Optical Imaging
## 4 Discussion  
The development of a crack is a dynamic process, and the change in its morphological characteristics with time is an important factor to be considered. The stable or slow development of crack morphology is the desired result of engineers. If the cracks continue to extend and widen over time, this will bring great difficulties to the evaluation and repair of concrete structures. Therefore, it is necessary to regularly observe the crack morphology. Due to the construction requirements, most of the boreholes were quickly blocked after the first measurement. Only boreholes zk15 + 018 and zk14 + 994 were allowed to remain as observation boreholes. Fifteen days after the first measurement, boreholes zk15 + 018 and zk14 + 994 were measured again with the same borehole camera equipment. Figure 11 shows the comparison of borehole wall images obtained from the two measurements. There was only a slight difference between the two measurement results. In borehole zk15 + 018, the second measurement found that the crack continued to extend 6 mm downward, while in borehole zk14 + 994, no significant crack extension was found. The crack width of the two boreholes at the orifice basically increased by 2 mm. This may be due to the vibration caused by vehicles and pedestrians on the tunnel surface, resulting in the falling off of mortar and aggregate. However, below the orifice, the crack width had no obvious expansion trend.  
The development of cracks can be monitored by using the borehole camera technology to conduct periodic measurements in the same borehole. In this paper, subtle differences in the development of cracks were found in the two measurements, and the cracks observed in the two boreholes were basically in a stable state. However, since the number of boreholes measured in the second time was too small, it is arbitrary to give the development law of cracks in a large area. If the construction conditions permit, the number of observation boreholes and the measurement period shall be increased in similar monitoring schemes.  
Figure 10: Crack extension characteristics at the edge of coarse aggregate.  
The borehole camera technology can accurately measure the morphological characteristics of the cracks on the borehole surface, but it cannot measure the cracks in the concrete around the borehole. By increasing the borehole density, a comprehensive analysis of multi-borehole data can help reduce the impact of this limitation. In addition, it is difficult for a single detection technology to fully meet the requirements, and the combined use of multiple measurement methods can make the results more detailed and comprehensive.
# Machine Learning for Child Oral Health: A Scoping Review
## 1 Introduction
Furthermore, ML offers significant support to dental professionals in detecting and diagnosing conditions promptly and accurately, predicting outcomes, identifying at-risk individuals, standardizing care, and reinforcing clinical findings [13]. Specific applications include evaluating development and growth of a child to aid in orthodontic diagnosis [19], recognizing early signs of dental pathology [20], evaluating dental plaque levels [21], and identifying teeth such as supernumerary [22], deciduous, and permanent [23]. The integration of ML tools aligns with the profession's core focus on patient education, paving the way for preventative and predictive dentistry, ultimately elevating the standard of care. Within the ML domain, deep learning, employing multilayered neural networks, stands out as a prominent field. Notably, convolutional neural networks in deep learning have demonstrated efficacy in detecting dental caries in radiographs, offering a sophisticated approach to image analysis [24]. Research suggests that artificial neural networks (ANNs) can improve radiographic accuracy by locating the apical foramen and identifying the working length on radiographs [25]. An in vitro study used ANN to calculate composite Vickers hardness ratios, shade, and light-curing units [26]. Early childhood, defined by the World Health Organization as the period from prenatal development to eight years of age, is a critical window for health interventions due to its profound influence on lifelong well-being [27]. By age of eight, permanent first molars, central incisors, and lateral incisors are typically erupted, marking a crucial stage for preventive dental care [28]. This period represents a time when children are particularly vulnerable to oral conditions, and early interventions can have long-lasting impacts. The application of ML during this formative period holds immense potential for timely identification and management of dental issues, given the rapid developmental changes and the need for age-specific diagnostic and treatment approaches. This review aims to explore ML applications within this key demographic, uniquely mapping the current literature while examining trends across countries with varying income levels. By identifying gaps in current practices and evaluating the potential of ML-based tools, this review seeks to illuminate opportunities for ML to advance pediatric dentistry, enabling predictive and preventive care that can elevate the standard of oral health management for children.