esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Validation of Adaptive Newborn Distribution  
Adaptive newborn distribution was proposed in this scheme, as newborn targets can occur at random places in the monitoring area at random periods in actual applications. The OSPA and cardinality comparisons of BGGIW-PMBM and GCI-IGGM-PMBM were derived from the adaptive newborn distribution and known newborn distribution, as shown in Figure 9a. The OSPA tended to relative stable values other than sharp change values after some initialized periods for the two filters with adaptive newborn distribution, whereas it changed sharply with known distribution at the intersections of these trajectories, e.g., at 26-34 s, 46-53 s, 68-74 s, and 88-92 s. The OSPA reached similar values for adaptive and known newborn distributions in other periods. The cardinality presented a similar behavior to that of OSPA, as shown in Figure 9b. The actual number of each scheme slowly reached the true cardinality for the adaptive distribution, whereas the actual number matched the true cardinality after sharp changes at the intersection points.  
Figure 8: Energy consumption comparisons based on anchor rate \(\gamma_{0}\) of GCI–IGGM–PMBM with other multitarget tracking schemes with unknown detection probability: (**a**) \(\gamma_{0}=0.05\); (**b**) \(\gamma_{0}=0.1\).  
Figure 9: OSPA and cardinality comparisons of GCI–IGGM–PMBM filter and BGGIW–PMBM filter based on newborn distribution: (**a**) average OSPA; (**b**) cardinality.  
The GOSPA distance, including the LE, ME, and FE, is shown in Figure 10 for unknown newborn and known newborn distributions. There were abrupt changes for LE, ME, and FE for known newborn distributions, as shown in Figure 10, at the trajectory intersection points, e.g., at 26-34 s, 46-53 s, 68-74 s, and 88-92 s, whereas changes were smooth over transition points for adaptive newborn distributions. The GCI-IGGM-PMBM filter presented relative superiority over BGGIW-PMBM at the initialized tracking stage.
## 2 Materials and Methods
### HMI Processing  
Fiji (Imagej 2.0) [14] was used to process raw TIFF images collected in the hypercube stacks. Figure 1 shows a flowchart for the image processing method that extracts the mean single cell spectra in less than 5 min.  
Figure 1: Flowchart of steps used to record Fiji macro for processing hyperspectral microscope images of bacteria.  
The hypercube was imported into Fiji as a virtual stack, and the spectral band resulting in a high cell to background contrast was identified and duplicated as an 8-bit grayscale image for shape analysis. The auto-thresholding option in Fiji was selected, with 16 thresholding algorithms being tested. It was found that [PERSON]'s method gave the optimal separation of cells from the background. Here, [PERSON]'s thresholding method was applied to mask the background, leaving a mask with only pixels representing cells. [PERSON]'s thresholding assumes a Gaussian distribution for image values, where the objective is to maximize the difference between-group variance, in this case, the feature (bacterial cells) and the background [15]. The probabilities of a pixel value falling into one of two groups can be calculated by Equation (1), as follows:  
\[P_{1}(T)=\sum_{i=0}^{T-1}P_{i}P_{2}(T)=\sum_{i=T}^{I_{max}}P_{i} \tag{1}\]  
where \(P_{1}\) and \(P_{2}\) represent cumulative probabilities of the two groups, \(T\) = a threshold that divides the image into pixel set \(S_{1}\) or \(S_{2}\), and \(P_{i}\) = the probability of image value \(i\). After the global thresholding was computed, the Time Series 3.0 plugin [16] was used to apply the masks to the virtual stack, calculating the mean of the pixels in each regions of interest (bacterial cell). Next, Fiji exported two comma-separated value (CSV) files, where one file represented the spectral data and one file represented the shape metrics. The two CSV files were combined into one matrix, where rows were single cells with corresponding shape and spectral data shown as columns. Circularity represents how close a shape is to a perfect circle on a scale of 0 to 1, and was computed by Equation (2), as follows:  
\[Cir=4\pi(\frac{A}{P^{2}}) \tag{2}\]  
where \(Cir\) = circularity, \(A\) = area, and \(P\) = perimeter. Bacterial cells are not always close to a value of 1, as _Salmonella_, _E. coli_, and many others are rod-shaped, in addition to _Campylobacter_, which can take on an S-shape. It was found that extremely low circularity values were correlated with clumps of overlapping cells, and extremely high values were typical of a small number of pixels representing extracellular debris. Thresholding values of 0.35-0.9 were optimal in removing large clumps of cells, as well as extracellular debris. Figure 2 shows an example of the bacterial hypercube and data files.  
Figure 2: Representation of data collection of hyperspectral microscope images of bacterial cells between 450 and 800 nm.
## 7 Example of Using CPM to Determine the Resistance of a Continuous I-Beam
### Welded Section
#### 7.2.3 Comparison of Critical Stress Results  
Table 2 compares the values of critical stresses for supporting \(\sigma_{\sigma,s}\) and span \(\sigma_{\sigma,p}\) sections, determined according to: (1) ABAQUS, (2) CPM, (3) CUFSM, (4) Reference [19], (5) Reference [22], and (6) EC3. It should be emphasised that comparison of stresses determined according to Reference [22] was for reference only, because the formulas derived in Reference [22] apply only to hot-rolled sections, and not to the welded ones.  
From the comparison of the values given in Table 2, it follows that the new calculation model adopted in this work using the critical plate method (CPM) gave very good congruence with the results obtained from the ABAQUS software, which were adopted as a reference. This applied to both the support section (first eigenvalue, compliance approx. 0.99) and the span section (second eigenvalue, congruence approx. 0.985). The calculations obtained from CUFSM also gave very good results in the span section (congruence approx. 0.96), while in the support section, where a large stress gradient occurred, and on which the critical resistance of the beam depends, congruence was around 0.81. A similar situation occurred when comparing the results obtained from the formulas according to Reference [19] (span section about 0.91, support section about 0.77). However, a worse congruence of the results obtained according to Reference [22] (span section about 0.78, support section about 0.66) resulted from the different, in relation to hot-rolled sections, geometrical proportions of the sections considered in this study. The largest differences (span section about 0.53, support section about 0.45) were obtained from calculations made according to EC3. The local buckling half-wavelengths determined in this case were: (1) according to CPM, Equation (9) \(l_{\sigma}\) = 307 mm; (2) according to CUFSM, \(l_{\sigma}\)= 340 mm; and (3) according to Reference [21]\(l_{cr}\) = 353 mm. It should be noted that in this case, the following rule applies: for higher critical stresses, there is a shorter local buckling half-wavelength.  
The ompared computational methods showed that very good congruence of results in relation to finite element analysis (ABAQUS) was obtained by the critical plate method (CPM), which takes into account both effects. Methods that consider the effect of mutual elastic restraint of cross-section component plates give a good approximation of critical stresses in those beam zones where there is no significant variation of stresses in the longitudinal direction (e.g., span cross-section). In contrast, calculations according to EC3 are the most conservative, because they omit both effects.
## 3 Results
### Regional Heterogeneity Analysis
#### 3.3.3 Western Region  
The original sequence of variables in the western region failed to fully pass the unit root test, prompting the use of the Kao test to examine the long-term equilibrium relationship  
Figure 6: Impulse response function in the central region. (**a**) Impulse response function of DE to DE. (**b**) Impulse response function of DE to GD. (**c**) Impulse response function of GD to DE. (**d**) Impulse response function of GD to GD.  
between the two variables. The results indicate that the ADF test of the residuals yielded a \(p\)-value of 0.0000, confirming the existence of this relationship. Consequently, the PVAR estimation could be performed. The optimal lag order was determined to be 5. After constructing the model using GMM estimation, we conducted an impulse response analysis to explore the dynamic relationship between the two variables, as illustrated in Figure 7.  
The impulse response characteristics in the western region differed significantly from those in the other two regions. Firstly, a complex response of the DE to its own shocks was observed, transitioning from positive to negative and back to positive, indicating inherent instability in its development. Secondly, regarding the impact of the DE on GD, an initial response of zero was noted, followed by a negative response that only began to show a positive influence after the fourth period. This reflects the lagged effect of the DE on GD. Additionally, a slight shift from negative to positive in the response of the DE to GD was experienced in the early stages, but a significant decline was observed by the fifth period, suggesting a more pronounced suppressive effect of the DE on GD at certain stages. Finally, the response of GD to its own shocks initially showed a positive impact, which gradually diminished over time, eventually turning negative after the fourth period. This indicates that internal challenges may be faced by GD as time progresses.
## 4 Conclusions  
The following conclusions can be drawn from the analysis of the results obtained in the present study:  
* Reinforcement bars without inhibitor reach corrosion speeds typical of the active state for chloride ion percentages equal to or greater than 0.8%. In reinforcement bars with inhibitor, a chloride ion percentage of 2.0% in the concrete is necessary for the reinforcement bars to reach corrosion values typical of the active state. * In all cases, the presence of corrosion inhibitor significantly reduces the corrosion speed achieved by the reinforcement bars. * The correlation between corrosion potential measurements and corrosion speed is only consistent in reinforcement bars without inhibitor. * Corrosion potential measurements taken in isolation in structures with reinforcement bars with inhibitor are not indicative of the steel corrosion state. * The direct application of inhibitors on the reinforcement bars emerges as a viable strategy, both for new constructions and rehabilitation, being particularly effective in concretes with chloride ion percentages between 0.8% and 1.6%, where their application reduces the corrosion speed to low levels, increasing the lifespan of the structures. As future research directions, the adherence of the reinforcement with and without inhibitor will be studied, in addition to comparing their efficacy with respect to other types of inhibitors (applied superficially and added during mixing). Conceptualization, A.L.M. and J.P.F. ; methodology, A.L.M. and J.P.F. ; validation, A.L.M., J.P.F., A.C.E. and M.I.P.B. ; formal analysis, A.L.M., J.F.F., A.C.E., M.I.P.B. and T.G.P. ; investigation, A.L.M., J.P.F. and M.I.P.B. ; resources, A.C.E. and M.I.P.B. ; data curation, J.P.F. ; writing--original draft preparation, A.L.M. and J.P.F. ; writing--review and editing, A.L.M., A.C.E., M.I.P.B. and T.G.P. ; visualization, A.L.M. ; supervision, A.C.E. and M.I.P.B. ; project administration, A.C.E. and M.I.P.B. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.
## 2 Blockchain Structure Design Based on Multi-Ship Collision Avoidance Communication Scenario
### The Blockchain-Based Communication Process for Multi-Ship Collision Avoidance Scenarios  
The blockchain-based CA communication process is divided into three stages: ship discovery, trusted communication, and algorithm execution. The specific process is shown in Figure 1.  
When other ships are found to enter the navigation area of the ship, the ship discovery stage is entered, the CA communication process is started, and the contract layer calls the navigation data acquisition interface of relevant ships to obtain data and enter the trusted communication stage.  
Figure 1: The blockchain-based approach to ship collision avoidance communication process.  
In the trusted communication phase, according to the CA rules, the contract layer determines one by one whether all the ships in the navigation area have collision risk, and if there is collision risk, whether the ship is an avoiding ship according to the CA rules. If the ship is not a CA ship for all ships in the area, this means that the ship does not need to perform CA operations, and the CA communication process is finished. If there is a collision risk for a ship and the ship is an avoidance ship, the contract layer starts to calculate the closest point of approach (CPA) and then calculates the distance to CPA (DCPA) and the time to CPA (TCPA) from the calculation result.  
Then, the contract layer calls the service layer interface to send the CA intention to the ship (i.e., the target ship), and the service layer calls the facility layer interface to add the sent information to the transaction set. If a valid response is received from the target ship, a confirmation message is immediately sent in reply to the target ship. If no valid response is ever received from the target ship, the message is resent. The retransmitted record is also saved to the transaction set. The data that are saved to the transaction set is temporarily stored in the pre-submitted blocks. When the consensus of the facility layer is completed, the block is changed from the submitted state to the committed state and written to the database, waiting to be synchronized to other nodes. Any message can invoke the validity verification service of the service layer to verify the truth through the cryptographic components of the facility layer, etc., to ensure the trustworthiness, integrity, and untampered source of the message.  
After the trusted communication phase, to reach an agreement on the CA intention between the avoided ship and this ship, the CA algorithm is executed in the algorithm execution phase according to the type of CA algorithm selected by the user. The operation result of the algorithm calls the service layer data up-linking service to store it on the chain, and at the same time, it is output to the application layer. The CA communication ends.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 6 Tsunamis
### Reviewed Use Cases for Tsunami EW  
Table 5 contains reviewed articles on Tsunami EW systems, including both proposed or tested solutions and methods to evaluate and optimize sensors deployments, especially for the more critical underwater solutions. Non-UWSN systems and designs are also reviewed. Figure 4: Example implementation of a Tsunami Early Warning System based on the IoT and underwater sensors. [PERSON] et al. [79] designed and tested the performance of an acoustic underwater system to develop a near-field Tsunami EW in the Mentawai Basin, between West Sumatra and Indonesia. In this application the distance between sensors (pressure sensors) is large (tens of kilometers) and the distance to shore even larger, so a lower frequency is used for communication. The system was deployed close to the bottom at different heights and with variable receiver ranges. The communication link demonstrated a high reliability with a 350 bps data rate, showing that it is feasible for a tsunami alert system. Higher data rates could be achieved but they would have a lower reliability (which could require re-transmissions of sent packets). The latency is of about 1 s, so the time needed for data to be available to the user or to be ready to be transmitted to another node (in multi-hop configurations) is the duration of the packet plus 1 s, which is a good result for early warning in this context. The study by [PERSON] et al. [80] presents a method to determine an optimal array configuration of offshore tsunami sensors for near-field tsunami early warning. The methodology was tested in Northern Chile. The main issue with near-field tsunamis is that there is a short time interval between the tsunami generation and arrival, so it might be difficult to determine the tsunami source in time. Sensor cost might also be an issue so the study wants to find the minimal number of sensors for an EW system to get optimal performance.
# A DDPG-Based USV Path-Planning Algorithm
## 3 Simulation  
Our experiments were conducted on a computer with an AMD R5 5600 processor and 16 GB of RAM, running on the Ubuntu 22.04 LTS operating system.  
Software and Framework:  
OpenAI Gym: we chose OpenAI Gym as the main simulation environment framework because it provides a common set of interfaces for reinforcement learning. We used pip in-stall gym for installation.  
Tensorflow2-CPU: Used to build and train neural network models. Since a high degree of parallel computing power is not required in our models, we chose the CPU version. Pip install tensorflow-cpu was used for installation.  
Python 3.9.10: All implementations were performed in Python, ensuring portability and easy reproduction of the code.  
Simulation environment implementation:  
Defining the environment: we created a custom Gym environment that simulates the motion of an unmanned boat. This environment implements the necessary methods, such as reset() and step(), and defines the state and action space.  
Actor and Critic networks: we defined Actor and Critic neural networks using TensorFlow Keras API. The Actor network learns to map states to actions, while the Critic network learns to estimate the value of states.  
DDPG Algorithm Implementation: we implemented the DDPG algorithm, utilizing Actor and Critic networks. In addition, we define the playback buffer, the target network, and the optimization process.  
Training loop: in the training loop, we interacted with the environment, collected experience, and updated the Actor and Critic networks according to the DDPG algorithm.  
We simulated the seawater environment in the coastal area under mild weather. To ensure the accuracy of wind, water current, water temperature, and seawater density for two groups of experimental tests with the usage of different models, we set the wind, water current, water temperature, and seawater density to the same parameters. To be specific,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(m_{11}\) & 19 kg & \(d_{11}\) & 4 & \(\tau_{raw}\) & \(\sin(0.001t+0.3\pi)\) \\ \(m_{22}\) & 35.2 kg & \(d_{22}\) & 4 & \(\tau_{raw}\) & \(\cos(0.001t+0.1\pi)\) \\ \(m_{33}\) & 4.2 kg & \(d_{33}\) & 1 & \(\tau_{raw}\) & \(\sin(0.001t+0.2\pi)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of unmanned surface vehicle.  
Figure 2: Simplified model of USV.  
the direction of the wind flow is from north at a speed of 8 m/s, the direction of the water current is from east to west with an average flow velocity of 0.5 m/s, the temperature is at 20 \({}^{\circ}\)C, and the density of seawater is 1025 kg/m\({}^{3}\).
## 2 Materials and Methods
### Green Strength Test  
Plastic cylindrical molds with internal diameter (\(d\)) of 33.5 mm and 67.5 mm height (\(h\)) were used to make samples to investigate the green strength of the fresh mixtures. As [PERSON] et al. (2019) mentioned, nearly \(h\) = 2\(d\) can satisfy the requirement of forming a diagonal shear failure plane. Before pouring the fresh mortar, a silicon spray was used to coat the interior surface of molds for easily demolding later. All of the fresh samples were compacted on a vibration table with 30 Hz for 6-8s to reduce air bubbles. The samples were covered by a plastic film and carefully demolded just 30s earlier before performing the uniaxial unconfined compression test. For each mix design proposal, the samples were tested at the ages of t = 30, 45, 60, 90, 150, and 240 min (4 h). Five specimens were prepared for each fresh mortar  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time (min:s)** & **Steps Followed** \\ \hline -4:00 & Homogenizing dry components, mixing with the low speed by a planetary mixer (HOBART). \\
0:00 & While mixing with the low speed, add the blended liquid (water and superplasticizer). \\
4:00 & Pause, scraping the walls and blade (A dough-like mixture is generated). \\
4:30 & Mixing with the high speed. (until the dough-like material adhere to the walls of the bowl). \\
8:00 & Stop, start to fill molds or do other tests \\ \hline \hline \end{tabular}
\end{table}
Table 4: Timeline and procedures of fresh mortar preparation.  
age. 30 specimens for testing per mixture. The same Instron universal testing machine, equipped with a 10 kN load cell and employed for the extrusion tests was also used for the green strength assessment. A double layer of plastic film covered both sides of a sample. The Polytetrafluoroethylene (PTFE) spray was applied between two plastic sheets to reduce the friction between the sample and base plates. The tests were conducted using displacement control at a rate of 0.2 mm/s. The maximum displacement was 20 mm. Each test took about 2 min in total and strain values up to 30% were eventually achieved. A short test is preferable to preserve the fresh properties of the material under evaluation. The casting and test processes were performed under the same environmental condition (temperature: \(20\pm 2\)\({}^{\circ}\)C; relative humidity: \(50\pm 5\%\)). A high-resolution camera was used to acquire images during the mechanical test. Vertical and lateral deformations were obtained by image analysis using ImageJ. The cross-section of each sample was determined through a MATLAB-code.  
Figure 3: (**a**) Diagram of the extrusion process controlled by the actuator; (**b**) Predefined speed and displacement for the extrusion test.  
Figure 2: (**a**) Section drawing of the ram extruder; (**b**) Photograph of the ram extruder.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Boolean Functions  
In this paper we are interested in verifying the properties of hardware digital systems. Such systems can be thought to be made from large assemblies of logic gates which, in turn, are simple electronic implementations of Boolean functions. As described in Section 2.4, formulas of Propositional Logic can be used to represent Boolean functions and reason about them. In this section, we introduce some basic notions about Boolean functions.  
**Definition 25** (Boolean Function).: _A Boolean function is a function \(f:\mathbb{B}^{n}\rightarrow\mathbb{B}\), where \(\mathbb{B}=\{\top,\bot\}\) is the Boolean domain and \(n\in\mathbb{N}\) identifies the arity of the function._  
**Definition 26** (Functions Monotone in a Variable).: _Given an \(n\)-ary Boolean function \(f:\mathbb{B}^{n}\rightarrow\mathbb{B}\) and a value \(k\in\mathbb{N}\), with \(1\leq k\leq n\), we say that \(f\) is monotone in the variable \(x_{k}\) if \(f|_{x_{k}=\bot}\to f|_{x_{k}=\top}\). Thus, when \(f\) is monotone in \(x_{k}\), changing the value of \(x_{k}\) from \(\bot\) to \(\top\) cannot change the value of \(f\) from \(\top\) to \(\bot\). The definition here provided of monotonicity in a given variable is also known as unateness or positive monotonicity in that variable._  
**Definition 27** (Monotone Functions).: _A Boolean function \(f\) is monotone if it is monotone in each of its variables. Thus, changing the value of any of its variables from \(\bot\) to \(\top\) cannot change the value of \(f\) from \(\top\) to \(\bot\)._