esa-sceva/satcom-chunk-collection huggingface dataset:
# Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
## 2 The Establishment of the Model  
### The Assumption and Description of the Problem  
The optimization of multi-routing and multi-vessel types is a complex task that involves numerous parameters and variables. Table 1 summarizes the key parameters and variables considered in this paper. To facilitate the analysis and optimization of ship scheduling and vessel allocation, this paper outlines a hypothetical framework as follows:  
1. Vessel Diversity: each vessel type possesses a unique cargo-carrying capacity and operating costs. The selection of vessel types is tailored to the characteristics of distinct routes.
2. Intercontinental Trunk Shipping: the order of berthing ports on intercontinental trunk routes is determined, and the container demand between intercontinental endpoints is anticipated and confirmed.
3. Fixed Departure Frequency: the frequency of ship departures is standardized to once per week for all flights within the same week.
4. Vessel Type Consistency: each flight adheres to a predefined vessel type, ensuring only vessels of the same type are utilized within the same flight.
5. Uniform Speed and Fuel Consumption: A consistent speed is maintained by vessels of the same type across different sections, and their fuel consumption function remains unchanged across different sections.  
Drawing on these parameters and assumptions, this paper aims to explore methods for maximizing operating efficiency across various vessel types, routes, and operating costs. The principle is shown in Figure 1.
# A Polarization-Based Method for Maritime Image Dehazing
## 2 Related Work  
### The Physical Model of Atmospheric Scattering  
In situations characterized by foggy weather conditions, natural light undergoes scattering and absorption by fog droplets, resulting in a decrease in the amount of energy reaching the detector and subsequently causing image blurring. McCartney [23] proposed a physical model of atmospheric scattering that has been widely employed in dehazing algorithms. The equation that embodies the physical model of atmospheric scattering is expressed as follows:  
\[I=D+A=Lt+A_{\infty}(1-t) \tag{1}\]  
In the Equation (1), \(I\) represents the foggy image captured by the detector; \(D\) indicates the direct light; \(A\) represents the air light; \(A_{\infty}\) represents the air light from infinity; \(t\) represents the transmittance map of the atmosphere; \(t(z)=e^{-\beta z}\); \(\beta\) represents the extinction coefficient of the scattering medium; \(z\) represents the transmission distance. The equation is modified as follows to obtain the clear image \(L\):  
\[L=\frac{I-A}{1-A/A_{\infty}} \tag{2}\]
## 4 Materials and Methods
### XGBoost  
XGBoost (eXtreme gradient boosting) is an efficient machine learning algorithm that is deeply optimized on top of the gradient boosting decision tree (GBDT) framework. Its core mechanism is to continuously optimize the objective function by gradually adding new decision trees, and each tree is trained based on the residuals of the previous tree so as to gradually reduce the error between the model's predicted value and the true value. As the decision trees continue to accumulate, the value of the loss function gradually decreases, pushing the model to approach the optimal solution. This incremental learning approach makes XGBoost widely used in many fields, such as wind power prediction [23], wildfire disaster risk assessment [24], financial market trend prediction [25], and so on [26].  
In terms of mathematical construction, XGBoost's objective function \(\mathrm{L}(\varphi)\) consists of two parts: the loss function number and the regularization constraint term. The loss function measures how well the model fits the data, while the regularization constraint term acts as a penalty mechanism to control the complexity of the model and prevent it from falling into the trap of overfitting. This optimization strategy improves the generalization ability of the model while ensuring that it still maintains excellent computational efficiency when dealing with large-scale datasets. The formulas for the objective function and regularization term of XGBoost are as follows:  
\[\mathrm{L}(\varphi)=\sum_{i=1}^{n}1(\hat{y}_{i},y_{i})+\sum_{k=1}^{k}\Omega(f_ {k}) \tag{1}\]  
\[\Omega(f_{k})=\gamma\mathrm{T}+\frac{1}{2}\lambda\||\omega\|^{2} \tag{2}\]  
In Equations (1) and (2), \(\hat{y}_{i}\) represents the predicted output of sample \(x_{i}\), while its corresponding true value is \(y_{i}\). The model consists of k subtrees, and the output of the kth subtree is denoted as \(f_{k}\), whose complexity is bounded by the regular term \(\Omega(f_{k})\). The hyperparameters \(\gamma\) and \(\lambda\) together regulate the optimization process of the tree, where T refers to the number of leaf nodes in the decision tree, and \(\omega\) indicates the specific value of each leaf node. In addition, the training error of sample \(x_{i}\) is measured by the loss function \(1(\hat{y}_{i},y_{i})\), which affects the learning effect of the overall model.
## 3 Results and Discussion
### Applicability of Empirical Relations to Predict the Rock Mechanical Properties  
Based on the empirical relationship (Equations (23)-(27)) and well logging data, the continuous profile of rock mechanical properties can be obtained.  
4.1 Calibration of the Well Logging Values with the Laboratory-Measured P-Wave Interval Transit Time and Density  
Bulk density and P-wave velocity are parameters that can be obtained from the density and sonic logs, respectively. These parameters are conventional well logging data and usually used to gain formation information. A wellbore environment such as formation temperature, pore pressure, drilling fluid, etc., can have a considerable impact on the readings of well logging, resulting in the differences in the P-wave interval transit time and density values between well logging and laboratory-measured data [37]. Therefore, before using the P-wave interval transit time and density logging data combined with empirical equations to evaluate the rock mechanical properties, it is necessary to calibrate the P-wave interval transit time and density values obtained via well logging with respect to the laboratory-measured values. Using the linear fitting regression of laboratory-measured and logging values, the fitting model was established, shown in Figure 14. Thus, based  
Figure 13: The plots of the relative error for the calculation results of new equations ((**a**) Equation (23); (**b**) Equation (26); (**c**) Equation (27); (**d**) Equation (24); (**e**) Equation (25)).  
on the model, the P-wave interval transit time and density values from well logging were calibrated. The formulas are as follows:  
\[\Delta t_{\rm c}=1.0478\times AC-2.8636\ \ R^{2}=0.7252 \tag{28}\]  
\[DEN_{\rm m}=1.2778\times DEN_{\rm l}-0.8067\ \ R^{2}=0.9008 \tag{29}\]  
where \(\Delta t_{c}\) is the laboratory-measured P-wave interval transit time, \(\mu\)s/ft; \(AC\) is the P-wave interval transit time from sonic logging, \(\mu\)s/ft; \(DEN_{m}\) is the laboratory-measured density; and \(DEN_{l}\) is the density from density logging.  
As shown in Figure 15, the calibrated P-wave interval transit time and density are in line with the laboratory-measured P-wave interval transit time and density, except for a small amount of experimental data points, and the \(AARD\) values are 3.34% and 1.06%, respectively.  
Figure 14: Relationships between the well log P-wave interval transit time (**a**), density (**b**), and lab-measured P-wave interval transit time (**a**) and density (**b**).  
Figure 15: Well log vs. lab-measured P-wave interval transit time (**a**) and density (**b**).
# Integrated Carbonate-Based CO\({}_{2}\) Capture--Biofixation through Cyanobacteria
## Materials and Methods
### Microalgae Strains  
The cyanobacterium Spr01 was a _Spirulina_ strain donated by a company from Bergamo (Italy). The strain _Spirulina platensis_ Spr02 was kindly donated by Algae S.p.A., a company devoted to the development and supply of industrial photobioreactors. Both strains were maintained with the described _modified Spirulina_ medium in a 100 mL inoculum inside a 250 mL Erlenmeyer flask. The inoculums were kept at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) under a cool white LED lamp and were stirred with an orbital agitator at 80 rpm. The inoculums were made fresh every 14 to 21 days.  
Elemental analysis of a number of Spr01 samples allowed us to define its mean composition, that is, 44 \(\pm\) 1 wt% in carbon, and 10 \(\pm\) 1 wt% in nitrogen. Considering the mean nitrogen-to-protein conversion factor (=6.25) [27], the protein content is estimated to be 63 wt%. Analysis of Spr02 samples gave the following values: 48 \(\pm\) 1 wt% in carbon and 9 \(\pm\) 2 wt% in nitrogen. The estimated protein content is 56 wt%. These values are in good agreement with the average carbon content of microalgal biomass, which typically ranges from 37% to 52% [28], and with the expected protein content of blue-green cyanobacteria [29]. Representative elemental analyses of the samples are collected in Table S1.  
Cell morphology was observed on a temporary slide at \(\times\)400 and \(\times\)1000 magnification by means of a light microscope with differential interference (Leica Leitz BMRD, equipped with an Amscope MU1803 camera). Representative images are shown in Figure S1. The temporary slide was prepared as follows: 0.5 mL of culture solution was centrifuged and the obtained biomass pellets were diluted with 0.5 mL of distilled water, then a single droplet was placed on the slide. The slide was then dried at 37 \({}^{\circ}\)C and a 30 wt% aqueous solution of glycerin was added to suspend the biomass. Spr01 is constituted of round cells with a diameter ranging from 2 to 2.5 \(\upmu\)m, which forms small clusters in solution. On the other hand, Spr02 is composed of cells with a diameter between 2.5 and 4 \(\upmu\)m, forming straight filaments measuring 300--900 \(\upmu\)m in length. This significant difference in morphology is consistent with the macroscopic appearance of respective aqueous suspensions. Spr01 forms fine and uniform systems that require a long time to settle, while Spr02 suspensionsappear cloudy and settle quickly when mixing is stopped. These features also explain the different harvesting methods employed for the two strains.
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 3 System Architecture
### Wireless Node  
An important aspect of developing streetlight control systems is deciding which components to develop and which ones to purchase from the market. To make this decision, it is important to study  
Figure 4: Format of the DALI frames.  
Figure 3: Differential Manchester encoding.  
whether these components exist in the market, their price and the economic impact on the project, among other factors. In the presented system, we have to pay special attention to the wireless node (mote), as a mote needs to be installed in each luminaire. As a result, a high number of motes are needed. A comparison of several motes was implemented in [26], and in our case, several commercial motes such as Waspmote [27] have been analyzed, in particular, together with other open-source, Arduino- and hardware-based nodes [28]. All of them show the following disadvantages in our system:  
* They do not have a DALI interface.
* They are not properly powered. The input voltage is 230 VAC; hence, the voltages for the processing part and for the DALI interface must be generated, as explained below.
* The nodeID cannot be configured in an easy way by maintenance technicians if a mote needs to be replaced.
* No robust connectors are available.  
We decided to design our own mote, since if we had opted to use commercial mote, we would have needed to design an adaptation board to solve the aforementioned disadvantages. The designed mote has two different parts: the processing and radio module, and the rest of interfaces, including the DALI interface and the power supply block. The reason for this division is to try different radio modules, so that both parts can later be integrated into just one board. In this way, we would obtain a specific mote to control the ballasts at a low economic cost. The designed mote will be described in detail in Section 4.  
The decision to design a specific mote is a usual process in many works related to wireless sensor networks. In works such as [29; 30; 31; 32], motes were developed for specific applications to minimize total consumption. In [33], a wireless sensor mote was designed for precision horticulture applications. In other works, some motes were designed to search for possibilities not offered by commercial motes. For example, in [34], a mote was designed to support remote hardware modification. Lastly, regarding wireless nodes to control LED lamps in over-the-air lighting-control systems, motes using the XBee module of the Digi MaxStream radio frequency were designed in [16; 35]. In [18], the Waspmote module was improved by connecting sensors to it and including control over LED lamps. In [36], a simple intelligent wireless module was presented for use in street lighting systems that provides a specific driver capable of communicating through the DALI protocol to the lamp.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 4 Experimental Results and Analysis
### Energy Consumption  
Energy is one of the scarcest resources in UAVs. Micro UAVs are equipped with a small dry-cell battery which can allow flight operation for only a few minutes; approximately 25 to 30 min. This limited energy puts many hindrances on the wide application of these UAVs. It is highly desirable to optimize the energy in order to enhance the lifetime of a UAV. In UAVs, energy is dissipated by three mechanisms: the energy required to actuate the motor control to fly UAV in the air, energy consumed  
Figure 10: Cluster life time at a 2000 \(\times\) 2000 m\({}^{2}\) grid size.  
Figure 9: Cluster life time at a 3000 \(\times\) 3000 m\({}^{2}\) grid size.  
by different sensors and energy required for communication between UAVs. Communication is a major cause of energy consumption in UAVs. Energy consumed in communication is the sum of energy consumed during transmission and reception [28]:  
\[E\ =\ E_{comm}+\ E_{motor}+\ E_{sensor} \tag{8}\]  
\[E_{comm}=\ E_{Tx}+E_{Rx}\]  
where:  
\[E_{Tx}=E_{elect}*L+E_{amp}*L*d^{2} \tag{9}\]  
and:  
\[E_{Rx}\ =\ E_{elect}*L \tag{10}\]  
\(E_{elect}\) is energy dissipation while running transmitter and receiver circuitry. \(E_{amp}\) represents the energy for transmit amplifier. \"\(L\)\" shows the number of bits transmitted and \"\(d\)\" is the distance between transmitting and receiving nodes. We compute the cumulative energy consumption of whole network using EALC, GWOCNET and CACONET for the period of 120 s.  
Figures 11 and 12 show that network energy consumption increases with number of nodes. It is clear from the figures that EALC outperforms the other two techniques. Lower energy consumption of EALC is due to the setting of optimal transmission range and election of optimal CHs.
## 4 Discussion
### Velocity Parameters  
The primary focus of this analysis was the assessment of barbell movement velocity--specifically, peak velocity (PV) and mean velocity (MV)--following the application of various types of conditioning activities (CAs). Most of the studies included (Table 3) confirm that achieving the PAPE effect in the bench press is possible, provided that several key conditions are met. Regarding the intensity range of the CA, it was shown that loads between 70 and 80% of 1 RM, applied in short sets (e.g., one--three sets), were most often associated with significant increases in movement velocity in subsequent sets. For example, a +7.0% increase in PV and +15.9% increase in peak power (PP) were observed following a standard bench press at 80% 1 RM [34]. In contrast, loads of \(\geq\)85-90% 1 RM require more careful management of rest periods and training volume, as excessive load and fatigue may cancel out the potential PAPE effect. This is supported by the findings of [PERSON] et al. [33], where higher BFR loads failed to improve PV. In terms of training volume, performing too many sets or taking rest intervals that are too short may lead to fatigue accumulation, thereby neutralizing the PAPE effect. In studies by [PERSON] and [PERSON] [27], a one-time increase in velocity and power was clearly observed in the first set after applying pyometric push-ups, but the effect disappeared in the following sets. A similar trend was observed in protocols utilizing blood flow restriction (BFR): increases in velocity were noted with light loads and in the first set, followed by declines as volume increased [32].  
Furthermore, rest intervals shorter than 3 min were shown to be insufficient, especially when heavier loads were used, resulting in reductions in PV or MV values [33]. On the other hand, rest periods of 4-5 min, or even up to 8-10 min, have been shown to yield positive outcomes [27; 28]. The final choice of rest duration should be tailored to the athlete's training level and the specific demands of the session. Some sources suggest that the strongest PAPE response occurs within a narrow \"time window\" of 3-8 min following the CA [6; 7; 15].  
Among the analyzed studies, the most effective CA variant for short-term increases in barbell velocity during the bench press was the standard bench press using a similar load (approximately 70-80% 1 RM and in the repetition range of 2 to 6), which resulted in PV improvements of up to +7% [34]. Equipment modifications (e.g., cambered bar) led to slightly smaller gains. Upper-body pyometric elements, such as explosive push-ups, also showed effectiveness, but typically only in the first of several sets [27].
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 4 Discussion  
This paper presents a crack propagation prediction model using a DBN, suitable for effective tracking of damage evolution in aerospace structures. The method is validated  
Figure 16: Updated parameters \(C\) under different stress ratios (**a**) \(R=0.1\); (**b**) \(R=0.5\); (**c**) \(R=0.7\)[38].  
Figure 17: Updated parameters \(\alpha\) under different stress ratios (**a**) \(R=0.1\); (**b**) \(R=0.5\); (**c**) \(R=0.7\)[38].  
Figure 15: Crack propagation prediction under different stress ratios (**a**) \(R=0.1\); (**b**) \(R=0.5\); (**c**) \(R=0.7\)[38].  
using experimental data from three-point bending and single-edge crack tension tests. The results show that through DBN particle filtering, the uncertainty interval for crack propagation in the three-point bending specimen decreased from 24% to 6%, as shown in Figure 11, and the prediction error for the lifespan was also reduced from over 30% to lower than 5%, as shown in Figure 12. In the single-edge crack experiment, although multi-source uncertain variables increased the uncertainty interval of crack propagation, the error range of the filtered uncertainty interval gradually decreased, and the prediction error for the uncertain variables, \(\Theta\), also exhibited the trend of gradual decrease and convergence. This indicates that the DBN filtering can track and correct time-dependent variables (crack length) and reduce the uncertainty of time-independent variables (parameter \(\Theta\)).  
By comparing the single adaptive variables and multiple adaptive variables in the two examples, it is evident that the convergence of the variables in particle filtering decreases along with the enlarged dimension of the uncertain variable space. One potential reason for this is that the interdependence and nonlinear effects among parameters increase the complexity of the system. With multiple parameters involved, the update of each parameter depends not only on its own observed data but may also be influenced by the states of other parameters. This leads to multiple possible solutions within the parameter space. A possible solution includes the improvement method of marginalizing some uncertain variables (such as the Rao-Blackwellized particle filtering method), which is a direction for future research.  
As the model is continuously updated, both the parameter variables and the predicted cracks will gradually converge. However, when new uncertainties suddenly emerge, causing significant changes in the prediction outcomes, the variation in parameter means and uncertainty intervals will increase sharply until the new factors stabilize. Subsequently, the uncertainty interval gradually decreases to a new state of convergence.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 2 Game Design
### Default Parameters in Simulations
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline number of attack stations & \(N_{sa}\) & 10 \\ number of attackers per attack station & \(N_{am}\) & 4 \\ departure angle & \(\theta\) & 30\({}^{\circ}\) \\ attacker impact radius & \(D_{a}\) & 50 m \\ attacker speed & \(v_{a}\) & 1 mach \\ attacker flight time & \(A_{\tau}\) & 1500 s \\ attacker alert radius & \(R_{a}\) & 40 km \\ attacker maneuverability & \(\alpha_{e}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of attack force. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline time interval & \(\Delta t\) & 20 s \\ number of particles & \(N\) & 20 \\ number of iterations & - & 40 \\ particle velocity weight & \(w_{v}\) & 1 \\ acceleration constants & \((c_{1},c_{2})\) & (2, 2) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of optimization. pending on its maneuverability (\(\alpha_{e}\)). The attacker maneuverability increases its survivability, which in turn urges the defense force to optimize its deployment plan accordingly. The simulation time interval (\(\Delta t\)) is fine enough to manifest the capability of all the agents based on the game rules, yet is coarse enough not to prolong the CPU time unnecessarily. In the PSO algorithm, the weight (\(w_{v}\)) and acceleration constants (\(c_{1},c_{2}\)) on particle velocity are tuned to move the particles quickly to the global best position, without converging to local optimal positions. The number of particles (\(N\)), number of iterations, and particle velocity increment are fine-tuned by observing the convergence condition and the CPU time over many runs of simulation.