esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
## 3 Results and Discussion  
Figure 2a represents the detailed process of a droplet falling for \(Oh=0.15\) (70% G-W) and \(Bo=0.108\) (R = 0.8 mm). To study the kinetics of the contraction process of the liquid neck, a dimensionless analysis was used. \(h_{min}\) represents the outer radius of the liquid neck and R is the outer radius of the needle. \(tau\) represents the dimensionless of the remaining time before the liquid neck breaks \(\tau=(t_{b}-t)/t_{cap}\), \(t_{cap}\) is the capillary time, \(t_{cap}=(\rho R^{3}/\sigma)^{1/2}\), \(t_{b}\) is the time at the moment of the break, and \(t\) is the real time.  
The liquid neck pinch-off process is shown in Figure 2b. As the droplet is detached from the needle, the curvature at the liquid neck increases, the effect of surface tension is gradually enhanced, and the contraction process is accelerated, eventually forming a strip of liquid filament, with fracture occurring at the thinnest point. Due to the different curvature of the two sides, the process of contraction of the two sides of the liquid filament is also different. The liquid filament close to the side of the liquid sphere is faster to break, because the curvature change here is more intense, and the effect of surface tension is more obvious. The difference between the two sides of the liquid neck contraction can be observed in Figure 2c, which becomes apparent in the later stage of the contraction.  
Approximately 1.25 ms after the pinch-off (indicated by the blue line), the curvature of the two sides begins to change. The liquid filament gradually elongates, and two possible locations of fracture, one upper and one lower, start to appear. As the droplets fall off, the process is illustrated by the green, red, and black lines, where the curvature on the lower side exceeds that on the upper side. Eventually, the lower side, with the greater curvature, undergoes fracture first.
## Appendix B Optimization Algorithms
### Grid Peak-Power Minimization  
The third service that was envisaged to be optimized by the scheduling module was the required contracted power or grid-peak-power (GPP). Due to the nature of its objective function, a typical quadratic programming formulation sufficed to solve the optimization problem. The simple formulation, convergence speed, as well as its ability to find true optimal solutions, made us prefer this method over the GA to optimize this service. The minimization of the GPP hereby proposed, falls into the category of quadratic programming problems. Quadratic programming is the process of solving a linearly constrained, quadratic optimization problem, that is, the problem of optimizing (minimizing or maximizing) a quadratic function of several variables subject to linear constraints on these variables. Quadratic programming is a particular type of non-linear programming [41]. The formulation of the optimization problem is presented in Equation (A13). We observe that the same physical constraints as for the GA formulation are present in this problem (see Equations (A11) and (A12)).  
\[\begin{split}\underset{P_{grid},P_{but}}{}^{Minimize}& \sum_{h=1}^{24}({P_{grid}^{h}}^{2}+{P_{but}^{h}}^{2})\\ & s.t.\\ P_{grid}^{h}+{P_{but}^{h}}+{P_{pv}^{h}}+{P_{load}^{h}}=0\\ \Delta t\cdot\sum_{h=1}^{24}{P_{but}^{h}}=0\,\left({SoC^{h=24}= SoC^{h=1}} \right)\\ P_{pv}^{h}={P_{pv}^{h}}^{m\,mpt}\\ 0\%\leq SoC_{but}^{h}\leq 100\%\\ 0\,kW\leq P_{grid}^{h}\leq 36 kW\\ 0\,kW\leq P_{but}^{h}\leq 27 kW\end{split} \tag{21}\]  
We can note that here there are no costs associated to the power values, as the algorithm is only meant to minimize the peaks of power themselves, trying to make the profile as smooth as possible.  
As deduced from its objective function, the method minimizes the power peaks of the grid and the battery simultaneously, reducing not only the required contracted power, but also the depth-of-discharge of the battery, that can lead to extended battery life. There is no associated costs to the grid or battery power, as the power profile itself suffices for the optimizer to obtain the required results. As for the EC and CO\({}_{2}\), once the algorithm generates the resulting grid and battery profiles for the entire test period, all the performance indicators can be computed and the grid profile is used as the input for the balancing module.  
Contrary to heuristic methods such as the GA, quadratic programming methods do not required the tuning of hyper-parameters, which diminish the setting-up time and the uncertainty of obtaining sub-optimal solutions.
## 1 Introduction
With the development of deep learning technology, more and more researchers develop the application of deep learning methods for fault detection, such as convolution neural network [16], recurrent neural network [17], and multilayer neural network models [18; 19]. In [20; 21], Bayesian networks were used for sensors fault detection of autonomous vehicle. Even though the deep learning neural network can deal with the raw data without manually feature extraction, deep learning-based methods require a large-scale dataset with manual annotations, which is difficult to collect. The large-scale implementation of autonomous vehicles in transportation systems require the effective sensor fault detection and robust path tracking motion control. One of the significant contributions of this work is to detect the source fault sensor and isolated the fault signal in real-time for autonomous vehicle to enhance the reliability of fused data. The other one is the realization of robust path tracking control when sensor failures occur. Specifically, we develop a fault-tolerant path tracking control approach with combining the model of adaptive model predictive control algorithm and a novel sensor fault detection method based on federal Kalman filtering and chi-square detector. The main contributions of this work can be described as: (1) a single-track 3 DOF vehicle dynamics model is established and Taylor expansion is performed for modelling linearization; (2) an adaptive model predictive control algorithm is developed for robust path tracking control; (3) an improved weight assignment method based data fusion method is proposed for multi-sensor data fusion and fault signal isolation; and (4) a novel sensor fault detection approach is proposed using federal Kalman filtering and fault detector with parallel structure for each sensing source, consisting of two state chi-square detector and residual chi-square detector. The simulation and experimental results of double lane changing show the effectiveness of the proposed method in this work. The rest of this paper is organized as follows. The vehicle dynamic model is established and model linearization process is described in Sections 2.1 and 2.2. The constraints and objective functions are constructed in Sections 2.3-2.6, and the path tracking control algorithm based on model predictive control is designed in Section 2.7. The multi-sensor information fusion algorithm and fault signal detection and isolation algorithm are described in Sections 2.8 and 2.9, respectively. In Section 3, the simulation and experimental verification using double lane changing path tracking scenario is presented. Finally, the conclusions are given.
# Boundary Matching and Interior Connectivity-Based Cluster Validity Analysis
## 2 Related Work
### Typical Clustering Algorithms
#### 2.1.1 C-Means Algorithm  
C-means has been widely used in almost all fields owing to its simplicity and high efficiency. Its detailed steps are listed in Algorithm 1.  
```
Input: the number of clusters \(C\) and a dataset \(X\) containing \(n\) points. Output: a set of \(C\) clusters that contains all \(n\) objects. Steps:
1. Initialize cluster centers \(v_{1},v_{2},\ldots,v_{C}\) by selecting \(C\) points arbitrarily.
2. Repeat;
3. Assign each point to one certain cluster according to the nearest neighborhood principle and a chosen measure;
4. Update cluster centers by \(v_{i}=\sum_{j=1}^{n}u_{ij}x_{j}/\sum_{j=1}^{n}u_{ij}\) for \(i=1-C\);
5. Stop if a convergence criterion is satisfied;
6. Otherwise, go to Step 2.
```  
**Algorithm 1**C-means Clustering Algorithm  
The key parameter in C-means is the number of clusters (\(C\)) which has to be determined in the clustering process.
## Challenges and Potential Solutions
### Transceiver Design and THz Signal Generators  
The current transceiver structure is considered as one of the main challenges in utilizing the THz band. In other words, the available transceivers remain incapable to operate at the THz spectrum [80]. For instance, sophisticated signal processing is required to handle the massive propagation loss at THz band frequencies. Other parameters, such as the high power, high sensitivity, and level of noise figure must be handled. Furthermore, transmission power and distance require careful investigation. In THz frequency bands, utilizing silicon germanium-, gallium nitride-, gallium arsenide-, and indium phosphide-based technologies is highly possible, but designers must consider the limited power gains in mobile networks [80]. Transmission distance is also limited in such technologies. Therefore, a novel transceiver architecture that considers the nonlinear amplifier, phase noise, and modulation index is necessary. To conclude, the current transceiver architectures are inadequate to deal with the THz sources, and new architectures are required, especially for the medium-to-high part of the THz bands (> 300 GHz) [80; 81]. The conventional complementary metal oxide semiconductor technologies and the recently introduced nanomaterials, such as graphene, can be utilized for novel transceiver architectures for THz-enabled equipment [82].
## 4 Simulations and Results
### Simulation Scenarios  
Table 1 summarizes the main parameters used during simulations. When performing these simulations has been remained constant the entries number in the network per unit time. This factor is important, particularly in high density networks.
## 2 Methods
### Data Extraction and Data Synthesis  
Data extraction for the scoping review was performed using a form based on the JBI tool, capturing crucial details like authorship, publication country and year, study design, patient characteristics, outcomes, interventions, procedures, and other relevant data. Descriptive analyses of these data were conducted, with the results presented numerically to show the study distribution. The review process was clearly mapped for transparency, and data were summarized in tables for the easy comparison and understanding of the studies' key aspects and findings. Quantitative metrics such as the percentage reduction in plantar pressure, the average time integral under the hallux region, and variations in Achilles tendon tension were analyzed. Additionally, comparative analyses between different lacing techniques, such as high-top versus low-top shoes, were conducted to evaluate the biomechanical differences in terms of injury prevention and foot stability. In analyzing metrics such as the plantar pressure distribution and Achilles tendon tension, we assessed the comparability of these measurements across studies. Due to methodological variations--such as differences in pressure-sensing technologies and participant activity levels--direct comparisons were limited. Where possible, we normalized data by converting values into comparable units or by reporting findings relative to baseline measurements within each study. Any discrepancies in methodology were carefully noted, and the results were synthesized qualitatively when quantitative comparisons were not feasible, ensuring a balanced interpretation across studies.
## 2 Materials and Methods
### Materials
#### 2.2.1 Ground Measurements  
An overview of the collection of ground measurements is given below. The experimental set-up and protocols used to regular top soil moisture (TSM, 0-5 cm), texture, and roughness data were described in detail in [27].  
On each monitored plot, transects were determined to collect geo-located measurements of TSM and texture (Figure 1). A portable ThetaProbe (ML2x) was used to collect regular measurements of TSM. By using the calibration relationship established by [27], the signal delivered in mV was converted in volumetric moisture. For texture, 2 to 8 points were geo-located on each plot. For each point, 16 cores within a 15 m diameter circle were collected (to a depth of 25 cm). Clay, silt, and sand fractions were finally derived based on element size. Measurements of the surface roughness were performed after each tillage event. Two profiles were recorded parallel and perpendicular to the direction of the tillage of the plot, using a 2 m long needle profilometer (composed of 201 needles spaced one centimeter apart). The root mean square height (hrms) and the correlation length (lc) were derived from the couple of profiles.  
The ground measurements were collected quasi-synchronously with satellite acquisitions (\(\Delta\)t \(<\) 1 day for most measurements) on bare soils showing a wide range of surface conditions, characterized by values of TSM ranging from 3.2 to 34.9%, content in clay from 9 to 58%, hrms from 0.5 to 6.9 cm, and lc from 1.1 to 16.5 cm.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 2: Computation of the DODAG(s)
#### 5.2.2 Main Loops and DODAG Construction  
* _While Loop (Line 12):_ In the extreme case, each node forms a separate DODAG. Since there can be at most \(|V|-1\) DODAGs, this loop executes at most \(O(|V|)\) times.
* _For Loop (Line 14):_ Iterates over all nodes in \(QoSN\), adding another \(O(|V|)\) factor.
* _While Loop (Line 17):_ Processes paths, with operations involving set operations that take at most \(O(|V|)\) time.  
_Final complexity of Algorithm 2:_  
\[O(|V|^{3}) \tag{3}\]  
The worst-case complexity is due to nested loops iterating over nodes and paths and the processing involved in constructing the DODAGs.
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 4 Deep Q-Network for Energy-Efficient Resource Allocation
### Training and Testing Algorithm  
We train the DQN with the following methods: First, we use a data sampling method with experience replay memory. Data sampling is used to remove the temporal relationships between the used data to learn the Q-network. Here, the experience replay memory is a data storage technique in which the agent collects data while interacting with the environment. The data consist of tuples of the state, action, reward, and next state. The Q-network is trained by randomly sampling data tuples from experience replay memory. In this paper, the experience replay memory is denoted by \(\mathcal{D}\). Second, we use a fixed target network method that includes two Q-networks, the target network and the online network, in the training process. \(Q(\mathbf{s}_{t+1},a,\theta,\beta)\) of (13) is calculated as the target network, and \(Q(\mathbf{s}_{t},a_{t},\theta,\beta)\) of (12) is calculated as the online network. Additionally, the weights of the online network are periodically copied to those of the target network. The training process is described in Algorithm 1. Parameters of the online and target networks are initialized (lines 1-3). The agent observes the state in the environment and selects the action according to the \(\epsilon\)-greedy policy (lines 10-11). That is, the agent performs a random selection with the probability of \(\epsilon\), inputs the current state to the online network with the probability of \(\epsilon-1\), and selects the largest value among the observed Q-values as output. The selected action is saved in the joint action group (line 12). If the agent selects the actions for all V2V links, the joint action group interacts with the environment and acquires a reward (line 14). The data tuples collected through the above process are stored in the experience replay memory (lines 15-16). The sampled data tuples are used to update the online network. When the online network repeatedly updates the weights and biases, the weights and biases of the online network are copied to those of the target network (lines 17-25). The testing process is described in Algorithm 2. Unlike the training process, the testing process greedily selects an action based on the Q-network learned by the training process (lines 8-9). After that, the action is stored in the joint action group in the same way as the training process (line 10). If the agent selects the action for all V2V links, the joint action group interacts with the environment (line 12). When the time step \(t\) reaches the simulation end time, the performances are evaluated in terms of the sum rate of the V2I and V2V links, the outage probability of V2V links, and the average power consumption of V2V links (lines 14-17). Figure 3: A Q-network of the DQN framework.
## 3 Results and Discussions
### Roughness  
Figure 10 illustrates the distribution of hole wall roughness for CFRP and titanium alloy at different rotational speeds. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. It is concluded that when the rotational speed is lower, the difference between the hole wall roughness of UVR and CR is larger. When the rotational speed is 200 rpm, the roughness of CFRP is reduced by 20% with UVR and 28% for the titanium alloy, which indicates that ultrasonic improves the hole wall roughness of the laminated material better at low rotational speed machining parameters. As the rotational speed increases, the hole wall roughness decreases for both CR reaming and UVR. It shows that the higher the rotational speed, the better the hole wall finish. The reason for this phenomenon is that the reamer  
Figure 8: Comparison of thrust forces at different ultrasonic amplitudes.  
Figure 9: Comparison of thrust forces at different levels of tool rotation speed: **(a)** CFRP; **(b)** Ti.  
side edge scrapes the hole wall more significantly due to the higher reamer feed per tooth at lower speeds. As a result, ultrasound is more effective in improving roughness.
## 3 Classification Method
### Application Domain and Field
#### 3.1.2 Non-Medical Context  
Includes emotion recognition systems based on EEG signals that are designed to entertain, educate, monitor, or play games.
## 3 Results and Discussion
### Comparison of Protease Activity and pH during AAO and ABA Production  
Proteases break down proteins into peptides, which can contribute flavor precursors, mouthfeel, and enhance the \(\alpha\)-amylase activity [34]. We measured the protease activity to better understand the dynamics of the fermentation. This aspect had not been explored previously in amazakes prepared with either of the two microbes.  
The protease activity followed a similar trend to the \(\alpha\)-amylase activity, with overall higher levels in AAO than in ABA (\(p\) < 0.001). AAO showed a higher activity rate during the first 8 h and reached a maximum activity rate of 0.16 units/mL at 24 h (Figure 2D). In ABA, the maximum activity rate of 0.09 units/mL occurred at 16 and 24 h. This aligns with the literature, indicating that _A. oryzae_ produces more proteases than _B. amyloliquefaciens_ during koji fermentation [21]. After 10 h, the protease activity in both samples began to plateau, possibly due to the availability of rice protein, enzyme limitations, or the accumulation of inhibitory products. The protease activity changed significantly over time for each microorganism (\(p\) < 0.001).  
The optimal temperatures for protease enzymes of _A. oryzae_ and _B. amyloliquefaciens_ fall within the ranges of 55 \({}^{\circ}\)C-60 \({}^{\circ}\)C [35] and 50 \({}^{\circ}\)C-60 \({}^{\circ}\)C [36], respectively. This could explain why the protease activity for ABA was higher at 55 \({}^{\circ}\)C than at 70 \({}^{\circ}\)C, i.e., the reverse of what was observed for the \(\alpha\)-amylase activity.  
The enzymatic activity is governed not only by temperature but also by pH. The amazake inoculated with _A. oryzae_ started with a higher pH of 6.6, whereas the amazake inoculated with _B. amyloliquefaciens_ had an initial pH of 5.7. Over time, the pH values of all three amazakes landed between 5 and 5.5; both values correspond with the literature for koji fermentation (Figure 2C) [37]. This pH range may be less conducive to the proteases of _B. amyloliquefaciens_, which function optimally above pH 7.0 [38], while _A. oryzae_ proteases thrive in the range of pH 4.5 to 6.0 [35].  
Overall, our results can be explained by the fact that _B. amyloliquefaciens_ proteases are sensitive both to higher temperatures and lower acidity levels. The variations in protease activity highlight the distinct enzymatic behaviors of the two microbial communities during amazake fermentation.
###### Abstract  
Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.  
underwater acoustic sensor network; clustering; LEACH protocol; underwater energy propagation model; parallel transmission; replication computing
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 2 Current Developments of Unmanned/Autonomous Towing Technology  
Currently, there are numerous ongoing projects in different countries aimed at achieving autonomous ship berthing using tugboats. These projects are exploring various operational scenarios, taking into account the technology involved in the recognition, judgment, modeling, and control of the autonomous tugboats. Additionally, research efforts are focused on developing specific element technologies that will enable safe and efficient ship berthing, even with the absence of a human operator on the tugboat. As such, the field of autonomous ship berthing is rapidly evolving, and it is expected that the continued progress in the development of these technologies will have significant implications for the maritime industry in the coming years.