esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 1 Introduction  
In light of past maritime accidents, international conventions, such as SOLAS (Safety of Life at Sea [1]) and MARPOL (Maritime Pollution [2]), have been continuously updated to pursue two main goals: safeguarding the lives of seafarers and minimizing marine pollution. These conventions define minimum training and certification requirements for ship personnel, covering the full career progression from cadet to Master or Chief Engineer. These standards are codified in the STCW Convention (International Convention on Standards of Training, Certification and Watchkeeping for Seafarers) [3], which includes specific competencies for different operational areas, such as marine engineering, bridge operations, and electro-technical systems.  
To meet these requirements, advanced maritime simulators have been developed by companies like TRANSAS [4], providing high-fidelity training environments that replicate real shipboard conditions. These simulators are particularly valuable for training officers in scenarios that are rare or difficult to replicate at sea.  
Among the many navigation techniques practiced, leading lights--also known as range lights--remain essential. They offer reliable visual guidance, especially in narrow channels or riverine environments. Despite advances in satellite-based navigation, leading lights continue to be widely used due to their accuracy and resilience under various environmental conditions.  
However, this method requires continuous visual monitoring by the bridge officer, particularly during night shifts. Over time, this sustained attention can lead to fatigue and degraded performance--factors that contribute significantly to maritime incidents [5, 6, 7, 8, 9].  
In response to this challenge, the present study investigates the potential of artificial intelligence (AI) to support officers in maintaining visual alignment during night navigation. Specifically, we propose a deep learning model based on convolutional neural networks (CNNs) trained to recognize correct and incorrect alignment with leading lights, using video data collected from a navigation simulator.  
This work makes three key contributions: (1) the development of a lightweight CNN adapted to low-light maritime conditions; (2) an expanded classification scheme that distinguishes between six navigation states, improving interpretability; and (3) experimental validation showing that the proposed model can outperform human observation under visually ambiguous conditions.
## 3 Constitutive Equations  
In this work, the FGM constituents are considered to behave as isotropic linear elastic materials. It is worth pointing out that despite this assumption, the FGM analyzed here remains heterogeneous along the thickness direction, and the simple rule of mixtures is used as a homogenization scheme to evaluate the effective material properties of the FGM, which is written as [47; 51]  
\[P\big{(}\xi^{3}\big{)}=\big{(}P_{top}-P_{bot}\big{)}V_{top}^{i}\Big{(}\xi^{3} \big{)}+P_{bot}, \tag{11}\]  
where \(P\big{(}\xi^{3}\big{)}\) denotes the effective material property (e.g., Young's modulus, mass density, Poisson's ratio) along the thickness coordinate, \(P_{top}\) and \(P_{bot}\) are the material properties of the top and bottom constituents, respectively, while \(V_{top}^{i}\big{(}\xi^{3}\big{)}\) is the volume distribution of the top constituent--through thickness--corresponding to the following forms of the power-law [22]:  
\[V_{top}^{I}\Big{(}\xi^{3}\big{)}=\left(\frac{1+\xi^{3}}{2}\right)^{n}, \tag{12}\]\[V_{top}^{II}\left(\xi^{3}\right)=\left(\frac{1-\xi^{3}}{2}\right)^{n}, \tag{13}\]  
where \(n\) is the volume fraction exponent or power-law index, and its value may be greater or equal to zero. In order to adequately compare with numerical results reported in the literature, the volume distribution \(V_{top}^{II}\left(\xi^{3}\right)\) is also considered in this work.  
From now on, to distinguish the aforementioned volume distributions, the volume distribution given by Equation (12) is referred to as FG-I and the volume distribution described by Equation (13) is referred to as FG-II.  
Now, for the assumed constituent materials, the fourth-order elasticity tensor, in terms of contravariant vectors, is given by  
\[\mathbf{C}=C^{ijkl}\mathbf{g}_{i}\otimes\mathbf{g}_{j}\otimes\mathbf{g}_{k} \otimes\mathbf{g}_{l}, \tag{14}\]  
where the contravariant components are determined by  
\[C^{ijkl}=\lambda\left(\xi^{3}\right)g^{ij}g^{kl}+\mu\left(\xi^{3}\right)\left( g^{ik}g^{jl}+g^{il}g^{jk}\right). \tag{15}\]  
In Equation (15), \(\lambda\left(\xi^{3}\right)\) and \(\mu\left(\xi^{3}\right)\) are the Lame parameters defined in terms of [PERSON]'s modulus, \(E\left(\xi^{3}\right)\), and [PERSON]'s ratio, \(\
u\left(\xi^{3}\right)\), as  
\[\lambda\left(\xi^{3}\right)=\frac{\
u\left(\xi^{3}\right)E\left(\xi^{3}\right) }{\left[1+\
u\left(\xi^{3}\right)\right]\left[1-2\
u\left(\xi^{3}\right) \right]},\qquad\qquad\mu\left(\xi^{3}\right)=\frac{E\left(\xi^{3}\right)}{2 \left[1+\
u\left(\xi^{3}\right)\right]}.\]  
Also, in Equation (15), \(g^{ij}\) denotes the contravariant components of the Riemannian metric tensor in the reference configuration, given as \(g^{ij}=\mathbf{g}^{i}\cdot\mathbf{g}^{j}\).  
Recalling the considerations of the constituents behavior previously mentioned in this section, the mechanical behavior is assumed to follow [PERSON]'s law. Then, the relation between the second Piola-Kirchhoff stress tensor and the Green-Lagrange strain tensor is given by  
\[\mathbf{S}=\mathbf{C}\mathbf{E}, \tag{16}\]  
And the contravariant components of \(\mathbf{S}\) are determined by \(S^{ij}=C^{ijkl}E_{kl}\).
# Trends and Technological Challenges of 3D Bioprinting in Cultured Meat: Technological Prospection
## 4 Discussion
### Biofabrication of Cultured Meat
#### 4.3.3 Biomaterials  
It is in this context that biomaterials are applied. Several supporting biomaterials have been used successfully in adipose tissue engineering and may be viable options for cultured meat production. The material used for cultured meat must be biocompatible and biodegradable, safe for consumption, and mimic the texture of traditional meat [75].  
Amongst the different potential biomaterials, biopolymers stand out. This material class has been extensively studied in the biofabrication of cultured meat as they provide a temporary mechanical support structure for cell adhesion and proliferation, simulating the extracellular matrix (ECM) [76]. Biopolymers can be divided into two categories: the naturals and the synthetics. However, it is essential to note that biopolymers derived from animals should be avoided because one of the principal drivers for cultured meat production is animal welfare [77, 78]. For this reason, biopolymers derived from algae and plants have been widely studied for their application in cultured meat production. The main biopolymers derived from algae used in this process are alginate [79, 80, 81, 82], agarose [77, 83]and Konjac gum [84], and derived from plants are soy protein [85; 86; 87], glutenin [88], starch [89; 90], and bacterial cellulose [91].  
Alginate was the biomaterial with the highest co-occurrence in the network generated from our keywords search (Figure 4), linked with the term \"bioink\". This polymer is a natural polysaccharide extracted from brown algae and has been used as a matrix in the 3D cell culture, providing support for cell growth and integration [92]. Alginate is also widely used in the food industry as an additive [93]. Due to its capacity to produce hydrogels via ionic crosslinking, alginate is one of the most promising and most applied natural biopolymers in tissue engineering [94; 95] and, consequently, in the biofabrication of cultured meat. However, pure alginate lacks functional groups that stimulate cell adhesion and proliferation. It also has low mechanical properties [82; 96]. Alternative studies have shown that combining alginate with other biomaterials--for example, collagen and gelatin--revealed promising results, mainly improving cell adhesion and proliferation. However, the use of animal-derived materials is contrary to the concept of cultured meat, where the main purpose is to avoid the slaughter of animals. Therefore, studies using biomaterials from other sources, such as plants, algae, and microorganisms, have been conducted [84].  
[PERSON] et al. [33] used the 3D printing technique to print scaffolds using modified alginate-based bioinks, enriched with isolated pea and soy protein. The bioinks enriched with pea and soy proteins showed better stability and rigidity than those produced with modified alginate, increasing muscle cell proliferation by up to 90%. These results may be of great interest in the field of cultured meat biofabrication.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Model Comparison Experiments
With the lowest model complexity, the proposed model achieved excellent segmentation accuracy, highlighting its strong generalization ability and reliability, maintaining stable performance even in new application scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU/\%** & **F1-score/\%** & **Accuracy/\%** & **Params/M** & **Flops/G** & **FPS** \\ \hline UNet & 44.53 & 44.21 & 67.81 & 19.51 & 157.96 & 16.84 \\ DeepLabV3+ & 68.90 & 60.67 & 94.34 & 40.29 & 61.51 & 41.68 \\ HRNet & 52.00 & 48.40 & 77.85 & 29.53 & 80.30 & 21.49 \\ DDRNet & 62.51 & 52.72 & 91.92 & 32.36 & 31.39 & 70.96 \\ ContextNet & 66.06 & 59.37 & 91.95 & 0.87 & 0.78 & 135.97 \\ DABNet & 69.19 & 62.67 & 93.78 & 0.75 & 4.64 & 108.38 \\ EDANet & 65.03 & 58.77 & 91.04 & 0.68 & 3.91 & 87.09 \\ ESNet & 66.13 & 59.71 & 91.85 & 1.66 & 11.82 & 72.04 \\ MobileNetV3 & 68.05 & 60.42 & 93.57 & 2.98 & 1.16 & 119.16 \\ Ours & 69.63 & 63.78 & 94.03 & 0.23 & 0.95 & 73.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation Metrics of Various Network Models Tested Directly on the AsphaltCrack300 Dataset. Figure 6: Segmentation Results of Various Models on Representative Samples from the Crack500 Dataset. The segmentation results on representative samples from the AsphaltCrack300 dataset are shown in Figure 7. Due to the coarse granularity of the asphalt material, many models struggle to effectively distinguish between background and crack pixels (e.g., DeepLabV3+, DDRNet). In scenarios where the distinction between crack regions and the background is low (see left columns 3 and 6), several models exhibit misclassification (e.g., HRNet, ESNet, ContextNet). Under strong lighting interference (see left column 5), the proposed model achieves the closest results to the ground truth. Overall, the proposed model demonstrates the best segmentation accuracy and generalization ability across datasets.
## 3 Results and Discussion
### Flow Visualization from CFD
In the case of a spinning ball, the CFD results (Spin B; \(\omega=50.2\) rad/s, \(\text{Re}=4.25\times 10^{5}\)) indicated a large-scale counter-rotating vortex pair in the wake downstream of the rotating direction (right side of the back view in Figure 8), but the vortex pair remained relatively stable and exhibited only minor fluctuations (Figure 8g-i). Similar trends were observed in the other cases involving a spinning ball. In the free-flight test, a large-scale counter-rotating vortex pair, similar to that observed in the CFD analysis, was formed in the wake of the spinning ball (\(\omega=37.7\) rad/s, \(\text{Re}=3.94\times 10^{5}\)) on the downstream side of the rotating direction (counterclockwise when viewed from the top). Again, this vortex pair structure was relatively stable and had relatively minor fluctuations (Figure 8j-l). Various vortex structures containing a large-scale counter-rotating vortex have been observed in the wake of a smooth sphere, such as a horseshoe vortex, an alternating vortex, or a hairpin vortex [14; 15; 16; 17]. In addition, a large-scale vortex pair has been observed in wind tunnel tests conducted on nonspinning baseballs [10] and soccer balls [43]. The results of the CFD and free-flight experiments in this study also show a large-scale vortex pair in the wake of nonspinning and spinning soccer balls. Therefore, it can be concluded that a large-scale counter-rotating vortex pair is one of the dominant vortex structures in the wake of nonspinning and spinning soccer balls in flight based on these results.
## 7 Evaluation Results
### Number of Hops  
We measured the number of hops made from the sending terminal to the first receiving node within the destination area, as shown in Figure 25.  
Figure 23: How to measure the number of copies.  
Figure 24: Percentage of the reduction in the number of copies.  
The measured numbers of hops are shown in Table 4. \"Location-based routing\" recorded the largest number of hops with a maximum of 77 and an average of 32. The maximum number of hops of \"motion vector routing\" was 16. This is enormous considering that the bundle arrival rate is low, which means that the bundles reached only those destination areas that are close to the sending nodes. The maximum number of hops and the average number of hops of the \"epidemic routing\" were 35 and 12.8, respectively, and those of the \"spray & wait routing\" were 23 and 9.6, respectively. Those of both the circular covered area model and the vectorial covered area model were 16 and 5.5, respectively whichever covered area model was used. Those were the smallest among the different routing methods. This is because the role of relaying bundles over a long distance was assigned to traveling nodes.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 4 Structural Damage Prediction of Engineering Vibration Signals  
### Structural Damage Trend Prediction Method  
In this paper, the LSTM-SVR integrated model is used as a prediction model for structures. Since the instantaneous frequency can effectively reflect the changes in the health status of structures during their service life, the model is divided into three major parts in engineering vibration signal prediction: signal processing, data preprocessing, and LSTM-SVR network prediction. First, perform VMD and Hilbert transform on engineering data to obtain instantaneous frequency. Then, divide the obtained instantaneous frequency data into training sets and test sets and perform standardization processing. Finally, use the standardized data as input to the LSTM-SVR network model for data training and data prediction. Therefore, the method steps proposed in this article are as follows:  
1. Select the acceleration vibration signal required for the experiment.
2. Use VMD to decompose the original signal.
3. Use Hilbert transform to obtain the instantaneous frequency of the decomposed component.
4. The instantaneous frequency of each component is analyzed to find out the high-order modal that characterizes the decrease in structural stiffness.
5. The feature frequencies are divided into training sets and test sets, which are standardized and used as input to LSTM-SVR. After learning the network model using the training sets, the test sets are used to predict structural health issues.
6. To illustrate the effectiveness of the LSTM-SVR prediction model in predicting structural health problems, it is compared with a single prediction method.  
The prediction results of the chaotic time series data in the previous section show that the structural damage prediction of the integration of LSTM and SVR achieved excellent prediction results. To verify the effect of this method in actual vibration engineering data, actual engineering vibration data were used.
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### Moving Aperture SAR Imaging
#### 4.2.2 Description of Targets  
For the data collection, two targets were used, one linear and one nonlinear. The linear target was a trihedral corner reflector whose longest dimension was 50.8 cm (20 inch). The nonlinear target used was a commercial mixer with a custom antenna attached to the LO port. The antenna used was an in-house built dual band dipole antenna. The low frequency band of the antenna was tuned to 900 MHz and the high frequency band was tuned to 1800 MHz. These frequencies correspond to the center of the linear and the harmonic bands, respectively. Photographs of the targets are shown in Figure 14.
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 4 Experiments
### Compared Systems  
* **NPSS**: Our system, which we call Neural Parametric Singing Synthesizer (NPSS), as described in Section 2.
* **IS16**: A concatenative unit selection-based system [1], which was the highest rated system in the Interspeech 2016 Singing Synthesis Challenge.
* **Sinsy-HMM**: A publicly accessible implementation of the Sinsy HMM-based synthesizer ([[http://www.sinsy.jp/](http://www.sinsy.jp/)]([http://www.sinsy.jp/](http://www.sinsy.jp/))). This system is described in [4; 35], although the implementation may differ to some degree from any single publication, according to one of the authors in private correspondence. While the system was trained on the same NIT-SONG070-F001 dataset, it should be noted that the full 70 song dataset was used, including the 3 songs we use for testing.
* **Sinsy-DNN**: A publicly accessible implementation of the Sinsy feedforward DNN-based synthesizer ([[http://www.sinsy.jp/](http://www.sinsy.jp/)]([http://www.sinsy.jp/](http://www.sinsy.jp/))) [37]. The same caveats as with Sinsky-HMM apply here. Additionally, the DNN voice is marked as \"beta\", and thus should be considered still experimental. The prediction of timing and vibrato parameters in this system seems to be identical to Sinsy-HMM at the time of writing. Thus, only timbre and \"baseline\" F0 is predicted by the DNN system.
* **HTS**: A HMM-based system, similar to Sinsy-HMM, but consisting of a timbre model only, and trained on pseudo singing. The standard demo recipe from the HTS toolkit (version 2.3) [38] was followed, except for a somewhat simplified context dependency (just the two previous and two following phonemes).
## 4 Results
### Extraction of Critical Factors  
From the near-miss incident database, data points on 150 incidents corresponding to the target scenario were extracted. The phenotypes and genotypes were analyzed according to the analysis procedure described in Section 3.1.3. Figure 3 shows the relationship between the phenotypes and genotypes of all of the extracted incidents. The phenotype \"A1.1 Too early action\" accounted for approximately 95%. As for the highest-level genotypes, \"C2 Misjudgment of situation\" accounted for approximately 97%. In the lower-level genotypes, \"K1 Temporary obstruction of view\" and \"F2 Expectance of certain behaviours\" accounted for more than one-third of the extracted incidents. By analyzing near-miss incident data for right turns at signalized intersections using DREAM, we extracted frequent genotypes of K1 and F2.  
The definition of genotype K1 is \"Objects (e.g., driven or parked vehicles, gatherings of people) in the traffic environment cause temporary obstruction of view\" [45]. Figure 4 shows a sample of the near-miss incident classified as K1. When the vehicle stopped at the intersection, waiting for oncoming vehicles to pass, there was an occlusion created by the vehicle proceeding from the opposite direction to turn right. The driver could not see the lane where oncoming vehicles were moving. The driver started to turn right even though he could not see through the lane. An oncoming vehicle appeared from the occlusion area, and the driver applied emergency braking to avoid a collision.  
Figure 3: Relationship between phenotypes and genotypes in near-miss incidents with oncoming vehicles when making a right turn at a signalized intersection (\(N=150\)).  
The definition of genotype F2 is \"Expecting other road users to behave in certain ways following praxis (e.g., brake gently, stop for stop signs and red-lights, give way when driving on a non-priority or minor road and comply with the speed limits)\" [45]. Figure 5 shows a sample of a near-miss incident classified as F2. When the vehicle stopped at the intersection waiting for oncoming vehicles to pass, the traffic light changed from green to yellow and red with a green arrow. The driver saw this light change and began to turn right, expecting the oncoming vehicle to stop at the red light. However, the oncoming vehicle did not stop, and the driver applied emergency braking to avoid a collision.  
Figure 4: Sample near-miss incident classified as “K1 Temporary obstruction of view” (ID: 659524).  
Figure 5: Sample near-miss incident classified as “F2 Expectance of certain behaviours” (ID: 661582).
# Ultrasonic Deicing Efficiency Prediction and Validation for a Flat Deicing System
For the ultrasonic deicing system, deicing energy and efficiency are two critical issues during the deicing process. In this paper, deicing energy was estimated from the perspective of conservation of energy and deicing efficiency for different models was investigated, which can provide guidance for deicing model selection to achieve deicing with reduced energy consumption. With help of numerical simulations, deicing efficiency for a flat deicing system can be revealed and predicted to optimize the deicing system. In the process, harmonic analysis coupled electrical and mechanical responses for the piezoelectric deicing system was performed to simulate steady-state ultrasonic vibration. Shear stresses at the interface of the ice layer were calculated and presented. The geometry effects of piezoelectric actuators on shear stress and power consuming were investigated. Based on that, the deicing efficiency factor (DEF) was proposed to describe deicing efficiency for seven different configurations, which is very meaningful for deicing result evaluations. Then, four icing cases were studied to validate DEF. Meanwhile, the shear stress distributions along three different axis directions were discussed. Based on results of harmonic analysis, a cohesive zone model (CZM) [23; 24] was introduced in the standard-explicit co-simulation [25; 26] process to study the wave propagation and deicing process. Finally, according to parameters used in finite element simulations, the experiment platform was founded and deicing experiments were conducted to verify the feasibility of the system.