esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Grading Model of Traffic Interruption States Based on the Entropy Weight-TOPSIS Method and FCM  
### Entropy Weight-TOPSIS Method  
The entropy weight-TOPSIS method, an objective weighting multi-factor evaluation technique, integrates the advantages of the entropy weight and TOPSIS methods. Its evaluation procedure is as follows.  
First, multi-factor data are organized into a matrix denoted as \(X_{(m\times n)}\).  
\[X_{(m\times n)}=\left[\begin{matrix}x_{11}&\cdots&x_{1j}\\ \vdots&\ddots&\vdots\\ x_{i1}&\cdots&x_{ij}\end{matrix}\right] \tag{2}\]  
where \(i\) is the traffic interruption (\(i=1,2,\cdots,n\)); \(j\) is the index parameter of the interruption (\(j=1,2,\cdots,m\)).  
Data are normalized and standardized to eliminate the dimensions among different factors. The normalized index is adopted in the work.  
\[Y_{ij}=\left\{\begin{array}{l}\frac{x_{ij}-(x_{ij})_{min}}{(x_{ij})_{max}-(x _{ij})_{min}},\text{ }Normalized\text{ }index\\ \\ \frac{\left(x_{ij}\right)_{max}-x_{ij}}{\left(x_{ij}\right)_{max}-(x _{ij})_{min}},\text{ }\text{ }\text{Reversed index}\end{array}\right. \tag{3}\]  
Proportion \(P_{ij}\) of the \(j\)th parameter index in the \(i\)th traffic interruption is denoted by  
\[P_{ij}=\frac{Y_{ij}}{\sum_{i=1}^{m}Y_{ij}} \tag{4}\]  
where \(m\) is the number of traffic interruptions; \(Y_{ij}\) is the value of the parameter index after normalization and standardization.  
Information entropy \(E_{j}\) of the \(j\)th parameter index is as follows.  
\[E_{j}=-\frac{1}{\ln m}\left(\sum_{i=1}^{m}P_{ij}\cdot\ln P_{ij}\right) \tag{5}\]  
Utility value \(D_{j}\) of the \(j\)th parameter index is denoted by  
\[D_{j}=1-E_{j} \tag{6}\]Objective weight \(W_{j}\) of the \(j\)th parameter is calculated. \(n\) is the number of indices, i.e., \(n=3\).  
\[W_{j}=\frac{1-E_{j}}{\sum_{j=1}^{n}D_{j}} \tag{7}\]  
Weighting matrix \(Z_{ij}\) is obtained.  
\[Z_{ij}=W_{j}\cdot Y_{ij}=\begin{bmatrix}z_{11}&\cdots&z_{1j}\\ \vdots&\ddots&\vdots\\ z_{i1}&\cdots&z_{ij}\end{bmatrix} \tag{8}\]  
Each parameter index is defined, i.e., \(z_{j^{+}}\) and \(z_{j^{-}}\) are the maximum and minimum values of each column, respectively.  
\[\begin{array}{l}z_{j}^{+}=max\big{(}z_{1j},z_{2j}\ldots z_{nj}\big{)}\\ z_{j}^{-}=min\big{(}z_{1j},z_{2j}\ldots z_{nj}\big{)}\end{array} \tag{9}\]  
The ideal normalized and reversed distances are calculated.  
\[\begin{array}{l}D_{i}^{+}=\sqrt{\sum_{j=1}^{n}\left(z_{j}^{+}-z_{ij}\right)^ {2}}\\ D_{i}^{-}=\sqrt{\sum_{j=1}^{n}\left(z_{j}^{-}-z_{ij}\right)^{2}}\end{array} \tag{10}\]  
Equation (11) shows the score of each traffic interruption in [0, 100].  
\[Score_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}}\times 100 \tag{11}\]  
The flowchart of the entropy weight-TOPSIS method is shown in Figure 9:  
Figure 9: Flow chart of entropy weight\(-\)TOPSIS method.
## 3 Examples of New Empirical Laws from \(y_{2\text{A}}(x)\)
### Correction of Gamma-Ray Well Logs  
The knowledge of natural radioactivity is important not only for localising radioactive mineral deposits for nuclear energy use but also for quantifying the percentage of clay contained in many sedimentary formations, which strongly influences the medium permeability. A representative case of subsurface characterisation involves the measurement of gamma radioactivity in boreholes.  
Radioactive nuclides undergo a series of collisions with each other and other particles in a medium during their displacement. This produces two different effects: (1) attenuation when the particles are transformed in these collisions and (2) build-up in certain spatial areas owing to the trajectory deviations. The geometry of the source-detector system also affects the measurements, whose correction is conventional in laboratory processes. However, the environmental conditions are highly variable when measuring natural radioactivity in boreholes. Thus, empirical charts were used until 2018 for each set of environmental conditions and probes.  
This led to the development of several empirical relationships to quantify the attenuation and accumulation that occurs in cylindrical sources towards their interior (axis). In this case, the empirical relationships developed by [PERSON] et al. [7] not only provided analytical expressions to obtain the clay content but also implemented the accumulation effect, which had not been previously considered. This effect was not addressed in studies that  
Figure 4: Porosity exponent to estimate permeability using the NMR technique [6].  
attempted to establish a solution using Monte Carlo modelling, which focused on empirical relationships to obtain the effective distances for application in the radiation attenuation and accumulation equations when propagating through a cylindrical medium from the outside to the inside. That work concluded with formulas fitted to the historically used empirical charts. In addition to the application in wells, the relationships developed for this geometry in their inverse form are of great interest in the study of geological repositories of radioactive waste containers. However, these are admittedly specific applications.  
Nevertheless, in the procedure of arriving at these relationships, an empirical relationship must be included to reflect the sharp reduction to zero of the radioactive build-up as a function of energy at very low energies (Figure 5). Although the energy values of radioactive nuclides in nature are considerably higher than 0.1 MeV, without this consideration, the energy dependence would establish that the accumulation of radiation grows asymptotically toward \(\infty\) when approaching very low energies, which has no physical meaning.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 6 Discussion
### Limitations  
Some of the limitations of such systems in real-world scenarios include battery life (drones and sensors), overall cost, scalability-related issues, and storage issues. Notably, a major problem with these devices is their battery capacity. The main factor in the successful completion of a mission by drones is their lifespan. Large battery capacities are not a solution to this issue, as they increase the weight of the battery and, consequently, the energy required by the drone to lift that weight [29]. Finding the golden ratio is, therefore, necessary.  
Additionally, sophisticated algorithms [31], can aid in optimizing drones' battery usage. Of course, in this case, a powerful yet efficient algorithm is needed. Utilizing the many recharge stations across a specific area is an additional strategy [32]. Drones will, therefore, be able to land at the stations as needed and recharge their batteries without having to return to the base. Finally, using panels is yet another suggestion [33]. The drone will have to carry more weight, which will make flying more challenging, but there is also a sustainable energy source. This strategy is less useful for missions that take place at night or without much sunlight. The employment of potentially hybrid solutions, such as combination panels with backup batteries or relatively large-capacity batteries paired with optimization algorithms for more efficient power distribution, is more efficient. However, no precise solution to this problem has yet been discovered.  
As for the cost, the price of putting such a plan into action could be high. The price of computer boards, communication devices, networks, and sensors is in addition to the price of drones. Furthermore, a drone must be adequate in terms of size, design, and material construction (it cannot be a small, low-cost vehicle) in order to be efficient in the sense that it is stable in flight, has a long lifespan, and can carry a sufficient amount of payload. As a result, the financial requirements significantly increase. Moreover, the need for experts in this area in order to build and maintain such complicated systems could potentially significantly increase the overall cost.  
Scalability is a problem that plagues all blockchain-related apps. Adding more drones to the swarm makes it more resistant to attacks or single points of failure, but on the other hand, it adds to the time it takes for the nodes to reach a consensus and secure transactions. This might necessitate more computational power, which is not always acceptable. Additionally, as the number of drones in swarms grows, so does the complexity of such systems' implementations and architectures.  
Finally, the transactions that take place must be replicated by each node in the blockchain. Therefore, sufficient storage space is needed, especially in dense clusters where drones would conduct a lot of transactions while flying. Pruning methods, IPFS, and data optimization can all be used to address this issue.
# Exploring ISAC: Information-Theoretic Insights
## 6 Secrecy of ISAC Systems  
Secrecy is a major concern in ISAC systems, both for the communication and the sensing tasks. Depending on the applications, adversaries should not be able to learn the transmitted data and/or infer information about the sensing targets. The information-theoretic literature has mostly studied the problem of ensuring secrecy of messages [69; 85; 111; 112], but first results also exist to ensure secrecy of sensing information [70]. The model is important because ISAC systems enable the surveillance of the environment, and in many scenarios, it is crucial to prevent unauthorized access to user or channel information. In this section, we review both these lines of work.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 3 CMF Net
### Network Structure of CMF Net  
The network structure of CMF Net is shown in Figure 1, which consists of four parts: backbone network, region proposal network RPN, ROI pooling layer, and classification network. The first part is the backbone network, which mainly adopts the migration learning method, two multiscale feature extraction mechanisms, and a feature fusion mechanism. The output feature map contains both low-level visual features and high-level semantic features. The second part is the region proposal network RPN [22], which is mainly used to extract the region proposal frames containing the target for the feature map output from the backbone network and filter out about 300 high-scoring regions proposal frames. The third part is the ROI pooling layer, mainly used to map ROI regions to convolutional regions and pool them into feature maps of fixed size. The fourth part is the classification network, which is used mainly for target location correction and classification recognition after mapping the ROI pooling layer and achieving target detection.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 4 MAuth-CAN
### Sufficient Conditions for MAuth-CAN Resiliency and Sustainability  
CAN is resilient to masquerade attack if the authentication makes it to investigate every single message. Also, it is sustainable if the authentication is never disabled by BoA. MAuth-CAN achieves the above two goals by introducing a new authenticator equipped with two CAN controllers. In this paper, we show that MAuth-CAN achieves the above two goals with the following properties and prove them using model checking.  
Theorem 1: _If MAuth-CAN authenticator uses a reception queue of size 2 for incoming new messages and the authentication time is always less than the message transmission time, it never fails to transmit AFR messages and the duration for ECUs to wait for AFR message needs no longer than 4 \(\times\) T\({}_{tx}\)._  
Theorem 1 emphasizes on the size of the reception queue for the CAN controller of the authenticator and the relation between the authentication time and the transmission time. The size of the CAN controller's reception queue is relevant to the resiliency of MAuth-CAN authenticator. The relation between the authentication time and the transmission time is relevant to the waiting time of ECUs for AFR messages.  
Theorem 2: _MAuth-CAN authenticator is sustainable under BoA if it uses two CAN controllers._  
The sustainability of MAuth-CAN in Theorem 2 means that MAuth-CAN is never enforced to be off from CAN bus. In order to prove Theorem 2, we focus on TEC of authenticator's CAN controllers because TEC of the CAN controller goes over the threshold of Passive Error mode, then the CAN controller is enforced to leave CAN bus for a while. Thus, we will show that TEC of authenticator's CAN controllers never goes over the threshold of Passive Error mode even if that of attacker's CAN controller goes over the threshold. In next section, we will prove the above two theorems using model checking techniques.
## 4 Results and Discussion
### Performance Evaluation of Actuator Component  
#### 4.2.1 Clutch Actuator  
In order to evaluate the position control performance of the clutch actuator, the range shift of the transmission was set to the 2 nd and then 3 rd gears, and the response performance was evaluated, according to gear stages, by moving the clutch from disengaged to engaged, as shown in Figure 12. In the entire range of gear stages, as the maximum overshoot and steady-state errors were less than 0% and 1%, respectively, the target control performance was satisfied, as shown in Table 4. The settling time of the disengaged \(\rightarrow\) partially engaged (a) section was about 0.500 s, and that of the partially engaged (a) \(\rightarrow\) fully engaged (b) section was about 1.00 s, and all showed similar results, regardless of the change in gear stages. The settling time, including disengaging \(\rightarrow\) partially engaging (a) \(\rightarrow\) fully engaging (b), was less than 1.50 s in most gear stages, which also satisfied the target control performance.  
Figure 11: Comparison of simulation and component experiment results of (a) shuttle shifting actuator and (b) clutch actuator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Actuator**} & \multicolumn{2}{c}{**Max. Overshoot (\%)**} & \multicolumn{2}{c}{**Settling Time (s)**} & \multicolumn{2}{c}{**Steady-State Error (\%)**} \\ \cline{2-7}  & **Simulation** & **Experiment** & **Simulation** & **Experiment** & **Simulation** & **Experiment** \\ \hline Shuttle shifting & 3.00 & 3.03 \(\pm\) 0.02 & 0.44 & 0.42 \(\pm\) 0.01 & 0.00 & 0.002 \(\pm\) 0.001 \\ \hline Clutch & 0.00 & 0.00 \(\pm\) 0.00 & 1.06 & 1.04 \(\pm\) 0.01 & 0.00 & 0.002 \(\pm\) 0.001 \\ \hline \end{tabular}
\end{table}
Table 3: Response performance in simulation and experiment for shuttle shifting and clutch actuators.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 2 Previous Related Works
### Application of Delay Tolerant Sensor Networks (DTSN)  
DTSN originates from the concept of spatial communication proposed by NASA, and it was tested with Spirit and Opportunity missions to Mars [14]. In recent years, the DTSN architecture has been extended to more sophisticated communications environments such as underwater wireless sensor systems, driverless vehicle systems, remote and extreme environment monitoring, and rural communications.  
For DTSN, mobile sensor nodes are often used as mobile data collectors (DMC) to process the data, or to send the data to the monitoring center. Static nodes can interact with the mobile nodes and forward messages [15]. Recently, a couple of DTSN-based projects have been actively carried out by IT companies, such as Google, Facebook and Intel [16; 17]. The main purpose of these projects is to build low-cost Internet services in remote areas. Google launched its Loon project [16], using the stratospheric solar-powered balloons and ground base to build dynamic DTSN network services in remote areas. Facebook's internet.org [17] uses local infrastructure, unmanned aerial vehicles and communication satellites to provide limited Internet services based on DTSN in remote rural areas.  
Emergency response operation based on DTSN has become a promising application area in mobile ad-hoc networks (MANET) [18]. For example, in disaster scenarios, the delay/disruption tolerant network (DTN) can be used to establish emergency support communications for ease of rescue [19]. In DTSN, a message from a source node may be delivered to the destination node despite the nonexistence of an infrastructure and an end-to-end connectivity [20]. Therefore, more and more DTSN is used to solve the emergency communication problems after the destruction of telecommunication facilities.  
Some research institutes have designed and developed some DMC for DTSN. Massachusetts Institute of Technology (MIT) has daknet [21], where buses or public service vehicles are used as DMC to provide Internet services in remote rural areas. In underwater wireless sensor network (UWSN), an autonomous underwater vehicle (AUV) is also used as a mobile node for message transmitting [22; 23]. This research also developed a DTSN mobile node, the Inspection Robot [6; 7] for constructing the grid monitoring system in remote areas.  
The characteristics and research directions of DTSN are shown in Figure 2. All DTSN applications have the following characteristics.  
* The nodes in the network distribution are sparse and mobile.
* The application environment is complex, and ordinary communication is often not available.
* Application (monitoring data) has the characteristics of delay tolerance.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC2: Urban Environment  
The urban environment solution in Figure 10 illustrates UC2. As observed, the backhaul connectivity shows a street canyon shape, which clearly describes an urban environment as opposed to the rural instance of UC1.
## 3 Results and Discussion  
### Characterization of 2D SERS Substrate  
The morphologies of BP and BP-Au nanosheets (BP-AuNSs) were detected by transmission electron microscopic (TEM) analysis. As shown in Figure 1a, the BP nanomaterial showed the typical lamellar structure with several hundreds of nanometers in size. After the reaction with the gold salt, the surface of the nanosheet was attached with the spherical Au NPs with a mean size of 26 nm (Figure 1b). The existence of Au and P elements in nanohybrids could be proved by the EDX pattern of BP-AuNSs (Figure 1c). The ultraviolet-visible-near infrared (UV-vis-NIR) absorbance spectrum of BP nanosheet exhibited a decreasing light absorption from UV to NIR region (Figure 1d). A new absorption peak with the central wavelength of 535 nm was observed in the absorption spectrum of BP-AuNSs, further confirming the formation of Au nanoparticles on the BP nanosheets. Figure 1e displayed the Raman spectra of BP and BP-AuNSs, which revealed the three characteristic Raman peaks of BP nanostructure at about 360, 437 and 464 cm\({}^{-1}\), assigned to the one out-of-plane phonon mode (A\({}^{1}_{\text{g}}\)) and two in-plane modes (B\({}^{2}_{\text{g}}\) and A\({}^{2}_{\text{g}}\)), respectively [31]. These three bands in the Raman spectrum of BP-AuNSs experienced a slight blue shift, which could be ascribed to the interaction between BP and the metal nanostructures.
## 4 915 MHz/2400 MHz Bands' Performance in SG-oriented IoTs  
In this section, we present and discuss the performance results, in terms of the three key performance indicators, of the two scenarios evaluated. We start by listing the variables employed in the Scenario 1 (the one with 2.4 GHz co-existing devices) and the obtained results. Then, the noise conditions are altered (Scenario 2) to show a different condition on which an SG-oriented IoT may operate.  
Both scenarios share some common parameters: the payload lengths assessed (6, 12, 18, and 24 bytes), the scaling factors (\(\alpha\)) evaluated (0.5 to 1.5 in steps of 0.05), the transmission rate of each band (40 Kbps for the 915 MHz band and 250 Kbps for the 2400 MHz one, as defined by the 802.15.4 standard), and the packet generation rate (modeled via a random uniform distribution which generates a packet every 1 to 3 s). Data generation rate, payload length and \(\alpha\) ranges are in line with the nature of SG-oriented IoT services and are chosen so as to effectively illustrate when it is more beneficial to work in one band or another. Furthermore, the above packet lengths reproduce the wide variety of services that SG-oriented IoT may offer: from very limited reports that may fit in payloads of six bytes, such as informing of a particular switchgear temperature, to more complex ones that may need (one or more) 24-byte packets, such as a complete report of a transformer status.
## 3 Theoretical Framework
### LEF Approach  
The linear effusivity fit is a method introduced by [PERSON] and [PERSON] [7], using the Laplace transform to transform both the irradiation pulse and the temperature response from the surface into the frequency domain to determine the thermal effusivity by fitting a linear function to the quotient of the transformed data. It can be briefly described as a four-step process:  
1. Transform both irradiation pulse \(q(t)\) and surface temperature \(T(t)\) using the discrete Laplace transform into the complex frequency domain to obtain \(\overline{q}(s)\) and \(\overline{T}(s)\), respectively. Note that \(s\) denotes the complex Laplace operator value.
2. Calculate the quotient of the aforementioned signals: \[\overline{Z}(s)=\frac{\overline{T}(s)}{\overline{q}(s)}\] (8)
3. Thermal effusivity is given by: \[\overline{Z}(s)=\frac{1}{b\cdot\sqrt{s}}\] (9)Plotting \(\overline{Z}(s)\) over \(\left(\sqrt{s}\right)^{-1}\) results in a linear relationship: \(\overline{Z}(s)/\sqrt{s}=b^{-1}\), the slope \(m\) of this function being \(b^{-1}\).
4. A linear fit using \(\overline{Z}(s)\) over \(\left(\sqrt{s}\right)^{-1}\) is performed and the slope of this fit is inverted to obtain \[b=1/m_{Fit}\] (10)  
The linear fit is necessary to suppress noise and high-frequency content in the resulting transformed values, but in theory, two points from \(\overline{Z}(s)\) could be used to determine \(b\).  
This approach was employed in a region of interest-based context, where a single region of interest (ROI) was given as a mean value time vector to the algorithm to calculate the material properties of the whole ROI. Further, it was conducted on a pixel basis, where the LEF method was performed on each pixel to obtain an \"effusivity image\".
## 5 Results and Verification
### Modeling Result  
The probability of being classified into one of the three RORC severity categories in the situation of individual data was calculated. Among the calculated values, the category with the largest probability value was selected as the predicted value. The main function of the existing regression model is to select factors that influence RORC severity, whereas machine-learning techniques, such as the Bayesian network, focus on predicting the category that the data will fall into among the three categories of RORC severity. For example, as shown in Figure 5, in case of a crash with a pier that is 2.5 m away from the roadside while driving on a road composed of a vertical slope of 4.5%, design speed of 80 km/h, damped road surface, weekdays, AADT is 33,000 vehicle/day, single lane, two entrance, weekdays, nighttime, a radius of the curve of 300 m, and with the presence of a compound curve, the probability of a fatal accident is 69.0%  
Table 2 shows the results of RORC severity prediction when applying road information and 403 traffic accident events that were collected using the constructed model. The severity representing the highest probability in the total 100% RORC severity distribution is presented as the severity of the corresponding interval. In the case of category 1, the probability of slight accident is 48.3%, which is higher than that of serious and fatal accidents, so the section is predicted as a slight accident section. The predicted severity was compared with the actual RORC severity, and a total agreement of 91.5% (369/403) was obtained.