esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Simulation Results
### Simulations on the Effectiveness of the Pollution Control Measures
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Air pollution Control Measure** & **Average APM\({}_{2.5}\) (ug/m\({}^{3}\)) 1** & **Expected Reduction in Number of Deaths** \\  & & **(90\% Confidence) 2** \\ \hline \hline \multicolumn{1}{c}{Regulate power facilities (stricter power sector regulations and standards)} & 0.405 (0.03–1.13) & 827 (213–2022) \\ \multicolumn{1}{c}{Install pollution control devices in state-owned businesses (e.g., & 0.050 (0–0.44) & 82 (21–199) \\ \multicolumn{1}{c}{Decommissioning of bolsters (accelerate the retirement of 5000 industrial boilers and 1000 commercial boilers)} & 0.061 (0.01–0.19) & 114 (29–280) \\ \multicolumn{1}{c}{Control smoke from 7000 restaurants} & 0.114 (0.01–0.21) & 206 (53–504) \\ \multicolumn{1}{c}{Change fuel-burning customs and traditions (increase centralized} & 0.016 (0–0.03) & 27 (7–67) \\ \multicolumn{1}{c}{\begin{tabular}{c} Regulate fugitive dust from construction sites and stockiples of dust-generating materials, increase conformance to 90\% \\ Improve control smoke from agricultural waste-burning (reduce the area of openair burning by 90\%) \\ \multicolumn{1}{c}{Control fugitive dust from riverbeds} & 0.105 (0.01–0.19) & 188 (48–460) \\ \multicolumn{1}{c}{\begin{tabular}{c} Retire 80,000 Stage 2 and stage 2 diesel trucks \\ Install exhaust filters in 380,000 Stage 3 diesel trucks \\ Eliminate 1 million 2-stroke motorcycles \\ \end{tabular} } & 1.959 (0.12–418) & 3846 (94–9415) \\ \multicolumn{1}{c}{\begin{tabular}{c} Install exhaust filters in 380,000 Stage 3 diesel trucks \\ Eliminate 1 million 2-stroke motorcycles \\ \end{tabular} } & 0.046 (0.01–0.08) & 87 (22–213) \\ \multicolumn{1}{c}{Promote electric vehicles (up to 2100 vehicles) for the transportation} & 0.033 (0–0.01) & 5 (1–13) \\ \multicolumn{1}{c}{of fresh production} & 0.0529 (0.04–1.48) & 994 (255–2430) \\ \multicolumn{1}{c}{\
\begin{tabular}{c} Steptering air pollution controls in port areas (reduce ship speeds, negotiating ship fuel usage, promote the use of shore power) \\ Set strict emission standards for automechnics that are 10 years or older, and set up air quality maintenance zones, where the entry of highly puluting vehicles is restricted or forbidden \\ \end{tabular} } & 0.529 (0.04–1.48) & 994 (255–2430) \\ \multicolumn{1}{c}{
\begin{tabular}{c} Siet striest emission standards for automechnics that are 10 years or older, and set up air quality maintenance zones, where the entry of highly puluting vehicles is restricted or forbidden \\ \end{tabular} } & 0.107 (0.01–0.21) & 208 (53–508) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Improvement in air quality and health impact due to each pollution control measure under the 14 air pollution control strategies (TAPCAP).
## 6 Applications of VSNs
### Structural Monitoring  
Intelligent monitoring in facility management can make safety checks and periodic monitoring of the architecture status highly efficient. Powered nodes can be included in the design phase of a construction or battery-equipped nodes can be added afterwards. All nodes are static and manually deployed. Some data such as normal room temperature is not critical for security protection, but event-driven emergency data must be handled in very critical manner. Let us consider the following scenario for further clarification: a 1,000 m long bridge with 10 pillars is described. Each pillar and the bridge body contain five sensors to measure the water level, and five vibration sensors are used to monitor its structural health. The VSN nodes are deployed to have 100 m line-of-sight distance from each other. All nodes are placed statically and manually configured with a single-hop connection to the local coordinator. All VSN nodes do not move while the service is provided. The network configuration and forwarding/routing tables are changed only in case of node failure. Except from the pillars, there are no special obstacles to attenuate the node signals, but careful configuration is needed to prevent signal interference between VSN nodes. On the top part of each pillar, an \"infrastructured\" sink node is placed to collect the sensed data. The sink nodes of each pillar become data gathering points of the VSN hosts at the pillar as coordinators.
This investigation specifically examines the terminal guidance loop of anti-ship missiles operating in mono-pulse tracking mode. To address these complexities, we have developed a multi-stage trajectory optimization model that integrates both the missile's dynamic characteristics and real-time tracking data. The model accounts for various complex constraints inherent to the system. Additionally, through enhancements to the multi-objective particle swarm optimization algorithm (MOPSO) for handling complex objective functions, the missile can achieve terminal trajectories characterized by large impact angles and minimal miss distances across diverse simulation scenarios. Current research is limited to the Data-Driven Modeling, Control, Optimization, and Decision-Making in Intelligent Systems, which is beneficial in providing a rigorous and practical solution for anti-ship missile trajectory design and interaction with shipborne air defense systems in high-dynamic environments, and does not pose a threat to public health or national security.
## 1 Introduction  
The use of social and telecommunication networks has dramatically increased recently, resulting in new prominent applications of network analysis. In addition to these real-world applications, network representations of new types of data, in particular biological data, highlight the drastic need for a new, multi-dimensional, type of (sub)network mining in which several networks, representing several relations between the same objects, are simultaneously investigated for the extraction of a multi-dimensional pattern [1; 2; 3].  
The study of multi-dimensional mining started several years ago, but it mainly concerns homogeneous representations of data: directed graph alignment [4], undirected graph alignment [5], relational data mining [6] and social networks mining [2] are several examples. Recently, such approaches found applications in computational biology [7; 8; 9], but also showed their limits, due to the multiple types of biological networks that are used to describe different views of the same biological process. In such applications, a process is often represented as a path in a directed network, for example a metabolic network [9; 10; 11] or a signaling network [12; 13], and as a connected graph in an undirected network, for example a protein-protein interaction network [14]; the two networks are linked by the components involved in the process, which are represented as vertices in each network. Identifying a particular biological process then requires identifying parts of the two networks (directed and undirected) that have the desired topological patterns and the same vertex set. This multi-dimensional mining approach has led to the discovery of novel biological insights [3; 15; 16; 17].  
In this paper, we consider a network mining across two heterogeneous networks, driven by the previously-cited applications in biological networks. Following the situation described above, we consider the case in which we are given a directed graph \(D\) and an undirected graph \(G\) and want to find a path \(P\) in \(D\) (that is, a graph with vertex set \(V(P)=\{p_{1},\ldots,p_{k},p_{k+1}\}\), \(k\geq 1\), and arc set \(\{(p_{i},p_{i+1}):1\leq i\leq k\}\)), whose vertices induce a connected subgraph in \(G\). In the application context when \(D\) is a metabolic network and \(G\) is a protein interaction network, such a problem corresponds to searching for a chain of reactions (a path in \(D\)) involving proteins that form a complex (that is connected in \(G\)).  
**Definition 1**.: _Let \(D=(V,A)\) be a directed graph and \(G=(V,E)\) be an undirected graph on the same vertex set. A \((D,G)\)-supported path is a simple (directed) path \(P\) of length at least one in \(D\), such that the subgraph of \(G\) induced by \(V(P)\), denoted \(G[V(P)]\), is connected._  
The length restriction in the definition simply allows us to disregard trivial \((D,G)\)-supported paths consisting of only one vertex. We consider several natural variants of finding \((D,G)\)-supported paths. The most basic problem simply asks for the existence of such a path.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 5 MQTT-SN Attacks and Performance Evaluation
### Sniffing Attack  
This attack is based on a well-known MitM scheme. It can be performed using layers below the application layer, where MQTT-SN operates. This case is different from the rest of the attacks discussed in this paper because the attackers need to achieve an MitM scheme, a process that requires no use of MQTT-SN features. This attack takes advantage of the unencrypted connection (by default) in MQTT-SN communications, but has the limitation (compared with the others presented in this paper) that it needs a third protocol to complete the MitM scheme. Normally, MQTT-SN is supported with other lower layers such as RPL [24] or 6 LoWPAN [21]. For this attack, it is firstly necessary to compromise the communications by attacking these protocols, and once the attacker is in the middle of the communications, he/she has access to MQTT-SN information. The impact of this attack could be extensive: first of all, it can affect confidentiality; secondly, integrity suffers because the attacker can modify the messages; finally, availability is not guaranteed because the attacker can break the communication. Figure 13 depicts an MitM attack over the MQTT-SN protocol. We can see that the malicious device is placed between the broker and the legitimate devices.  
Figure 12: Disconnect wave attack evaluation diagrams. (**a**) MQTT-SN broker after disconnect wave attack. (**b**) MQTT-SN legitimate client after the disconnect wave attack.  
Figure 13: MQTT-MitM scheme.  
Evaluation of the Sniffing Attack  
This attack is successful when there is unencrypted communication, and it must be preceded by a man in the middle attack. In our experiment, a rank attack against RPL [37] was used to achieve the MitM scheme. The consequence of this attack is that the attacker gains access to the information exchanged between the broker and the devices (see Figure 14). This is not a pure IoT attack, but in this context, it can be more dangerous, because when there are constrained devices, it is more difficult to implement an encryption algorithm to protect the communication. One way to tackle this problem is to use Secure MQTT-SN (SMQTT-SN) [38], but this implies an overhead that is introduced in the communication and more resources employed on the devices. This attack does not affect network performance, but then again, it is an attack that compromises system confidentiality.
## 3 Results and Discussion
### Electrophretic Analysis of DNA Damage in the Plasma-Treated C. pruinosa Spores  
In general, a single cell spore contains genomic DNA, which is a large molecule. Typically, DNA extraction results in a single high molecular DNA band during agarose gel electrophoresis. If genomic DNA is damaged due to physical shearing or chemical exposure, degradation occurs. DNA degradation can be visualized as a weakened high molecular DNA band and the smearing of DNA on agarose gels after staining with ethidium bromide or other dyes [30]. Smearing occurs when many bands originated from degradation go along the path of DNA movement in the gel. Considering this, we further analyzed DNA damage of _C. pruinosa_ spores using agarose gel electrophoresis. DNA staining with Ecodyne reveals that more DNA produces a stronger band intensity. Figure 9 shows the  
Figure 8: Flow cytometric analysis of phosphorylated histone fluorescence of _C. pruinosa_ DNA damage caused by soft plasma jet treatment. C, 1, 2, 3, 6, and 10 represent phosphorylated histone fluorescence of the control, 1 min, 2 min, 3 min, 6 min, and 10 min plasma-treated _C. pruinosa_ spores, respectively.  
results of electrophoretically separated fungal spore DNA. A tight single strong DNA band with high molecular weight (a band of maker DNA lager than 13 kb) and a very strongly smeared DNA trace lower than 500 bp of the maker DNA (lane M) were present in lane 1 of the gel containing control DNA (from _C. pruinosa_ spores without plasma treatment). This smeared trace of DNA degradation is common in genomic DNA extraction because this procedure involves disrupting cells, denaturation of nucleoprotein complex, inactivation of nucleases (which allows DNA samples to be exposed to heat or physical shearing), and residual nucleases remaining in the DNA samples [31]. Single high molecular DNA bands were also observed in lanes 2 and 3 of the gel containing DNAs from _C. pruinosa_ spores with plasma treatment for 1 and 3 min, respectively. However, the intensities of these single DNA bands in lanes 2 and 3 were weaker than that in lane 1. Note that the single DNA band in lane 3 is visibly weaker than that in lane 2. In addition, the smeared DNA in lanes 2 and 3 is less than 1 kb and is both less apparent than that in lane 1. Smeared DNA less than 1 kb almost diminished in lanes 4 and 5. These results indicate that DNA degradation increased with increasing plasma treatment time. Furthermore, single high molecular DNA bands were not present in lanes 4 or 5, implying that plasma treatment for 6 or 10 min completely degraded the spore DNA. Overall, progressive degradation of DNA involving a reduced band intensity and the absence of a single high molecular band in the electrophretic pattern was observed with increasing plasma treatment time. These results showed that plasma-triggered DNA damage involved DNA degradation of _C. pruinosa_ spores, which is detrimental to the biological activity of fungal spores.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Algorithm Verification and Analysis
#### 4.1.2 Parameter Impact Analysis and Comparison  
To further test the method under more rigorous conditions, we vary the entry flight path angle \(\gamma_{0}\) and the spacecraft's lift-to-drag ratio \(L/D\) in a wider range, and analyze their impacts on the performance of the guidance algorithm.  
Set the dispersion of the entry flight path angle to be uniformly distributed in [\(-0.5\), \(0.5\)] deg, and \(1000\) Monte Carlo simulations are carried out in the Earth scenario. The guidance results of the method proposed in this paper (Mode 1) and the method in the literature [24] (Mode 2) are shown in Figure 7. Through comparison, it can be clearly seen that when \(\gamma_{0}\) goes very steeper or very shallower, \(\Delta V\) increases significantly in both Mode 1 and Mode 2. Under the large variation of entry flight path angle, the minimum value of \(\Delta V\) obtained in Mode 1 is basically equal to that in Mode 2, indicating near-optimal performance. Moreover, the \(\Delta V\)'s dispersion in Mode 1 is smaller, and the standard deviation is reduced by 40.7% compared to that of mode 2. Furthermore, there is no extreme value of \(\Delta V\) exceeding 400 m/s in Mode 1, which also shows that our method is more adaptive to the variation of entry flight path angle.  
Then we study the impact of the lift-to-drag ratio on the performance of the guidance algorithm. The comparison results of 1000 Monte Carlo simulations in the Earth scenario are shown in Figure 8. It can be seen that although the \(\Delta V\) in both Mode 1 and Mode 2 increases significantly at the very low lift-to-drag ratio, the \(\Delta V\) in Mode 1 can still maintain  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Mode 1: Total** & \multirow{2}{*}{**Mode 2: Total**} & **Mode 1:** & **Mode 2:** \\  & \(\Delta V\)**, m/s** & \(\Delta V\)**, m/s** & **Inclination** & **Inclination** & **Inclination** \\  & & & **Deviation, deg** & **Deviation, deg** \\ \hline Mean & 65.1257 & 113.3175 & 0.0485 & 2.3632 \\ Maximum & 85.1014 & 346.2785 & 0.4903 & 4.1306 \\ Minimum & 44.6487 & 42.1140 & \(-0.2110\) & \(-0.0115\) \\ Standard deviation & 9.9212 & 78.9862 & 0.1387 & 0.9319 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results from Monte Carlo simulations for Earth aero capture.  
Figure 7: Dispersion of the velocity increments with the different initial flight path angles for Earth aero capture.  
a relatively small value at a very high lift-to-drag ratio. In addition, the standard deviation in Mode 1 is reduced by 51.1% compared with that in Mode 2, which proves our method's good robustness.
## 3 Case Study
Thus, one of the most efficient classical methods, the reduced gradient method (RG-CC), [32; 33], was chosen to compare the results and demonstrate the efficiency of the proposed method considering the average computational time (\(CT_{av}\)) and the value of the objective function, \(F_{Loss}\), in the three cases, see Figure 7:1  
Figure 7:2  
where \(ct_{h,c}\) refers to the computing time in which the solution of the OPF problem was obtained for hour \(h\), \(h\) = 1,..., \(T_{H}\), and the case \(c\), \(c\) = 1,..., \(N_{\textit{\tiny{cases}}}\). It can be observed that the time computational, \(CT_{av}\), is 31% faster than in the RG-CC, which demonstrates that the SQP-CC can be successfully implemented in real-time to change the tap positions. The second indicator highlights the accuracy of the SQP-CC methodology. In all three analyzed cases, \(F_{Loss}\) associated with the SQP-CC is smaller than in the RG-CC, by 1.32%, 3.07%, and 2.64%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistical**} & \multicolumn{6}{c}{**without Coordinated Control of the OLTCs**} & \multicolumn{6}{c}{**with Coordinated Control of the OLTCs**} \\ \cline{2-13}  & **Bus2** & **Bus3** & **Bus4** & **Bus6** & **Bus7** & **Bus8** & **Bus10** & **Bus2** & **Bus3** & **Bus4** & **Bus6** & **Bus7** & **Bus8** & **Bus10** \\ \hline \multirow{4}{*}{**Quartiles**} & **Q0** & 1.089 & 1.089 & 1.087 & 1.087 & 1.070 & 1.057 & 1.056 & 1.039 & 1.040 & 1.041 & 1.042 & 1.037 & 1.038 & 1.037 \\  & **Q1** & 1.090 & 1.091 & 1.089 & 1.088 & 1.072 & 1.059 & 1.059 & 1.050 & 1.047 & 1.047 & 1.045 & 1.047 & 1.047 \\  & **Q2** & 1.092 & 1.092 & 1.091 & 1.090 & 1.074 & 1.061 & 1.061 & 1.061 & 1.056 & 1.057 & 1.054 & 1.056 & 1.056 \\  & **Q3** & 1.099 & 1.099 & 1.098 & 1.096 & 1.083 & 1.069 & 1.069 & 1.067 & 1.066 & 1.068 & 1.069 & 1.061 & 1.062 & 1.061 \\  & **Q4** & 1.100 & 1.100 & 1.100 & 1.099 & 1.086 & 1.073 & 1.073 & 1.070 & 1.070 & 1.071 & 1.073 & 1.065 & 1.065 & 1.064 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The statistical parameters associated with the voltage at the buses, scenario S1, Case 3. Figure 16: The boxplots of the voltage at the level of the 110 kV buses, scenario \(S1\)—with coordinated control of the OLTCs: **(a)** Case 1; **(b)** Case 2; **(c)** Case 3.
## 1 Introduction  
Tunneling-based ternary complementary metal-oxide-semiconductor (TCMOS) technology has been reported recently [1; 2; 3]. Instead of the binary systems of the existing complementary metal-oxide-semiconductor (CMOS) technology, the third output voltage (V\({}_{\text{out}}\)) state is formed in the ternary systems, and it has been spotlighted in terms of scaling and energy-efficiency [4; 5]. In the TCMOS, the off-current (I\({}_{\text{OFF}}\)) levels of NMOS (n-type MOS) and PMOS (p-type MOS), which are generated by band-to-band tunneling (BTBT), should be matched to form the third V\({}_{\text{out}}\) state during inverter operations. In contrast to conventional ternary devices that utilize multithreshold voltage (Multi-V\({}_{\text{t}}\)) transistors [6; 7; 8; 9; 10; 11; 12], the TCMOS can perform ternary operations using a pair of NMOS/PMOS with a single voltage (V\({}_{\text{t}}\)); the fabrication process is also comparable to the conventional CMOS process, because it can be fabricated only by introducing one additional doping process. However, the TCMOS has the disadvantage of slow switching speed caused by low on-state current (I\({}_{\text{ON}}\)). In general CMOS, the time required for V\({}_{\text{out}}\) state transition is sub-nsec, while it takes ~usec for the TCMOS to be switched [1].  
In this study, TCMOS consisting of ferroelectric-gate field effect transistors (FE-TCMOS), consisting of ferroelectric-gate field effect transistor (FeFET), is proposed to improve switching speed and supply voltage (V\({}_{\text{DD}}\)) scaling since it is well-known that the FeFET boosts I\({}_{\text{ON}}\) and steepenssubthreshold swing (SS), compared to conventional MOSFETs [13; 14; 15; 16; 17]. To verify the operations of the FE-TCMOS, technology computer-aided design (TCAD) simulations with the calibrated ferroelectric material parameters are used and the ternary operations are rigorously compared between TCMOS and FE-TCMOS.
## 4 Model of the Average Outage Probability Factor  
The average outage probability factor \(\rho_{m}(f)\) shown in Figures 5 and 6, by setting \(f_{o}=16\) GHz, can be modeled (best-fit analysis) with the following mathematical expression:  
\[\rho_{m}(f)=1+(f-f_{o})^{a}-(f-f_{o})^{b} \tag{3}\]  
Notice that each addend in the right-hand site of Equation (3) has no particular physical meaning; what counts is the sum of the addends. The constants \(a\) and \(b\) are reported in Table 2, for each site. For Spino d'Adda, as example, for \(A=3\) dB, \(P_{16\text{ GHz}}(3)=0.081\%\) (Figure 1), now at 40 GHz the average probability factor, from (3) and Table 2, turns out to be \(\rho_{m}=9.70\); therefore, the estimated \(P_{40\text{ GHz}}(3)=0.787\%\) (outage time about 70 h in an average year) is to be compared to \(P_{40\text{ GHz}}(3)=0.954\%\) (Figure 3).  
In Table 2, notice that when the climatic rainfall regions are very similar, as is the case for Spino d'Adda and Gera Lario, the curves tend to coincide.  
Let us asses how good the best-fit given by Equation (3) is by studying the error it gives in considering only the carrier frequency as independent variable. We defined the error as follows (%):  
\[\varepsilon=100\times\frac{\rho_{m}(f)-\rho(A,f)}{\rho(A,f)} \tag{4}\]  
Figure 7 shows the mean error versus frequency. In general, the error is very low, for this kind of measurement, not exceeding \(\mp 30\%\). Notice, however, that  
continuous line); Prague (PR, magenta continuous line); Tampa (TM, cyan dashed line); Norman (NR, magenta dashed line); White Sands (WS, green dashed line).  
New satellite radio-links--such as those with spread-spectrum design--may occupy a large radiofrequency bandwidth centered at frequency \(f_{c}\), in which, for a fixed power margin, it is very likely to find different equivalent outages at the extremes of the bandwidth, which may produce signal distortions. In the next section we discuss this issue.
## 3 Bridge Type, SNR and Multipath in Geodetic Deflection Measurements
### Short, Stiff Bridges  
High-rate (10 Hz or higher, up to 100 Hz [15]) GNSS monitoring was recently expanded to short-span bridges. These bridges are usually stiff (f \(>\) 1 Hz), with small deflections, ranging from submillimeter level (c.f. [26], so that geodetic monitoring is not possible) to a level of a few mm or cm (see also Table 2). In such cases, typical GNSS measurement and analysis techniques lead to a SNR \(<\) 1, and hence to imprecise results. More refined geodetic analysis is, however, possible only in combination with other sensors (for example GNSS combined with RTS and accelerometers) and specific filtering (denoising) techniques; an example is shown in Figure 3; see also Figure 2 in [13].