esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### BETHY Soil Hydrology Scheme--Modified VIC 1L  
To simulate surface soil moisture within BETHY in a way that can be compared to remotely sensed surface soil moisture, a modified version of the Variable Infiltration Capacity 1-layer (VIC-1 L) soil model by [38] was implemented in BETHY, for more details see [19]. The VIC model has the distinct property of representing within-grid cell spatial variability of infiltration capacity of the soil in a statistical fashion, something that is particularly suitable for simulations at the scale of the SMOS footprint of several hundred km\({}^{2}\)[39]. Therefore, a thin layer was added from which all soil evaporation occurs. The VIC-1 L evapotranspiration was also slightly modified, because in its original form, vegetation effects were not accounted for. This was needed to be consistent with transpiration and canopy throughfall of BETHY, and changed the base flow equation to prevent the soil from draining below field capacity. This new scheme is thus a 2-layer hydrology model with a surface and root-zone layer and the same 1-day time step as BETHY's original bucket model.  
Soil moisture at saturation and at wilting point were computed from soil texture, total soil depth, and -for the root-zone layer--rooting depth. The values for rooting depth have been adapted from [12], taking account of the higher rooting depth of tropical trees and assigning a slightly higher rooting depth for temperate trees and shrubs and a lower one for grasses and tundra. A global soil depth and texture map and parameterization of soil porosity (saturation water content) and water potential was adapted from [40]:  
\[\theta(\psi)=n(\psi/\psi_{s})^{-1/b} \tag{3}\]  
where \(\theta\) is the soil water content, \(n\) is the soil porosity, \(\psi\) is the soil water potential, \(\psi_{s}\) is the soil water potential at saturation, and b is the slope parameter of the retention curve. Details as well as validation of the modified VIC-1 L model are given in [19]. For the simulations described in this study, we used a physical depth of the surface soil layer of 40 mm to be comparable to the SMOS observations. \(s_{n}\) and \(s_{b}\) are scaling parameters for \(n\) and \(b\) included in the optimization. Additionally, soil hydrological parameters were set as fixed values derived from the VIC_3L model [41].
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 2 Materials and Methods
### Unit of AC Current Source
#### 2.1.2 Voltage to Current (_V/l_) Converter  
As explained above, in this project we use a current source (not a voltage source) to be injected into biological samples. Therefore, the voltage that has been generated by the voltage generator must be converted to an equivalent current. For this purpose, we use a voltage to current (_V/l_) converter. There are several types of _V/l_ converter, one of the most popular is Howland current source (HCS). The HCS is a voltage controlled current source (VCCS) with loads connected to the ground. The HCS has advantages in the stability, grounded mode, and simple application method. In this project HCS with buffered feedback path is used, as given in Figure 3. The use of buffered feedback path can provide advantages in high impedance measurements.  
Considering the op-amp role and Kirchhoff law, the output voltage at the point of the circuit in Figure 3 can be obtained  
\[V_{-A1}=V_{+A1};V_{\mathrm{C}}=V_{B} \tag{1}\]  
\[\left(\frac{R_{3}}{R_{3}+R_{4}}\right)V_{A}=\left(\frac{R_{1}}{R_{1}+R_{2}} \right)V_{\mathrm{G}}+\left(\frac{R_{2}}{R_{1}+R_{2}}\right)V_{\mathrm{C}} \tag{2}\]  
\[V_{A}=\left(\left(\frac{R_{1}}{R_{1}+R_{2}}\right)V_{\mathrm{G}}+\left(\frac{R _{2}}{R_{1}+R_{2}}\right)V_{\mathrm{C}}\right)\left(\frac{R_{3}+R_{4}}{R_{3}}\right) \tag{3}\]  
For \(R_{1}=R_{2}=R_{3}=R_{4}=R\), then  
\[V_{A}=\left(\left(\frac{R}{2R}\right)V_{\mathrm{G}}+\left(\frac{R}{2R}\right) V_{\mathrm{B}}\right)\left(\frac{2R}{R}\right)=V_{\mathrm{G}}+V_{\mathrm{B}} \tag{4}\]  
\[I_{\mathrm{S}}=\frac{V_{\mathrm{A}}-V_{\mathrm{B}}}{R_{\mathrm{S}}}=\frac{V_{ \mathrm{G}}+V_{\mathrm{B}}-V_{\mathrm{B}}}{R_{\mathrm{S}}}=\frac{V_{\mathrm{G }}}{R_{\mathrm{S}}} \tag{5}\]  
Current injected to the material load \(\mathbf{Z}_{\mathrm{L}}\) is \(\mathbf{I}_{\mathrm{Z}}\). According to Kirchhoff's Current Law (KCL),  
\[I_{\mathrm{Z}}=I_{\mathrm{S}}-I_{A2} \tag{6}\]  
Figure 3: Howland current source with buffered feedback.  
\(I_{\rm A2}\) is current entering the A2 op-amp, and for high input impedance op-amp, \(I_{\rm A2}\approx 0\), so that  
\[I_{\rm Z}=I_{\rm S}=\frac{V_{\rm G}}{R_{\rm S}} \tag{7}\]  
Based on Equation (7), and with \(V_{\rm G}\) set constant at 0.5 volts, then the current value is only determined by \(R_{\rm S}\). By selecting \(R_{\rm S}\) of 500 k\(\Omega\), 50 k\(\Omega\), 5 k\(\Omega\), and 500 \(\Omega\), an \(I_{\rm Z}\) current value of 1 mA, 10 mA, and 1 mA will be obtained, respectively. In addition, in this research we use LF357 for op-amp A1 and LF351 for op-amp A2. The LF357 has GBW 20 MHz and input impedance in order T\(\Omega\), while the LF351 has GBW of 4 MHz and input impedance in order of T\(\Omega\). Both are low-cost JFET input op-amp. In addition, to provide current selector is used an analog multiplexer CD4051, and it is controlled by the microcontroller.
# Scale-Free Features in Collective Robot Foraging
## 2 Methods
### Robot Behavior
#### 2.1.1 State Switching Probabilities  
Following [14], there is a minimum duration \(\theta\) for the robot to stay in a certain state. The purpose of having such a threshold is to ensure that robots can perform the sub-tasks associated with this state for a certain amount of time so that necessary dynamics can take place. For instance, a minimum exploring time \(\theta_{e}\) needs to be at least as long as it takes for a robot to reach the most remote items (taking into account the constant linear speed of that robot) [16].  
With this in mind, let us formulate the individual response to social and environmental cues in terms of _switching probabilities_. We denote \(\{i,i_{r}\}\in\left\{\mathbb{R}_{0}^{+},\mathbb{R}_{0}^{+}\right\}\) and \(\{s_{e},s_{r}\}\in\left\{\mathbb{R}_{0}^{+},\mathbb{R}_{0}^{+}\right\}\) as the robot's internal (\(i\)) and social (\(s\)) cues to switch to exploring (\(e\)) or resting (\(r\)) state, respectively. The probability of a robot to switch from the resting state to the exploring state is denoted by \(p_{r\to e}\), whereas the probability to switch from the exploring state to the resting state is denoted by \(p_{e\to r}\).  
The probabilities are updated iteratively at every simulation time step in a discrete manner as in the following:  
\[p_{r\to e}(t+1) =p_{r\to e}(t)+\delta_{\eta}(t)s_{e}+\delta_{\phi}(t)i_{e} \tag{1}\] \[p_{e\to r}(t+1) =p_{e\to r}(t)-\delta_{\eta}(t)s_{r}-\delta_{\phi}(t)i_{r}, \tag{2}\]  
where \(\delta_{\eta}(t)\) is the number of'success' minus 'failure' messages received by the robot from its neighbors at every time step spent in the _resting_ state. Additionally, the robot's own experience is characterized using \(\delta_{\phi}(t)\). This is defined for every exploration attempt as follows:  
\[\delta_{\phi}(t)=\begin{cases}+1,&\text{at the time instance of finding an item if }t_{se}<t\leq t_{se}+\theta_{e}\\ 0,&\text{at all other time instances if }t_{se}<t\leq t_{se}+\theta_{e}\\ -1,&\text{if }t>t_{se}+\theta_{e}\text{ and the robot is still exploring}\end{cases} \tag{3}\]with \(t_{se}\) as the time at which the robot started its current exploration. Moreover, \(\delta_{\phi}(t)=0\) while the robot is resting. In case \(p_{e\to r}<0\) it is truncated to \(p_{e\to r}=0\) and when \(p_{e\to r}>1\) it is truncated to \(p_{e\to r}=1\); same holds for \(p_{r\to e}\). Please note that there is a strict difference between \(\delta_{\eta}(t)\) and \(\delta_{\phi}(t)\): \(\delta_{\eta}(t)\) may be non-zero only when the robot is resting inside the nest because it is computed based on the information broadcast by the neighbors which can only be received in the nest (i.e., when the robot is in the resting state). Whereas, \(\delta_{\phi}(t)\) may be non-zero only when the robot is exploring--it is computed based on the robot's own foraging experience. Table 1 lists the parameters relevant for the computation of the switching probabilities.
## 1 Introduction
The persistence has been thoroughly analyzed among medically-relevant bacteria, but its occurrence in phytopathogens is poorly understood, with only a few bacterial species studied to date. For example, copper or chlorine exposure results in unculturable cells for some phytopathogens such as _Erwinia amylovona_[21; 22; 23], _Xylella fastidiosa_[24; 25] and _Xanthomonas campestris_[26]. The occurrence of these unculturable cells (so-called VBNCs) in phytopathogens was first described in _Ralstonia solanacearum_[27], and the cause of these phenotypic variants is low temperatures [28] and starvation [29]. Curiously, oxidative stress is pivotal to the unculturability for both _Erwinia amylovora_[30] and _Ralstonia solanacearum_[31] since catalase supplementation changes the culturability profiles. Copper and zinc are potent inducers of oxidative stress in living cells [32; 33; 34; 35; 36]; hence, they are likely linked to persistence and unculturable cells in agriculture. These metals can induce oxidative stress by the production of hydroxyl radicals by the Haber-Weis and Fenton reactions [37; 38]. This imbalanced oxidative environment can irreversibly damage lipids, nucleic acids and proteins, which can be fatal to live cells [37; 38]. Metals cannot be synthesized or degraded, and their concentration needs to be strictly balanced: its excess can prevent bacterial growth, and complex metalloregulatory pathways have evolved [39]. The current knowledge on persistence in phytopathogens has been reviewed [40], highlighting that, despite persistence occurring in the field, agricultural management practices largely neglect persister cells. This can result in the inadvertent production of these cells, which are more tolerant to regular control measures. In addition, since persister cells may not be killed by the application of many chemicals, special approaches are needed for their eradication. Here we show that _X. citri_ cells become persistent in specific situations, and we suggest management practices that may lower the impact of their occurrence in the field.
## 5 LMS Channel Models
### Single-State Channel Model
Moreover, the proposed model is suitable for non-geostationary satellite channels such as LEO or MEO orbits. The mathematical formulation for the proposed model is provided, also based on other authors' work [32], in which the channel model for both cellular and satellite communications is described for the statistical characterization under different propagation conditions. Another important work is represented in [34], where the authors take into account outages due to obstruction of the LoS path caused by interference from multipath radio waves for providing a statistical model suitable for mobile communications more generally, with respect to the well-known previous statistical models.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design
### Transmission Waveform Design  
In this part, the components of the proposed transmission waveform are introduced, including DFT spreading, FTN signaling and IOTA filter design, respectively.
## 1 Introduction
Then, for any matrix \(E\in\mathcal{R}^{m\times m}\), there holds_  
\[\|dg(E)+D\|_{  
The fulfillment of condition (2) depends not only on the characteristics of \(A\) (like the condition number) but also on \(x_{0},E_{0}\) and \(\theta\). Note also that usually, the sequence \(\{E_{n}\}\) is bounded, and if \(F\) is also bounded on \(\mathcal{R}^{m}\) then the condition (2) is satisfied; in Section 5 an example of mapping is given which is bounded on \(\mathcal{R}^{m}\) and \(\|D(x_{n})^{-1}\|\|E_{n}\|<1\).  
**Theorem 1**.: _Let \(A\in\mathcal{R}^{m\times m}\) be a nonsingular matrix, \(D=dg(A)\), and let \(\theta\) be a real number, \(0<\theta<2\). Suppose that \(D\) is invertible and that \(a\|D^{-1}\|<1\), where \(a\) is defined in Lemma 1. Suppose also that condition (2) is satisfied and that \(E_{0}+D\) is invertible. Then the parallel variant of Broyden method (Algorithm 3) converges to the solution of the equation \(Ax-b=0\)._  
Proof.: Since \(\|E_{n}\|\|D^{-1}\|\leq\|D^{-1}\|a<1\), from perturbation Lemma, it results that \((E_{n}+D)^{-1}\) exists for all \(n\) and the sequence \(\{x_{n}\}\) is well defined by Algorithm 3. By taking \(M=E_{k}+D,\ s=s_{k}\) and applying Lemmas 1 and 2, we obtain  
\[\|E_{k+1}+D\|_{F}^{2}\leq\|E_{k}+D\|_{F}^{2}-\theta(2-\theta)\frac{\|(E_{k}+D )s_{k}\|^{2}}{\|s_{k}\|^{2}}.\]  
By summing on \(k\), \(k=0,1,...,n\), we have  
\[0\leq\|E_{n+1}+D\|_{F}^{2}\leq\|E_{0}+D\|_{F}^{2}-\theta(2-\theta)\sum_{k=0}^ {\infty}\frac{\|(E_{k}+D)s_{k}\|^{2}}{\|s_{k}\|^{2}}.\]  
This implies that  
\[\frac{\|(E_{n}+D)s_{n}\|}{\|s_{n}\|}\to 0,\ n\rightarrow\infty.\]  
Now, because \((E_{n}+D)s_{n}=-F(x_{n})\), we have  
\[\frac{\|F(x_{n})\|}{\beta}\leq\frac{\|F(x_{n})\|}{\|s_{n}\|}=\frac{\|(E_{n}+D )s_{n}\|}{\|s_{n}\|}\to 0,\]  
and  
\[\|A^{-1}\|^{-1}\|x_{n}-x^{*}\|\leq\|A(x_{n}-x^{*})\|=\|F(x_{n})\|\to 0.\]
## 3 System Overview
### Network Structure Overview  
In our system, there are SPs, SRs, and a neutral certificate authority (CA). The neutral CA reviews the applicants who want to join the network and sends the identity certificate after validation. Based on the issued identity certificate, users' footprints can be tracked by our system if they do something illegal. Moreover, the service-exchange must be conducted face-to-face between SRs and SPs since members' safety is the top priority. CA can also revoke the identity certificate for the member who had menaced other members.  
After the enrollment, valid users get permission to invoke chaincodes and query the on-chain data. There are two different channels in the network of our TimeBank system (cf. Figure 2):  
* Service Channel (SC): Processes in SC managing all service inquiries and handling the matching process. Users evoke SC for posting their queries then match their supply-and-demand attributes with the other members in this channel.
* Token Channel (TC): Processes in TC managing all users' accounts and balances. If a service-exchange is accomplished, evoking TC confirms whether the corresponding service status on SC is completed, then approves sending the token to SPs from associated SRs.  
Figure 1: The processes of posting SP’s and SR’s service inquiries on the blockchain.  
Figure 2: The underlying architecture of the proposed TimeBank system is founded based on Hyperledger Fabric’s multi-channel network.  
In summary, the proposed system is designed based on Fabric's multiple-channel architecture to increase the system's scalability. Because TimeBanks are community-based institutions, the circulatory nature of time credits is also limited. Therefore, multiple TimeBanks are allowed to join in the proposed system. All TimeBanks in the network share the same TC, but each of them has its own SC. Each SC manages its community's services, and the services cannot match each other if they belong to different SCs.
# Development of Fire Consequence Prediction Model
## 2 Research Method
### Research Subject  
LNG bunkering is the process of supplying LNG as a fuel to LNG-fueled ships, and research and demand for LNG-fueled ships are increasing. The subject of this study is a fuel gas supply (FGS) system that supplies LNG from an LNG bunker ship to an LNG-fueled ship. Figure 3 shows the piping and instrumentation drawing (P&ID) of the LNG bunkering system. The fueling process consists of storing LNG in the fuel tank of an LNG-fueled ship along the fueling line from the storage tank and the recovery of vapor return gas to the LNG storage tank through the return line to match the pressures of the two tanks. The system includes fire detectors, gas detectors, pressure transmitters, a monitoring system as sensors, and a ball valve and gate valve as shut-off valves. In addition, a ball valve is placed in the LNG fueling line for LNG control, and a gate valve is placed in the gas return line for gas control.  
The factors that significantly affect the analysis results when an accident is analyzed in LNG bunkering work include the materials inside the piping, hole size, leak duration, and leakage volume. These factors can be grouped into those for identifying the hole size and those for identifying the fire consequence. The factors for identifying the hole size include the LNG mass flow, velocity, and pressure, which can be obtained by monitoring during LNG bunkering. The factors for identifying the fire consequence include the hole size, leak duration, and leakage volume.  
Figure 2: Research method.  
In the case of the LNG FGS system, holes can be created because of pressure owing to the presence of weak parts, such as the fittings of the pump or pipes during LNG fueling. The material that is leaked when a hole is created in the pipe is LNG, which is composed of 0.8 methane, 0.1 ethane, 0.05 propane, and 0.05 butane. Because the bunkering work pressure is 10 bar, there is a high risk of fire when the gas leaks. Therefore, appropriate measures for identifying and responding to fire risks during LNG bunkering should be developed.
## 1 Introduction
### Objectives and Organization of the Review  
This review seeks to address this gap by delivering an in-depth analysis of the present state of DT applications in transportation infrastructure. It examines the conceptual framework, key components, challenges, and future directions of DT technology, providing valuable insights for researchers, urban planners, engineers, and policymakers. A full lifecycle perspective is adopted to ensure a thorough understanding of both the broad benefits and limitations associated with DTs in transportation systems.  
The rest of this paper is organized as follows: Section 2 introduces the literature review method (SLR), search strategy, and eligibility criteria adopted in this study. Section 3 provides a conceptual overview of DTs in transportation infrastructure, defining key principles and assessing the current state of these systems. Section 4 introduces a new taxonomy tailored for transportation-related DT systems, detailing their diverse applications. It also explores the core components of DTs, such as data integration, simulation models, 3D visualization, and system architecture. Section 5 discusses how DT technology manages assets throughout their lifecycle. Section 6 reviews current applications of DTs in urban planning, traffic management, and infrastructure maintenance, supported by case studies. Section 7 addresses the main challenges of DTs in transportation infrastructure and highlights future research directions. Section 8 concludes the survey by summarizing key insights and implications for future developments.
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 2 Waveform Mapping Theory  
### Static Calibration of Sensor Waveform  
When the blade tip-timing sensor is used to measure the tip-timing data of the given thickness blade, the output voltage waveform of the blade tip-timing sensor \(U\) mainly depends on the radial distance between the blade tip and the sensor probe \(h\) and the angle between the blade center line and the sensor center line \(\alpha\) (i.e., the coincidence degree between the blade tip and the sensor probe). The output voltage can be expressed as  
\[U=U(h,\alpha). \tag{1}\]  
The sensor waveform is calibrated in the static state. First of all, we are given a blade tip radial clearance value, then we change the included angle to measure the output voltage value of different angles. Secondly, we are given the next blade tip radial clearance value, then we change the included angle to measure the output voltage value of different included angles, and so on. Finally, in the range of radial clearance measurement and coincidence degree, the output voltage values under different radial clearance angles is obtained. The calibrated output waveform group of the sensor is obtained by data fitting.  
The static calibration system mainly consists of a radial calibration system and circumferential calibration system, as shown in Figure 1. The radial calibration system consists of manually fine-tuning the X-Y displacement platform, the blade tip-timing sensor and its fixing bracket. During the radial calibration, we adjust the Y-direction micrometer head to ensure that the center line of the blade and the center line of the sensor overlap as much as possible. Then, the X-direction micrometer head is adjusted to make the sensor move with the bracket fixed on the displacement platform along the X direction, and then the radial distance between the sensor probe and the blade tip is adjusted. The circumferential calibration system consists of an R-direction manual fine-tuning rotation platform, a fixed bracket, a circumferential adjustment plate and other components. The fixing bracket is connected with the fixing hole on the operating platform. The rotation platform is connected with a fixing bracket by bolts, and a circumferential adjusting plate is fixed on it. During the circumferential calibration, the rotation platform is first set to rough shift, and the rotation platform is rotated to move the blade and sensor into the center position. Then, the rotation platform is adjusted to the fine-tuning gear, the R-direction micrometer head is rotated to drive the circumferential regulating plate to rotate and then the blade is slightly rotated to complete the circumferential calibration.  
Figure 1: Diagram of static calibration system.  
When the blade tip clearance \(h\) is a fixed value, the output voltage of the sensor can be expressed as follows:  
\[U=f(x), \tag{2}\]  
where \(x\) is the circumferential displacement of the blade tip relative to the OPR sensor and \(f\) is the fitting static calibration waveform function.