esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we presented a detection method that has improved BER performance compared to the MVA [12]. The proposed model is able to achieve an approximately 2 dB gain at \(10^{-5}\) dB. With a three-way GPR target, the proposed detection scheme can also improve BER performance when TMR and position fluctuation occur. By employing the feedback line and target, the MVA can estimate the ITI and predict TMR. Due to this, it can choose the solution suitable for the ITI case and TMR case, respectively. With a three-way GPR target, the proposed model also improved the performance against the TMR effect and withstood fluctuations of nearly 18%. The proposed model requires training data to estimate the target before starting the detection of user data. In addition, the proposed detector is based on the MVA, which is fed the ITI information from the GPR and the feedback line. Therefore, the computational complexity is almost the same as conventional Viterbi methods. Finally, we can expect that it is possible to improve the BER performance of the proposed scheme if the MVA detection block is replaced by a BCJR detector with the cost of complexity.  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1 FA1046899).  
The authors declare no conflict of interest.  
Figure 18: BER versus fluctuation position from 6% to 18%.
## 4 Experiments and Discussion  
The primary objective of the experiments is to quantitatively and qualitatively verify the performance of the proposed model in remote sensing image segmentation. By comparing with traditional methods and the latest deep learning models, we aim to demonstrate the advantages of the proposed model in improving segmentation accuracy, handling boundaries, and detecting small objects.
# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
## 2 Literature Review
### Fault Tolerance and Multipath Transmission Technologies
Connection restoration and reliable transmission are achieved using the full 2-connectivity restoration algorithm (F2 CRA) and the partial 3-connectivity restoration algorithm (P3 CRA) which were proposed by authors in [16]. Meanwhile, [PERSON] et al. in [35] proposed the grade diffusion method paired with the genetic algorithm that is used to develop fault tolerance-based fault node recovery algorithm. A proactive fault tolerance method was proposed in [36], to prevent faults from affecting the network. Their method detects the fault after the network starts running. However, this method was not evaluated for the reliability requirements, such as packed delivery rate and network lifetime.
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 2 Comparative Analysis of Transient Responses Based on Different Damping Models
### Comparative Analysis of the Transient Response  
The relationship between loss factor and damping ratio is [28; 29]  
\[\eta=2\xi \tag{17}\]  
In order to compare the transient responses of the viscous damped system and hysteretic damped system, the attenuation coefficient ratio of two damping models is  
\[\delta_{\rm z}=\frac{\alpha_{\rm v}}{\alpha_{\rm h}}=\frac{\xi}{\frac{\sqrt{2} }{2}\sqrt{\sqrt{1+4\xi^{2}}-1}} \tag{18}\]  
Equation (18) shows that the attenuation coefficient of the viscous damping model is greater than that of the hysteretic damping model. Based on Equation (18), the attenuation coefficient ratios of the two damping models are shown in Figure 1. With the increase in damping ratio, the attenuation coefficient ratio increases gradually. When the relative error of the attenuation coefficients for the two damping models is less than 10%, namely, the attenuation coefficient ratio is less than 1.1000, the damping ratio is less than 0.5041. The threshold of the damping ratio was selected as 0.5041 based on the attenuation coefficient ratio.  
In order to compare with the transient responses of the viscous damped system and hysteretic damped system, the damped natural frequency ratio of two damping models is  
\[\delta_{\rm\omega}=\frac{\alpha_{\rm v}}{\alpha_{\rm h}}=\frac{\sqrt{1-\xi^{2 }}}{\frac{\sqrt{2}}{2}\sqrt{\sqrt{1+4\xi^{2}}+1}} \tag{19}\]  
Equation (19) shows that the damped natural frequency of the viscous damping model is less than the one of the hysteretic damping model. Based on Equation (19), the damped natural frequency ratios of the two damping models are shown in Figure 2. With the  
Figure 1: The change curve of attenuation coefficient ratios.  
increase in damping ratio, the damped natural frequency ratio decreases gradually. When the relative error of damped natural frequencies for the two damping models is less than 10%, namely, the attenuation coefficient ratio is greater than 0.9000, the damping ratio is less than 0.3308. The threshold of damping ratio was selected as 0.3308 based on the damped natural frequency ratio.  
The attenuation coefficient ratio and the damped natural frequency ratio are important indexes of comparative analysis for the transient responses based on viscous damping model and hysteretic damping model. The threshold of the damping ratio was selected as the smaller value between 0.5041 and 0.3308. When the damping ratio is less than 0.3308, the transient responses based on the two damping models are approximately equal.
## 5 Experimental and Analysis  
### Experimental Settings  
The ROV is measured to have a mass of 26 kg; moment of inertia \(I_{z}=1.04\) kg: m\({}^{2}\). The buoyancy and gravity counterweights are balanced. Meanwhile, the overall structure of the ROV designed in this paper is symmetrical front-to-back, up-and-down, and left-to-right, with the center of gravity coordinates as the origin of the coordinate axis.  
By the above analysis and calculation, we can obtain the rigid-body matrix:  
\[M_{RB}=\begin{bmatrix}26&0&0&0\\ 0&26&0&0\\ 0&0&26&0\\ 0&0&0&1.04\end{bmatrix}, \tag{46}\]  
The added mass matrix:  
\[M_{A}=\begin{bmatrix}-24.56&0&0&0\\ 0&-34.27&0&0\\ 0&0&-39.19&0\\ 0&0&0&-2.01\end{bmatrix}, \tag{47}\]  
The linear hydrodynamic damping matrix:  
\[D_{L}=\begin{bmatrix}-30.57&0&0&0\\ 0&-42.21&0&0\\ 0&0&-89.10&0\\ 0&0&0&-0.028\end{bmatrix}, \tag{48}\]  
The nonlinear hydrodynamic damping matrix:  
\[D_{NL}(V)=\begin{bmatrix}-97.43&0&0&0\\ 0&-79.39&0&0\\ 0&0&-117.40&0\\ 0&0&0&-1.25\end{bmatrix}, \tag{49}\]  
The evaluation index adopted in this study is the root mean square error (RMSE), which represents the square root of the ratio of the squared difference between the predicted and true values to the number of observations \(N\). The RMSE value serves as a measure of the model's performance, with smaller values indicating more accurate prediction results. The calculation formula for RMSE is presented below:  
\[RMSE=\sqrt[2]{\frac{1}{N}\sum_{i=1}^{N}(\hat{y}-y)^{2}}, \tag{50}\]  
where \(N\) is the total number of samples, \(\hat{y}\) is the predicted attitude of the ROV, and \(y\) is the ROV's actual attitude.  
Due to the complexity of simulating the real state of the anchor chain underwater, it is necessary to approximate the anchor chain as a suspension line. The suspension line can be described by the general equation  
\[y=acosh(\frac{x}{a}-1), \tag{51}\]  
where \(a\) is the distance from the vertex of the curve to the horizontal coordinate.  
The motion state of the ROV is influenced by varying sea states, which are presented in Table 2[29] below. The strength of ROV interference increases as the sea state degree increases. When the sea state degree is 0, the impact of waves and sea wind can be mostly disregarded, and only the influence of ocean currents needs to be considered. Therefore, a Gaussian noise model can be assumed during this time. Conversely, when the sea state degrees are 1-3, the impact of waves, sea wind, and ocean currents must be taken into account, and a non-Gaussian noise model should be used.  
State vector \(\hat{x}=0\), covariance matrix \(p_{0}=diag[0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3]\), \(T=200\), particles \(N=300\), anchor chain length \(a=60.845\). Our proposed algorithm is subjected to comparison with CKF, CPF, and PF.
## 2 Materials and Methods
### Volatile Compounds Analysis by GC-MS  
GC-MS analysis was performed using a Shimadzu GC-2010 plus gas chromatograph interfaced with a QP-2010 Mass Selective Detector (ionization voltage 70 eV), equipped with a nonpolar DB-5 MS capillary column (30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m), using helium as a carrier gas (1.0 mL\(\cdot\)min\({}^{-1}\)). The oven temperature was programmed from 50 \({}^{\circ}\)C to 260 \({}^{\circ}\)C, at 7 \({}^{\circ}\)C/min, then isothermal at 260 \({}^{\circ}\)C for 5 min, using H\({}_{2}\) as the carrier gas (1.0 mL\(\cdot\)min\({}^{-1}\)). The injector and detector temperatures were 250 \({}^{\circ}\)C. The injection volume was 1.0 \(\upmu\)L (2 mg sample/mL CH\({}_{2}\)Cl\({}_{2}\)) in splitless mode. Linear velocity (\(\upmu\)) was 14 cm/s. MS interface temperature: 280 \({}^{\circ}\)C; mass range: 40-700 Daltons; scan speed: 150 u s\({}^{-1}\); interval: 0.50 s (2 Hz). The volatile constituents were identified by comparing their retention indices and mass spectra with those reported in the literature or presented in the Wiley data system library of the GC-MS equipment.
## 4 Case Study
### Establishment Communication between Grid Elements  
Subsequently, the communication configuration of each of the elements of the microgrid must be carried out with Home Assistant. There are different communication protocols that allow the transmission of information when all the devices are connected to the same data grid, or to other devices in local mode, and these in turn are connected to the same grid.  
Among the most common communication protocols, Modbus stands out from the rest. With it, we can control a network of devices (in our case, generation, storage, and consumption systems) and communicate between them with a control system (Raspberry Pi 4 with Home Assistant). It is the standard communication protocol in the sector as it is robust, easy to use, free and, above all, reliable.  
Figure 5: Case of study microgrid. Source: prepared by authors.  
To communicate between each element (photovoltaic panels, wind turbine, Pb-acid batteries, and loads) with Home Assistant, and monitoring and recording required information, we have to proceed as seen in step 3, Section 2.  
For the non-dispatchable generation elements, such as the wind turbine, photovoltaic inverter, and loads (PQube grid analyzer), only the instantaneous power will be read, as it is not possible to send them any setpoint values. On the other hand, for the controllable generation elements and storage systems (Pb-acid batteries), all the records will be read, with which a SCADA will be developed to control their operation via the control panel in which they will be integrated with all the elements of the microgrid.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ2: What Types of Malicious Activity and Which Scenarios Involve IoT Botnets?
#### 5.2.4 Scanning Attacks  
In this type of attack, the cybercriminal illegally scans the IoT devices in a network to gather information about them prior to launching sophisticated attacks. For example, they send packets by bots to find out whether a specific port is open on the device and return the information back to the botmaster to take the advantage of this information to exploit this device. In the literature, some studies analyzed scanning techniques to identify coordinated IoT campaigns that sought open ports that could be exploited for amplification attacks [90]. Others checked for updates and applied patches for device software to be proactive, as this prevents vulnerabilities from being discovered by attackers [91]. Finally, [92] proposed a solution that detects port scanning behavior. Some of the selected studies focused on bots scanning for vulnerable devices [52], whereas the article [74] analyzed the Dark Web to detect the scanning activities of IoT botnets. As Figure 8 explained, 14% of studies concentrated on this type of attack--five studies, as Table 10 displays. Some of the selected studies that focused on this attack are [52; 74; 78].
## 4 Discussion
### Total Organic Carbon and Organic Matter Variations  
The similarity in the temporal trend in TOC by the moisture levels can be attributed to the initial mass and C/N ratio of the piles, which were proportionately similar across the treatments (results shown). The moderate losses in TOC (21.6%) were an indication that the compost mixtures sufficiently decomposed, despite the variations in the moisture content. According to [PERSON] et al. [51] and [PERSON] et al. [52], piles that undergo incomplete degradation processes express little or no losses in TOC. The final TOC was lower in the 55% and 65% moisture contents but higher in the 45% moisture. Ideally, water is a universal solvent that promotes the hydrolysis of organic materials and other compounds during composting [40]. Water regulates pile temperatures and ensures the digestion of unstable organic materials [33]. However, an insufficient moisture content leads to dehydration of the heap, limiting microbial activities on organic materials. Meanwhile, a sufficient water content in the compost pile ensures that organic materials become softer and easier formicroorganisms to digest. These processes may explain the observed increase in TOC by the MC at 45% and the decrease in TOC by the MC at 65%.  
The decrease in the organic matter (OM) content with time is attributed to the emission of carbon dioxide and methane (results shown) in the early stages of composting [53]. Microorganisms use organic matter as a carbon source for their metabolic and physiological activities, which in turn reduces the organic matter content [54]. An increase in the organic matter content with 65% moisture implies that the treatment improved humification and mineralization of the compost piles [51]. Humification and mineralization are microbially mediated processes that influence the biodegradation of OM [55]. The rise in temperature for the 65% moisture treatment shows high microbial activity and explains the observed increase in OM degradation. Studies have shown 55-70% moisture contents as effective for promoting high temperatures in the process of composting waste foods [56]. Nonetheless, a moisture content at low levels inhibits microbial activities and hinders nutrient mobility, leading to reduced temperatures during composting [56]. Therefore, the observed decline in temperature by the MC at 45% also explains the high loss in OM by the treatment.
# Dual-Energy CT Applications in Urological Diseases
## 3. DECT Benefits
### Iodine Maps  
Iodine is used as a contrast agent in CT scans to enhance the visibility of blood vessels, tumors, and other lesions. The iodine map provides information about the distribution and concentration of iodine in tissues (Figure 3) and helps assess the presence and extent of iodine uptake in tissues, providing valuable information for the characterization of lesions, evaluation of tumor vascularity, and monitoring of treatment response [20]. The iodine map images are typically displayed in color, with higher iodine concentrations appearing as brighter colors, allowing for easy visual identification of iodine-rich areas.  
Iodine maps can also enhance the visualization of extraluminal iodine components, therefore helping the detection of urinary leakage in trauma patients [21] or as a result of iatrogenic damage [22].
# Designing Smart Electromagnetic Environments for Next-Generation Wireless Communications
## 4 Conclusions  
Within the framework of the emerging \"smart EM environments\" paradigm for enabling next generation wireless communications systems, an optimization-based strategy for synthesizing a desired field distribution over a region of interest, where the end-user terminals are located, has been here introduced. Thanks to the complex scattering phenomena generated from the EM interactions of the primary source and the obstacles present in the scenario, supposed known and opportunistic exploited as passive metastructures, the configuration of the excitation weights controlling the field transmitted from a BTS has been properly synthesized by solving an optimization problem with the PSO to minimize a cost function quantifying the mismatch between the actual field generated in the region of interest and a target field distribution. The reported illustrative result has pointed out that it is possible to generate complex field distributions in the target region without recurring to the deployment of wider (i.e., more complex and expensive) free-space BTSs but just benefiting from the interactions between the EM field generated by the actual/standard BTS and the scattering objects.  
Future research activities, beyond the scope of this paper, will be aimed at extending the proposed methodology to consider scenarios that are not deterministically known, for example by taking into account statistical propagation models, with the inclusion of moving scatterers. Moreover, future advances will be devoted to improving the efficiency of the method in terms of field computation, now representing one of the key computational issues in the optimization stage and to consider as target the power values or the EM field strength since telecommunications' operators usually want to obtain a power level at the receivers higher than a threshold rather than a complex EM field distribution.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Ministry of Education of China within the [PERSON] Visiting Professor chair and by the Italian Ministry of Education, University, and Research within the Program PRIN 2017 (CUP: E64I19002530001) for the Project CYBER-PHYSICAL ELECTROMAGNETIC VISION: Context-Aware Electromagnetic Sensing and Smart Reaction (EMvisioning) (Grant no. 2017 HZJXSZ) and for the Project CLOAKING METASURFACES FOR A NEW GENERATION OF INTELLIGENT ANTENNA SYSTEMS (MANTLES) (Grant no. 2017 BHFZKH).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Fractional Poynting-Thomson Model  
The next model to be considered is fractional Poynting-Thomson one. Here \(\zeta\) reads as:  
\[\zeta^{2}=\frac{\rho\omega^{2}}{E}\Big{[}\frac{1}{(E_{0}/E)(i\omega\tau)^{ \gamma}}+\frac{1}{\big{(}(i\omega\tau)^{\alpha}+(i\omega\tau)^{\beta}\big{)}} \Big{]}, \tag{31}\]  
or alternatively in nondimensional form:  
\[\tilde{\omega}=\tilde{\omega}^{2}\Big{(}\frac{1}{\psi(i\tilde{\omega})^{\gamma }}+\frac{1}{(i\tilde{\omega})^{a}+(i\tilde{\omega})^{\beta}}\Big{)} \tag{32}\]  
Here we impose two additional conditions for fractional orders \(\alpha\), \(\beta\), and \(\gamma\): \(\alpha-\gamma\geq 0\) and \(\beta-\gamma\geq 0\). As previously, we first examine the behavior of centerline velocity (Figure 9). Fixing \(\alpha=\gamma=0.2\), \(\psi=1\), we vary \(\tilde{a}\) and \(\beta\). The trends are somewhat repeatable compared to fractional Maxwell model (Figure 3a,d,g) and demonstrate elastic behavior for the entire range of \(\beta\). Next, we fixed \(\alpha=\beta=0.2\), \(\psi=1\) and varied \(\tilde{a}\) along with fractional order \(\gamma\). The corresponding plotsare presented in Figure 9b,e,h. Here aperiodic oscillations for lower values of fractional order \(\gamma\), oscillatory and monotonically increasing velocity profiles with increasing \(\gamma\) are observed. Finally, we set \(\alpha=\beta=\gamma=0.5\) and varied \(\tilde{a}\) and \(\psi\) (Figure 9c,f,i). Here, resonant behavior is observed even for lower values of \(\tilde{a}\) (Figure 9c). Moreover, for \(\psi<1\) aperiodic oscillations of velocity are observed followed by it becoming independent from \(\tilde{\omega}\). The trend, however, changes to resonant for \(\psi>1\). Overall, the velocity oscillations are damped slower with \(\psi\) increasing.  
The shear stress dynamics at the wall are shown in Figure 10. As previously, we first fixed \(\alpha=\gamma=0.2\), \(\psi=1\) and varied \(\tilde{a}\) and \(\beta\) (Figure 10a,d,g). After major peak, shear stress drops dramatically with oscillations being damped for the entire range of \(\beta\), except for Figure 10a, where it increases monotonically. The value of the major peak itself oscillates with increasing \(\beta\). Next, as shown in Figure 10b,e,h, system parameters were set at \(\alpha=\beta=0.2\), \(\psi=1\) with varying \(\tilde{a}\) and \(\gamma\). Monotonically increasing trend for lower values of \(\tilde{a}\) changes to aperiodic oscillations and resonant for higher ones. Moreover, the value of the major peak decreases monotonically with increasing fractional order \(\gamma\). Finally, we set \(\alpha=\beta=\gamma=0.5\) and varied \(\tilde{a}\) and \(\psi\). The corresponding plots are shown in Figure 10c,f,i. As can be seen from it, resonant behavior is present for all values of \(\tilde{a}\). However, it switches to monotonically increasing with increasing \(\psi\). Moreover, the switch happens at higher values of \(\psi\) as \(\tilde{a}\) increases and is absent for \(\tilde{a}=0.1\) (Figure 10i).
# Intelligent Interference Management in UAV-Based HetNets +
## 3 System Model
### Performance Metrics  
The objective of our study is to design algorithms to calculate the best FeICIC and eICIC parameters individually for all MBSs and UABSs as well as the positions of the UABSs to, thus, maximize our performance metric. We evaluate two performance metrics: the mean SE and median SE. The algorithm should find the best state, \(\mathbf{S}^{\prime}\), out of all possible states \(\mathbf{S}\) such that:  
\[\mathbf{S}^{\prime}=\underset{\mathbf{S}\mathbf{maxSE}}{\mathbf{arg}}\mathbf{ C}(\mathbf{S}), \tag{12}\]  
where \(C(.)\) denotes the function that calculates the mean or median SE over the whole network area for a given state \(\mathbf{S}=[\mathbf{X}_{\text{uabs}},\mathbf{S}_{\text{mbs}}^{\text{ICIC}}, \mathbf{S}_{\text{uabs}}^{\text{ICIC}})\). First, we calculate the SE for each UE as per Equations (1)-(4). As defined previously, \(\mathbf{X}_{\text{uabs}}\) is the matrix representing the location of the \(N_{\text{uabs}}\) UABSs in three dimensions, \(\mathbf{S}_{\text{mbs}}^{\text{ICIC}}=[\boldsymbol{\alpha},\boldsymbol{ \beta},\boldsymbol{\rho}]\in\mathbb{R}^{N_{\text{uabs}}\times 2}\) is a matrix that captures individual ICIC parameters for each MBS, and \(\mathbf{S}_{\text{uabs}}^{\text{ICIC}}=[\boldsymbol{\tau},\boldsymbol{\rho }^{\prime}]\in\mathbb{R}^{N_{\text{uabs}}\times 2}\) is a matrix that captures the individual ICIC parameters for each UABS. The vectors \(\boldsymbol{\alpha}=[\alpha_{1},\dots,\alpha_{N_{\text{mbs}}}]^{T}\) and \(\boldsymbol{\rho}=[\rho_{1},\dots,\rho_{N_{\text{mbs}}}]^{T}\) capture the power reduction factors and scheduling thresholds, respectively, of each MBS. On the other hand, for each UABS, \(\boldsymbol{\tau}=[\tau_{1},\dots,\tau_{N_{\text{uabs}}}]^{T}\) and \(\boldsymbol{\rho}^{\prime}=[\rho_{1}^{\prime},\dots,\rho_{N_{\text{uabs}}}^{T} ]^{T}\) denote the CRE and scheduling threshold, respectively.