esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 5 Case Studies  
### Case Study 1: Successful AI Implementation in Shipping  
#### 5.1.1 Company Overview: Maersk Line  
Maersk Line, a global leader in container shipping, has been at the forefront of adopting AI technologies to enhance operational efficiency and sustainability. With a vast fleet and extensive global network, Maersk faced challenges related to fuel consumption, emissions, and route optimization [47; 70; 244].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Case Study** & **Company/Project** & **AI Technology Used** & **Outcomes** \\ \hline Case Study 1: AI in Shipping [47; 70; 244] & Maersk Line & Fuel optimization, predictive maintenance Air and water quality monitoring, predictive models & Reduced fuel consumption, lowered CO\({}_{2}\) emissions, improved maintenance practices Enhanced environmental monitoring, showed rapid response to pollution events, compiled with regulations \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case Studies of AI Implementation.  
Fuel Optimization  
Maersk implemented AI algorithms to optimize fuel consumption across its fleet. By analyzing historical data, real-time sensor inputs, and weather conditions, AI systems were able to recommend optimal speeds and routes for vessels. This predictive approach allowed Maersk to significantly reduce fuel usage, cutting operational costs and lowering greenhouse gas emissions [70; 247; 248].  
For instance, the AI system could adjust a vessel's speed based on predicted weather patterns, avoiding adverse conditions and reducing unnecessary fuel burn. Additionally, by optimizing routes, the system ensured that ships traveled the shortest and safest paths, further contributing to fuel efficiency [78; 136; 249].  
To enhance the reliability and efficiency of its fleet, Maersk integrated AI-driven predictive maintenance systems. These systems utilized data from various sensors installed on the ships' engines and other critical components. By continuously monitoring parameters such as temperature, vibration, and pressure, AI algorithms could predict potential failures before they occurred [247; 249].  
This proactive maintenance strategy minimized unexpected breakdowns, reduced downtime, and extended the lifespan of equipment. As a result, Maersk improved its operational efficiency and reduced maintenance costs, contributing to its overall sustainability goals [70; 78; 250].  
Outcomes  
The AI implementation at Maersk yielded significant benefits:  
* A substantial reduction in fuel consumption and CO2 emissions;
* Enhanced route planning, leading to more efficient and timely deliveries;
* Improved maintenance practices, resulting in reduced operational disruptions and costs;
* Overall, Maersk's adoption of AI technologies positioned the company as a leader in sustainable shipping practices.
## 5 Conclusions and Future Work  
Efficient energy consumption is a challenging problem in battery-powered wireless sensor networks. The facts that each node has a limited battery charge and it is also impossible or impracticable to recharge the batteries, thus reducing energy consumption, are the keys to increasing the network's lifetime.The scenario investigated was the PA for a coffee plantation in Southeast Brazil, and this research made clear the necessity to evaluate the benefits of the sensor's location in the field. In this paper, the evaluation of the energy consumption of the RPL routing protocol was given for nine combinations of different scenarios in three distinct topologies, changing the number of nodes, the transmission/interference range, and the metric used. Based on the results obtained, we observed the best performance of the network's energy consumption using a circular topology with the HOP metric. Our statistical results were achieved using the method of paired comparisons and indicated that for all topologies, the HOP metric obtained the lowest energy consumption compared to the ETX metric. When evaluated together, the circular topology had the lowest energy consumption compared to the other topologies with the HOP metric.  
Regarding the three topologies evaluated, it could be concluded that a circular topology resulted in better performance concerning maintaining the number of nodes. The tree topology required considerably more of the first hop node, and could also cause other bottlenecks in the network. In conclusion, node density was significant in the tree topology and needed to be carefully evaluated before being adopted in an IoT with the RPL.  
Figure 20: Energy consumption from the three topologies with the HOP metric.  
As future work, it is relevant to add knowledge of the RPL's topology and metrics to compare with other existing routing protocols in WSNs, e.g., Lightweight On-demand Ad hoc Distance-vector Routing Protocol - Next Generation (LOADng) [23] and LOADng-IoT [24], and evaluate the RPL under other metrics and network topologies, as well as propose modifications to the RPL metrics to consider the context-awareness and its virtual topologies, and finally, examine authentication and authorization methods [25].  
Conceptualization, E.F.S. and A.B.V.; methodology, E.F.S., Y.M., and F.O.S.; software, Y.M. and F.O.S.; validation, Y.M. and F.O.S.; formal analysis, E.F.S., A.B.V., Y.M., and F.O.S.; investigation, Y.M. and F.O.S.; resources, E.F.S.; writing, original draft preparation, Y.M. and F.O.S.; visualization, Y.M.; supervision, E.F.S. and A.B.V. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank UFJF, CAPES, CNPq, FAPESP and FAPEMIG.  
The authors declare no conflict of interest.
# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods
### Architectures
In their preliminary article focused on modelling nonlinear audio effects [39], a convolutional autoencoder with fully connected latent space (dubbed CAFx) was proposed with the following structure: an adaptive front-end consisting of two 1D convolutional layers (the first with 128 filters of size 64 and the second with 128 filters of size 128), a max pooling layer, batch normalization before this pooling and a residual connection; as well as a latent-space of two dense layers with 64 units and a decoder consisting of four fully connected layers (of sizes 128, 64, 64, 128) whose last layer includes a Smooth Adaptive Activation Function (SAAF). This architecture was used to model three of the audio effects in the IDMT-SMT-Audio-Effects data set [40]: distortion, overdrive, and equalization (EQ). Their follow-up article [37] modifies the latent space of this structure by replacing the fully connected layers with Bidirectional LSTM (Bi-LSTM) which are LSTM containing forward and backward information at every time step to create a convolutional and recurrent autoencoder (CRAFx) to model more complex, time-varying audio effects, again from the IDMT-SMT-Audio-Effects data set. Another variant of this architecture that uses the feedforward WaveNet in the latent space (CWAFx) is introduced in [37] and the three autoencoders are compared with the original feedforward WaveNet architecture introduced in [26] on various modelling tasks, including that of a vacuum tube amplifier, sampled from a 6176 Vintage Channel Strip unit. The results of this comparison showed that both the feedforward WaveNet and CAFx,the original DNN autoencoder, performed similarly and that they are both outperformed by CRAFx and CWAFx with CRAFx performing slightly better than CWAFx. It was reported that the vacuum tube preamplifier was able to be successfully modeled on the two-second samples. The last two architectures presented in Table 2 fall under the scope of gray-box neural methods which, along with newly emerging white-box methods, aim to improve interpretability of neural-network based approaches.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis
### Model Comparison
In general, the Hybrid-STL model shows unique comprehensive advantages in wave height prediction tasks, providing a better model selection direction for subsequent research and practical applications.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Communication Range for Lossless Transmission Mediums
#### 3.2.3 Characterization of the Detection Threshold for the Prototype Implementation
It is clearly evident that the detection thresholds based on the measurement data are in the same order of magnitude as the calculated detection threshold based on the values given by the datasheet. For the following numerical calculations, the minimum detectable magnetic flux densities for the three different bandwidths of the coil-to-AMR communication are compared with the coil-to-coil communication.
## 4 Branch-and-Price-and-Cut Algorithm
### Label Setting Algorithm
#### 4.4.4 An Overview of the Label Setting Algorithm  
Algorithm 1 outlines the procedure of the label setting algorithm. We define \(\mathcal{U}_{i}\) as the set of unextended labels associated with node \(i\) and \(\mathcal{P}_{i}\) as the set of extended labels. The algorithm begins by initializing both \(\mathcal{U}_{i}\) and \(\mathcal{P}_{i}\) as empty sets and proceeds to generate an initial label of the source node \(N_{0}\), denoted as \(L_{N_{0}}\), which is then placed into \(\mathcal{U}_{N_{0}}\). During each iteration of the algorithm, it chooses a label \(L_{i}\) with the least reduced cost from the unextended labels and continues its extension along feasible arcs. Newly generated labels are incorporated to \(\mathcal{U}_{i}\) if they are not dominated by existing labels, and any existing unextended labels dominated by the new ones are eliminated. Following this, \(L_{i}\) is moved from \(\mathcal{U}_{i}\) to \(\mathcal{P}_{i}\). This iteration process continues until all \(\mathcal{U}_{i}\) sets become empty. The pricing subproblem reaches an optimal solution by identifying the labels at node \(N_{end}\) with the lowest reduced cost.  
```
Set \(\mathcal{U}_{i}=\varnothing\); \(\mathcal{P}_{i}=\varnothing\), \(i\in\{P\cup D\cup N_{0}\cup N_{end}\}\);  Initialize the label \(L_{N_{0}}=\{N_{0},Y_{N_{0}}=0,AC_{N_{0}}=\rho_{0},RC_{N_{0}}=\rho_{0},V_{N_{0} }=(1,0,...,0)\}\);  Set \(\mathcal{U}_{N_{0}}\)=\(\{L_{N_{0}}\}\); while\(\cup_{i\in P\cup D\cup N_{0}\cup N_{end}}\mathcal{U}_{i}\
eq\varnothing\)do  Choose a label \(L_{i}\) with the least accumulative reduced cost ; for\(arc(i,j)\in A\)do ifExtending through arc(i,j) is feasiblethen  Extend \(L_{i}\) along arc \((i,j)\) to generate new label \(L_{j}\) using functions (40)-(43); if\(L_{j}\) is not dominated by any label in \(\mathcal{U}_{j}\cup\mathcal{P}_{j}\)then \(\mathcal{U}_{j}=\mathcal{U}_{j}\cup\{L_{j}\}\);  Discard labels in \(\mathcal{U}_{j}\) dominated by the newly added label \(L_{j}\);  end if \(\mathcal{U}_{i}=\mathcal{U}_{i}\backslash\{L_{i}\}\), \(\mathcal{P}_{i}=\mathcal{P}_{i}\cup\{L_{i}\}\);  end for  end for  end for Output:\(\mathcal{P}_{i}\), \(i\in\{P\cup D\cup N_{0}\cup N_{end}\}\)
```  
**Algorithm 1**Label setting algorithm overview.
## 3 Result and Discussion
### Biocompatibility of PGC/EYO Composite Films  
Biocompatibility is implemented in accordance with ISO-10993-5 specifications. The results of the biocompatibility test on the first and third days is show in Figure 6. The cell survival rate of each group was higher than that of the Control group (tissue culture plate, TCP). The three groups of PGC112, PGC/0.25 EYO, and PGC/0.5 EYO on the first day after culture all showed a very significant cell proliferation response. As the proportion of EYO increased, the proportion of cell proliferation increased compared to the Control group and were 115%, 123%, and 127%, respectively. PGC112 continued to grow by 4% on the third day of culture; the value-added was 119%, but the PGC/0.25 EYO and PGC/0.5 EYO groups both dropped to 117%. The above results show that PGC film or PGC/EYO film is non-cytotoxic and contributes to cell proliferation [15; 18]. [PERSON] et al., used an EYO-medium mixture treated with sonication to culture HaCaT-cells (immortalized human keratinocytes) and human endothelial cells (HUVEC). The result was found that EYO acts through the reduction of inflammatory processes and ROS production [29]. It can support cell repair and migration and shows potential development for the early acceleration of wound closure.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 6 Simulation Analysis  
The segmented hybrid impedance control method, in conjunction with the hyper-redundant robotic arm system's dynamic model provided, collision detection algorithms, and contact collision force models, is used to establish the dynamic model of the HRSM during end-effector operation and perform simulation analysis.  
In this section, we present two simulated application scenarios for the HRSM. We apply the control strategy outlined in Section 4 to the theoretical dynamics model described in Section 3. Each of the 24 links in the designed multi-rigid-body HRSM possesses identical properties. For reference, Table 1 provides the rotation axis direction for the manipulator joints for the first scenario. Table 2 shows the material parameters of the contact model. The surface material of the robotic arm is hard rubber, with a low Young's modulus and a high Poisson's ratio, and due to the strong elasticity of the material, the recovery coefficient is also high. The target surface material is hard aluminum alloy. Table 3 presents the inertia parameters and geometric data for the manipulator links, indexed from \(\text{i}=1\) to 24. and Table 4 provides the rotation axis direction for the manipulator joints for the second scenario.
## 4 Reconstructing Global Stress for a Marine Connector
### Simulation for the Connector  
(1) Load Analysis  
The THAFTS (version 2.0) software was utilized for the three-dimensional hydroelastic analysis, determining the connector's response amplitude operator and short-term extreme profile loads [25]. The analysis revealed that the connector experienced primarily longitudinal and vertical forces, with lateral compression as a secondary loading mode. Wave direction greatly influenced the connector load, with oblique waves resulting in the highest loads. The study considered a connector load range of 0 to 2630 kN within the \(XY\) plane, representing the primary loading mode [26; 27].  
(2) Nonlinear Contact  
A finite element model was created in ABAQUS for the simulation analysis of the connector, with different elements used to model the connector body, load-bearing bowel pin, and bearing sleeve.  
During the modeling process, a Tie constraint was employed between the bearing seat and the connector, while the contact fit between the bowel pin and the bearing seat was simulated using a penalty function with rigid contact in the normal direction and limited sliding in the tangential direction. The quasi-static nonlinear simulation was conducted using the arc-length method.  
In terms of simulation conditions, a block was introduced to simulate the load on the connector, coupled with a reference point (RP3) representing the compression and separation between the main and secondary platforms.  
(3) Calculation Conditions  
The load range varied from 50 kN to 5000 kN, with the maximum load approximately twice the design load of 2630 kN. Loads were calculated at 10\({}^{\circ}\) intervals, resulting in 3600 load cases across 36 directions. Figure 5 gives a schematic of the load directions.  
Figure 4: Inner bulkhead of connector structure.  
Figure 3: Connector structure of the offshore platform.  
Partitioning of Data Samples  
Due to the contact connection between connectors and pins, the global stress gradient does not linearly increase and distribute along the load magnitude under different load conditions with varying amplitudes and angles [28].  
All the finite element stress data for all load cases are used as sample data, and they are randomly divided into training sets (2400 cases), validation sets (600 cases), and test sets (600 cases). These data from the simulation will be utilized to build the initial ANN for global stress.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 1 Introduction
Among them, the underwater profile method is widely recognized as a more accurate measurement technique, commonly used for measuring the apparent optical properties of water bodies, particularly in large oceanic areas. The underwater light field profile distribution holds significant importance in the study of marine physical and biological processes, such as marine primary productivity, upper-layer heat balance, remote sensing inversion of water color elements, and interactions in marine biogeochemical processes [8]. Long-term, large spatial-scale aquatic environmental monitoring plays an important role in human understanding and comprehension of the oceans [9]. In order to realize global water ecological monitoring, it is extremely necessary to develop high-sensitivity, high-precision, stable, and reliable field sensors. Currently, representative in situ ocean radiometers internationally typically possess a spectral resolution of generally 10 nm or higher. However, with advancements in ocean color remote sensing technology, high-spectral and high-resolution instruments are continuously being developed. Examples include the Ocean Color Instrument (OCI) on the U.S. PACE satellite (focused on phytoplankton, aerosols, clouds, and the marine ecosystem), the Hyperspectral Imager for Coastal Ocean (HICO) utilized for coastal ocean remote sensing on the International Space Station, and the Advanced Hyperspectral Imager (AHSI) with full spectral resolution on China's Gaofen-5 satellite, all with spectral resolutions at 5 nm. A resolution of 10 nm is no longer sufficient for the latest generation of water color remote sensing instruments. Therefore, based on the consideration of the demand for spectral radiance observation of water bodies, this paper designs a miniature underwater spectral radiometer suitable for underwater profiling. The instrument uses concave holographic grating as a spectral component, and the spectral observation range is 400-950 nm, covering the spectral bandwidth requirements of all bands of underwater spectral observation and water color sensors. The full-band spectral resolution is better than 3 nm, which has the advantages of a broad spectrum, high resolution, and small size. In addition, the stray light in the optical structure of the spectral radiometer is simulated and analyzed. The measures to suppress the stray light are proposed, and the effect of inhibiting the stray light is analyzed. Laboratory calibration was carried out for the principle prototype, and the wavelength calibration results showed that the spectral resolution of the instrument met the design requirements. The radiation calibration experiment was completed and the spectral irradiance response curve of the instrument was obtained.
# Evaluation of Resource Exhaustion Attacks against Wireless Mobile Devices
## 3 Generalized Intruder Model  
In this section we propose a generalized analytical model of an intruder performing ERE attacks. The model represents a knowledge required for simulation and analysis of this class of attacks. The model is expressed by a formal tuple:  
\[M_{\text{ERE}}=(G,O,A,R,F,E,P) \tag{1}\]  
\(G\) specifies goals of ERE attacks directed to breach the availability of some autonomously working hardware unit. At that battery resource declining can by realized in a rapid gradual or spasmodic way, depending on the nature of the target system and the intruder intentions. \(O\) describes the targets of straightforward or implicit effects of ERE attacks, including sensors of the physical medium, network connections, software components, operating system processes, etc. \(A\) specifies sequences of steps the intruder runs to reach the goals \(G\). \(R\) outlines necessary system resources and means used during an attack, including starting possibilities of the intruder, knowledge and practical skills the intruder possess as well as required time and financial costs. In our work we differentiate five intruder types in compliance with a classification of the attacker access to a device [30], namely _Type_0--no direct access (social engineering can be used only), _Type_1--remote access through TCP/IP protocol from outside, _Type_2--remote access from nearby by means of Near Field Communication (NFC), Radio Frequency Identification (RFID), ZigBee, etc. ), _Type_3--outward access (direct access to RS-232, I2C, etc.) and _Type_4--full access (any modification of the microcircuits and software). During the analysis we use an intruder classification of intruder capability levels. It comprises the following levels: _Level1_--common typical utilities, known vulnerabilities, _Level2_--specially developed utilities, previously unknown vulnerabilities, _Level3_--team of intruders of _Level2_ (unrestricted resources) [31]. \(F\) determines specific conditions and limitations significant for ERE attacks, such as rules for switching between the sleep and regular modes of the hardware unit. \(E\) describes effectiveness of an ERE attack that as an averaged increase of the battery discharging speed after the attack application, whereas \(P\) denotes possible ways of protection from ERE attacks and attack stealthiness. Regarding existing papers in the field, the key ERE attack types are identified:  
* forced waking of a sleeping device (denial-of-sleep) [13; 14] (results of an analysis of this type of attacks are given in Table 1);
* increase of wireless ongoing or outgoing traffic on a device (Table 2);
* forming of electromagnetic interference on wireless channels, causing an increase in transmitter power [10] (Table 3);
* untypical usage (misuse) of a devices' software, including forcing incorrect settings and others (Table 4).