esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 3 Results
### PIP Warnings and Alerts  
When the \(AQI\) is between 101 and 150, indicating moderate warning according to Figure 2, PIP sends users a warning message via email. For instance, on 30 June 2023, the calculated \(AQI\) value was 138 in the Bronx. This exceeded the threshold for sensitive groups, prompting a warning message. Figure 14 shows the RTPF interface, where the exceedance of the \(AQI\) value can be observed. The interface highlights the elevated \(AQI\) levels and provides users with real-time updates and recommendations based on current AQ conditions. Notably, in Figure 14, the information level is marked in yellow on the RTPF platform. Figure 15 presents the content of the notification message sent by Azure Communication Services via email.  
Figure 14: Example of the RTPF graphical interface—warning message.  
Figure 13: Evolution of \(R^{2}\) Based on windowSize Adjustments in the AQIF model.  
On 7 June 2023, in the Bronx, the calculated \(AQI\) value was 194, which should trigger the transmission of an SMS message via Azure Communication Service. Figure 16 displays the graphical interface of the RTPF application, while Figure 17 presents a screenshot of the sent SMS message. In this case, the notification box is highlighted in red due to the elevated alert level, emphasizing the situation's urgency.  
Figure 16: Example of the RTPF graphical interface—alert message.  
Figure 15: Content of the email notification message.  
The MQ-AQI model has demonstrated practical usability due to its high \(OP\) of 95.61%. This level of precision was achieved through the customized design and implementation of the predictive algorithm RFR. Additionally, the AQIF model has shown an \(R^{2}\) accuracy of 71.62%, indicating its effectiveness in forecasting future \(AQI\) data.  
The RTPF application underscores practical usability by integrating the PIP component, which relies on the MA-AQI and AQIF models. These models empower authorities and the public to take informed actions regarding AQ, enhancing community awareness and promoting proactive measures for a healthier environment.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 4 Opportunistic Transmission Strategy for Farm WSN
### Selecting Forwarding Nodes Based on Backoff Time  
According to the broadcast characteristics of WSN, the current node can easily send packets to the nodes in the candidate set. However, if a packet is received by many candidate nodes but there is no suitable backoff scheme, these candidate nodes will reply ACK to the source node, which increases the collision possibility and causes unnecessary energy consumption. If the candidate node does not know the data forwarding status of other nodes, there will be multiple copies of the same packet in the network, resulting in invalid throughput.  
After the candidate node receives the packet transmitted by the source node, it first executes the backoff time before replying to ACK. This paper defines the backoff time based on the transmission cost:  
\[T_{d}=T_{i}+T_{a}\times\frac{CQ_{i}}{\max(CQ)} \tag{11}\]  
From the definition of backoff time, the lower the transmission cost is, the higher the forwarding priority is. The node with a smaller transmission cost will have the shortest backoff time. When the backoff time is over, the high-level forwarding node will broadcast the ACK first. In this way, when the source node receives the first ACK message, it knows that its packet has been successfully received. The backoff time of other candidate nodes receiving the packet is longer. If the ACK information from other nodes is received, the source node abandons the received packet and does not forward or reply. Using such backoff and ACK suppression strategies, the opportunity routing protocol of the farm WSN can make the source node unicast data to a candidate node, thereby completing data transmission and reducing the invalid copies in the network.
## 2 Data and Methods
### Sensitivity Analysis Methods
#### 2.3.2 Sobol Analysis Method  
[PERSON]'s analysis method, first proposed by [PERSON] in 1993 [PERSON] (1993), is a widely used quantitative global sensitivity analysis method. Compared with the qualitative sensitivity analysis method, this method can directly calculate the sensitivity index of model parameters. [PERSON]'s method is based on the idea of variance decomposition, and the total variance is decomposed into variance in terms of the interaction of one or several model parameters.  
If \(y=f(X)=f(x_{1},x_{2},\cdots,x_{M})\) represents the model structure, \(x_{1},x_{2},\cdots,x_{M}\) represent the model parameters, and \(M\) is the number of model parameters. Then, the variance decomposition formula can be expressed as follows.  
\[V(y)=\sum_{i=1}^{M}V_{i}+\sum_{i<j}^{M}V_{ij}+\cdots+V_{1,2,\cdots,M} \tag{6}\]  
where \(V(y)\) represents the total variance of model output \(y\). \(V_{i}\) is the variance term of the \(i\)th parameter. \(V_{ij}\) represents the variance term of the joint action of the \(i\)th and \(j\)th parameters. \(V_{1,2,\cdots,M}\) represents the variance term of the interaction of all parameters. The indicees of first-order sensitivity \(S_{i}\), second-order sensitivity \(S_{ij}\), and total sensitivity \(S_{Ti}\) are defined as follows.  
\[S_{i}=\frac{V_{i}}{V(y)} \tag{7}\]  
\[S_{ij}=\frac{V_{ij}}{V(y)} \tag{8}\]  
\[S_{Ti}=1-\frac{V_{-i}}{V(y)} \tag{9}\]  
where \(V_{-i}\) represents the variance without the effect of the \(i\)th parameter. First-order sensitivity \(S_{i}\) and the second-order sensitivity \(S_{ij}\) represent the influence of a single SWAT model parameter and the combination of two SWAT model parameters on the SWAT model output, respectively. Total sensitivity \(S_{Ti}\) describes the influence on the combination with the SWAT model parameters, including the \(i\)th parameter on the SWAT model output. Moreover, the difference between total sensitivity \(S_{Ti}\) and first-order sensitivity \(S_{i}\) can be used to analyze the mutual effect between the \(i\)th SWAT model parameter and other model parameters, which is described by the interaction effect.  
\[IE_{i}=S_{Ti}-S_{i} \tag{10}\]  
where \(IE_{i}\) stands for the interaction effect of the \(i\)th model parameter.
## 6 Applications of Chaos to Encryption
### Applications of Chaos to Random Number Generation
The modified map, analysed via bifurcation diagrams and Lyapunov exponents, exhibits a high number of chaos-related phenomena, including antimonotonicity and crisis. The map is then exploited for generating pseudo-random bit sequences via an effective algorithm [31]. The conceived bit generator readily passes the NIST statistical tests. Note that the proposed technique enables the complexity of any chaotic map to be increased. Moreover, note that the method in [31] can be generalized and, consequently, utilized for pseudo-random bit generators based on chaotic maps different from the logistic one. In [32], a novel chaotic system with hyperbolic sinusoidal function is introduced, where self-excited attractors and hidden attractors coexist. A chaotic circuit is designed and implemented. By varying its parameters, it is shown that the chaotic circuit presents two sets of hidden attractors (with line of equilibria and no equilibrium point), which provide additional complexity to the system dynamics [32]. On the basis of these results, a random number generator is designed, which successfully passes all the NIST statistical tests [32]. In [33], a modified logistic map that exhibits a number of different chaotic behaviours (i.e., antimonotonicity, crisis, and coexisting attractors) is proposed. The increased complexity of the map dynamics is then exploited to implement a chaos-based pseudo-random bit generator. The approach exploits techniques that include multiple map comparison, bit reversal and XOR [33]. The conceived generator passes all the NIST tests, confirming the validity of the approach. In [34], it is pointed out that most of the chaotic maps used for cryptographic applications have a number of weaknesses, including limited chaotic ranges, small Lyapunov exponents and heavy computational costs. The authors of reference [34] make an attempt to overcome these drawbacks by constructing a new chaotic map with complex dynamic behaviour via geometric operations. In particular, Lyapunov exponents and some associated theorems are mathematically derived, so that the map is characterized by good chaotic performances [34]. Moreover, the design of a pseudo-random number generator is carried out using the conceived chaotic map. Performance evaluations show that the conceived generator can effectively produce high-quality random sequences [34]. In [35], a generalization of the double-humped logistic map is introduced and applied to pseudo-random number generation (PRNG) for the encryption of medical images. The approach offers secure communication transfer of medical MRI and X-ray images. Different tests are carried out (i.e., sensitivity test, histogram analysis, correlation coefficients and NIST analysis), with the aim to highlight the encryption efficiency in transmitting medical images [35].
## 3 Results and Discussion
### Parametric Effect on Heat Transfer
#### 3.2.2 Effect of Mass Flux  
Figure 9 shows the variation in inner-wall temperature at different mass fluxes at the pressure of 8.0 MPa.  
Figure 8: Variations in inner-wall temperatures and heat transfer coefficients for various bulk enthalpies at different heat fluxes for the pressure and mass flux of 8.0 MPa and 100 kg/m\({}^{2}\)s, respectively. (**a**) Temperature; (**b**) Heat transfer coefficient.  
Figure 7: Variations in inner-wall temperatures and heat transfer coefficients for various bulk enthalpies at different heat fluxes for the pressure and mass flux of 7.5 MPa and 100 kg/m\({}^{2}\)s, respectively. (**a**) Temperature; (**b**) Heat transfer coefficient.  
As seen in Figure 9, mass flux greatly influenced the heat transfer coefficients, which increased significantly with the increase in mass flux. The characteristic is consistent with the results of regular fluids, whose thermo-physical properties do not vary too much with temperature. The turbulence intensity increased with the increase in mass flow rate, which resulted in a strong mixing effect and unstable flow. These factors led to an enhancement in heat transfer between the fluid and solid tube wall [18]. Meanwhile, unlike the ordinary fluids, the heat capacity and heat conductivity of supercritical CO\({}_{2}\) were much higher than that in the constant-property fluid. With the increase in mass flux, a large amount of heat was transferred and taken away when the system approached the pseudocritical temperature in the central domain, which also resulted in the enhancement of heat transfer between CO\({}_{2}\) and the solid tube wall. Therefore, the combination of these two factors enhanced the heat transfer coefficient with the increase in CO\({}_{2}\) mass flow rate.
## 2 Materials and Methods
### Receiver Operating Characteristics  
For the estimation of the statistical significance of an EQ prediction method, we use the Receiver Operating Characteristics (ROC) method [118], which is a plot of the hit rate (or True positive rate) against the false alarm rate (or False positive rate). ROC, therefore, depicts the quality of binary predictions.  
In particular, the ratio of the cases for which the alarm is ON and a significant event occurred over the total number of significant events defines the hit or true positive rate. The false alarm rate, on the other hand, is defined as the ratio of the cases for which the alarm was again ON but no significant event occurred over the number of non-significant events. A predictor will be useful only if the hit rate exceeds the false alarm rate. In addition, if a prediction is random, it will generate an equal number of hit and false alarm rates on average, and the corresponding ROC curves will have fluctuations depending on the number P of significant events (positive cases) and the number Q of non-significant events (negative cases) to be predicted.  
The area \(A\) under the curve (AUC) in the ROC plane is what determines the statistical significance of an ROC curve [119]. As it was shown in Reference [119], \(A=1-\frac{U}{\text{PQ}}\), where \(U\) follows the Mann-Whitney U-statistics [120]. In 2014, the statistical significance of ROC curves was visualized [121] using the envelopes of confidence ellipses, called \(k\)-ellipses, which cover the entire ROC plane. Using \(A\), one can measure the probability \(p\) to obtain an ROC curve passing through each point by chance.
###### Abstract
Note that low reliability would lead to frequent data retransmission, which would eventually result in longer delay, higher bandwidth consumption, and higher energy consumption. Therefore, reliability is of critical importance. To investigate the reliability of IoUT, the channel models play a vital role. In this paper, we thoroughly investigate the channel models, and validate the models through simulations. This paper provides a comprehensive study of the IoUT applications, challenges, and channel models. The main contributions of this paper are threefold:  
Figure 1: Network architecture of UWSNs. * we introduce and classify the potential IoUT applications;
* we point out the challenges for IoUT (i.e., the differences between UWSNs and TWSNs); and
* we investigate and evaluate the UWSN channel models, which are the technical core for IoUT. The rest of the paper is organized as follows. Section 2 introduces the practical and potential IoUT applications, and classifies the applications into five types. Section 3 points out the challenges for IoUT, and discusses the challenges from seven aspects. Section 4 thoroughly investigates the channel models for IoUT. Section 5 presents the evaluation of the channel models through simulations. Then, we discuss how this work accelerates the pace of research on IoUT in Section 6. Section 7 provides the IoUT related work. Finally, Section 8 offers a conclusion.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 5 Attack Scenarios, Features Selection, and Evaluation Scenarios
### Attack Scenarios  
Network attacks on SCADA systems can be divided into three categories: reconnaissance, command injection, and denial of service (DoS) [7]. Our focus in this paper is on the reconnaissance attacks where the network is scanned for possible vulnerabilities to be used for later attacks. A reconnaissance attack is the first stage of any attack on a networking system. In this stage, hackers use scan tools to inspect the topology of the victim network and identify the devices in the network as well as their vulnerabilities. Figure 4 shows our testbed attack scenario where the dashed rectangles highlight the vulnerable spots and possible attack targets in the system.  
Some reconnaissance attacks can be easily detected. For example, there are scanning tools which send a large number of packets per second under Modbus/TCP to the targeted device and wait for acknowledgment of the packets from them. If a response is received, the host (i.e., the device) is active. This attack generates a considerable variation in the traffic behavior which can be easily detected by the traditional IDS or even the traditional firewall or rule-based mechanisms. Figure 5 shows an example of the traffic behavior when a scanning tool was used in our testbed.  
Figure 4: Attack Scenario.  
On the other hand, there are some sophisticated reconnaissance attacks which are more difficult to detect. For example, some exploits can be used to map the network, which results in an attack behavior very similar to normal traffic. Figure 6 illustrates the network traffic behavior during such exploit attacks. As can be seen, the change in the traffic behavior is negligible under the attack. Thus, it is difficult to detect the attack. The use of rule-based mechanisms would fail because the signature of the Modbus and TCP traffic do not change, and the language used to express the detection rules may not be expressive enough. On the other hand, the use of ML can improve the detection rate as ML algorithms can be trained to detect these attack scenarios.  
We conducted the following reconnaissance and exploit attacks specific to the ICS environment described in Table 4. Details of the commands used to perform the attacks can be found in [28; 29]. During the attacks, the network traffic was captured to be analyzed. We used the following tools to analyze the captured traffic: Wireshark [30], and Argus [31]. The captured traffic included unencrypted control information of the devices (valve, pumps, sensors) as well as information regarding their type (function codes, type of data). Table 5 presents statistical information about the captured traffic.  
Figure 5: Network traffic behavior under easy to detect attacks.  
Figure 6: Network traffic behavior under difficult to detect attacks.
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
## 2 Experimental Setup and PCB Fabrication
### Structure of Haefely Standard PD Test Equipment  
The test system consisted of a Hipotronics AC dielectric testing set (Model 700-DI, Shelton, CT, USA) accompanied by a Haefely high-voltage capacitive divider (Haefely Test AG, Model: 9320, Switzerland) for accurate voltage measurements, as shown in Figure 1. Five test samples with different electrode designs were used. A Haefely PD analyzer was employed to capture the PD signals, which were then analyzed using a DDX 9121b PD detection system. This system was optimized for precise PD detection to ensure the accurate measurement of discharge activities.  
The AC voltage source, operating at 60 Hz, supplied a high voltage to the test subjects through a wideband quadrupole arrangement, providing a frequency range suitable for detecting PD signals--from 34 kHz at the low end to over-8 MHz at the high end. The bandwidth and center frequency were set to 400 and 230 kHz, respectively, to ensure the  
Figure 1: Structure of commercial Haefely standard PD test equipment.  
detection of the PD signals within the optimal frequency range for PD testing, as defined by standards such as IEC 60270. The capacitive divider reduced the high voltage to a safer, measurable level, allowing the system to record the voltage and PD signals without overwhelming the equipment. The data were simultaneously captured using a PD analyzer and voltage divider, and the results were stored for further analysis.
## 2 Materials and Methods
### Microsatellite Analysis  
Young or dried leaves from trees in orchards from Slovakia and Austria, respectively, or petioles or skins from apricot fruits purchased in shops, were used for DNA isolation using the DNEasy Plant Mini Kit (Qiagen, Hilden, Germany). Quality control and concentration of the isolated DNA was performed using a NanoDrop1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Eleven highly polymorphic microsatellite markers were selected and used to identify the apricot samples (Table 2). DNA amplification was carried out in a 25 \(\upmu\)L reaction mixture containing 2.5 \(\upmu\)L 1\(\times\) PCR buffer, 200 nM forward (labelled with 6-FAM) and reverse primers, 1.5-2.5 mM Mg\({}^{2+}\), 200 \(\upmu\)M dNTPs, 1 U of recombinant Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA), and 30 ng DNA using the GeneAmp PCR System 9700 (Applied Biosystems, Thermo Fisher Scientific Inc., Waltham, MA, USA). The PCR conditions were as follows: 95 \({}^{\circ}\)C for 5 min, 35 cycles of 94 \({}^{\circ}\)C for 1 min, 57 or 58 \({}^{\circ}\)C (Table 2) for 1 min, 72 \({}^{\circ}\)C for 1 min, and a final 72 \({}^{\circ}\)C for 10 min [18]. The PCR products were precipitated by ethanol, dissolved in sterile water, and stored at \(-\)20 \({}^{\circ}\)C. One microliter of purified products was mixed with 9 \(\upmu\)L of formamide containing LIZ 600 size standard (Applied Biosystems, Thermo Fisher Scientific Inc., Waltham, MA, USA), denatured at 95 \({}^{\circ}\)C for 3 min, and separated by capillary electrophoresis using the ABI 3100 Prism Avant (Applied Biosystems, Thermo Fisher Scientific Inc., Waltham, MA, USA). Electropherograms were analysed using a Peak Scanner 2 (Applied Biosystems, Thermo Fisher Scientific Inc., Waltham, MA, USA).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Cultivar** & **Pedigree** & **Country of Breeding** & **Geographical Origin \({}^{*}\)** & **Sampling Location \({}^{**}\) (Year)** \\ \hline
51 & ‘Stark Early Orange’ & \(-\) & USA & NA & GB, Slovakia (2023) \\
52 & ‘Mai Huang’ & \(-\) & China & CH & GB, Slovakia (2023) \\
53 & ‘Mai Chuan Sin’ & \(-\) & China & CH & GB, Slovakia (2023) \\ \hline \hline \end{tabular}  
* WE = West Europe; CE = Central Europe; NA = North America; CA = Central Asia; CH = China. \({}^{**}\) GB = Gene Bank.  
\end{table}
Table 1: _Cont._  
Figure 1: Map showing sampling locations on the Slovak (1–14) and Austrian sides (15–17). Names of the locations: 1—Myjava, 2—Cachtice, 3—Borovce, 4—Piesf’any, 5—Gstratic, 6—Bratislava Vajnory, 7—VerIke Ötany, 8—Dunajska Streda, 9—TvrdoSovce, 10—Barak, 11—Zeliezovce, 12—Svätý Peter, 13—Dulovce, 14—Sedliská, 15—Wachau, 16—Weinviertel, 17—Burgenland.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 3 ACER Routing Algorithm  
The ACER algorithm proposed in this paper aims to establish a connectivity-centered communication strategy to ensure the effectiveness of the communication process among construction robots through route selection and dynamic maintenance that incorporates routing path quality metrics. The algorithm is divided into four parts: route discovery, route selection, route maintenance, and topology control. The ACER routing algorithm flowchart is shown in Figure 1.  
Figure 1: Flowchart of ACER routing algorithm.