esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results and Experimental Setup  
The evaluation of the trained model was carried out in three aspects: reconstruction, generation, and molecular property prediction. The more accurately the input data are reconstructed with high probability, the more effectively the encoder and decoder of the Variational Autoencoder (VAE) can be evaluated as well trained. The evaluation of generation was conducted by generating new latent vectors through random sampling and exploration within the latent space of the generative model. To verify the chemical and physical validity of the generated molecules from a general molecular perspective, novelty and validity were evaluated [60]. The MOSES metric was used to compare the distribution of the generated molecules with that of the training dataset, confirming the generation of diverse molecular structures. Additionally, Posebuster and SA score calculations were performed to assess the chemical and physical feasibility as well as the synthetic accessibility of the generated molecules [61; 62]. After evaluating the general validity from a molecular perspective using these computational metrics, molecular property prediction evaluation was conducted to assess how well the generated molecules predict the HOMO and LUMO properties specific to the electrolyte additive domain. The prediction performance was compared with Unimol, the state-of-the-art (SOTA) molecular property prediction model at the time of this study [63; 64]. To evaluate the accuracy of HOMO and LUMO predictions for newly generated molecules, DFT calculations were performed on approximately 1000 generated molecules, and these DFT calculation values were used as the ground truth to compute the Mean Absolute Error (MAE) between the HOMO and LUMO predictions of the Unimol and NPVAE models.  
Each model was trained separately for HOMO and LUMO properties, and the hyperparameters are as follows. The latent vector dimension was set to 256 (z_dim), and the hidden layer size was 512 (h_size), with an intermediate layer size of 256 (mid_size). The KL loss was weighted by 0.01 (\(\xi\), kl_magnification), the topology loss by 3.0 (\(\beta\), topology_magnification), the bond loss by 1.0 (\(\delta\), bond_magnification), the root label loss by 2.0 (\(\alpha\), root_magnification), the label loss by 1.0 (\(\gamma\), label_magnification), the property loss by 1.0 (\(\varepsilon\), prop_magnification), and the molecular (conformation) loss by 2.0 (\(\varepsilon\), mol_magnification). The model was trained for 40 epochs with a batch size of 1, utilizing four NVIDIA GeForce RTX 3070 GPUs in a CUDA-enabled multi-GPU environment.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 2 The Importance of Order  
### Periodic Ordered Structures  
In photonics, periodically ordered structures are typically named photonic crystals (PCs) [5]. These periodic structures, whose length scale is proportional to the wavelength of light in the band gap, are the electromagnetic analogue of a crystalline atomic lattice. If the dielectric constants of the materials in the crystal are different enough, and the absorption of light by the material is minimal, then scattering at the interfaces can produce many of the same phenomena for photons as the atomic potential does for electrons. In particular, PCsmay possess photonic band gaps, which are ranges of frequencies in which light cannot propagate through the structure.  
In the last years, they have inspired a lot of interest in the scientific community, because they offer unprecedented opportunities for the realization of miniaturized optical devices [6; 7; 8; 9]. The introduction of proper defects in the periodic medium in terms of geometrical or physical parameters modifications, acting as defect states inside the photonic bandgap, gives the resulting device interesting functionalities (Figure 1a). The defect formation breaks the symmetry pertaining to the \"ordered pattern\" and enables electromagnetic field localization and light flow manipulation [5]. The key feature of such defected structures relies on their unique guidance capability [10; 11] but also on the tuning or sensing opportunity which can be provided to the whole photonic device by acting on the physical and geometrical parameters at the defect location. The properties of PCs out of the plane of periodicity are investigated too. In particular, guided resonances, which are PC characteristic modes that couple to out-of-plane impinging external radiation, provide interesting sharp spectral features in dielectric PC slabs with potential applications in imaging and sensing [12].  
The integration of metals with dielectric patterns allows the withstanding of surface plasmon resonances (SPR) and localized SPR. The characteristic enhancement at the metal surfaces can be judiciously exploited for ultrasensitive biological probes.  
The translation of such photonic ordered structures on the OF platforms is part of the lab-on-fiber technology development [2; 3]. In this regard, bottom-up methodologies based on self-assembly procedures provide a high production throughput without using high-cost microfabrication facilities. Unfortunately, these processes are still difficult to implement on unconventional substrates such as the OFs and the formation of ad-hoc defects is inherently limited. Remarkable examples of photonic structures self-assembled in a highly ordered fashion on the OF are reported in Section 3.
# Mathematical Modeling and Simulation of Logistic Growth
## 2 Background
\begin{table}
\begin{tabular}{c c c} \hline
**Continuous Growth** & **Discrete Growth** & **Percentage Error** \\ \hline \(N_{t}=N_{0}\,e^{kt}\) & \(N_{t+1}=N_{t}\,(1+r)^{t}\) & \(PE=100\,(r-k)\,/\,k\) \\ \hline \(k=ln(1+1.0)=0.693\) & \(r=1.00\) & 44.270\% \\ \(k=ln(1+0.5)=0.405\) & \(r=0.50\) & 23.315\% \\ \(k=ln(1+0.2)=0.182\) & \(r=0.20\) & 9.696\% \\ \(k=ln(1+0.1)=0.095\) & \(r=0.10\) & 4.921\% \\ \(k=ln(1+0.05)=0.049\) & \(r=0.05\) & 2.480\% \\ \(k=ln(1+0.02)=0.020\) & \(r=0.02\) & 0.997\% \\ \(k=ln(1+0.01)=0.010\) & \(r=0.01\) & 0.499\% \\ \hline \end{tabular}
\end{table} 
Table 1: **Comparison of discrete and continuous growth by evaluation of corresponding \(r\) and \(k\) rates**. Ranges: \(0.01\leq r\leq 1\), \(0.01\leq k\leq 0.693\), \(0.499\%\leq\) PE \(\leq 44.27\%\), respectively. Decreasing \(r\) below 0.1 implies a significant \(PE\) decrease with respect to \(k\), allowing discrete growth (Equation (3)) to catch up with continuous growth (Equation (2)) in accordance with the original [PERSON] model (Equation (1)). whose first derivative \(N_{t}^{\prime}\) (Figure 1b) is positive and increasing for \(N_{0}<N<L/2\), resulting in \(N_{t}\) being concave up. For \(L/2<N<N_{max}\), the slope \(\frac{dN}{dt}\) is negative and \(N_{t}^{\prime}\) is decreasing, making \(N_{t}\) concave down [5,12]. Figure 1: **Continuous model of logistic growth**. _Parameter settings_: initial population \(N_{0}=4\); maximum population (at time \(t=100\)); \(N_{max}=46.4\); crowding coefficient \(c=0.001\); continuous growth rate \(k=0.05\); carrying capacity \(L=50\); inflection population \(N^{*}=L/2=25\); inflection time \(t^{*}=\frac{1}{t}\ln(\frac{L-N_{0}}{N_{0}})=48.85\). (**a**) The exponential growth (Malthusian) model is displayed as a reference. \(N^{*}\) and \(t^{*}\) are, respectively, the \(x\) and \(y\) coordinates of the inflection point, obtained by setting \(N_{t}^{\prime\prime}=0\), when the logistic function grows the fastest and then flips its concavity, growing slower until \(L\) is reached [13]. (**b**) At \(N^{*}\), the _acceleration_ changes from increasing to decreasing, i.e., the second derivative is zero at the top of the hump. The _discrete_ version of logistic growth  
\[N_{t+1}=N_{t}\bigg{[}1+r\bigg{(}1-\frac{N_{t}}{L}\bigg{)}\bigg{]}, \tag{9}\]  
also called the _master_ equation, has no general closed-form solution, and it was the reference method for our discrete-time event simulation. The ecologist [PERSON] studied this equation for populations and discovered that it could produce very complicated dynamics [14]. However, by keeping \(r\) small enough (Table 1), the population is prevented from changing in size rapidly over short periods of time, and rounding errors are minimized (Figure 2) [15].
## 3 Wheel Flat Recognition and Positioning Algorithm Based on Multisensor Arrays
### Step II: Where the Abnormal Impacts Occurred  
The purpose of this step is to position the specific location of each abnormal impact. For every impact process identified in Section 3.2, the filtered signals during the corresponding period are extracted and arranged in order, according to the positional relationship of sensors. This gives us a different perspective, i.e., as if observing Figure 17 from the right side.  
We found that there is an obvious correlation between the amplitudes and the impact location. Figure 19b illustrates the filtered signals of all sensors during IMP\({}_{1}\). During this process, the impact location is just over \(S_{5}\), leading to the much higher output of \(S_{5}\) than its counterparts. A similar conclusion can also be drawn according to Figure 19c.  
Inspired by this, a data sequence containing the maximum values of every sensor's outputs during IMP\({}_{j}\) is acquired as follows:  
\[Ex_{j}=\left(Max_{j,1},Max_{j,2},\cdots,Max_{j,N_{s}}\right)^{T} \tag{9}\]  
where \(Max_{j,i}\) (\(i\) = 1, 2,..., \(N_{s}\)) is the maxima of the sensor \(S_{i}\)'s outputs during IMP\({}_{j}\).  
To better describe, a parameter, namely \"flag\", is introduced to represent the impact location. As is shown in Figure 19a, the value of flag is set as 1.5 in the beginning. All the elements of \(Ex_{j}\) are compared successively to determine the impact location. If \(Max_{j,i+1}\) is greater than \(Max_{j,i}\), then, the value of flag is increased by one, meaning that the impact is supposed to occur somewhere behind \(S_{i}\). On the contrary, if \(Max_{j,i+1}\) is less than \(Max_{j,i}\), then the value of flag stays unchanged. After the calculation, the nearest two integers to the parameter flag suggest the location of a certain impact process. For instance, the final value of flag for IMP\({}_{2}\) is 9.5, which means that the most probable location of the second impact process is between \(S_{9}\) and \(S_{10}\).  
Figure 18: Identified impact processes using sliding window method: (**a**) Thresholds and outliers of \(S_{1}\); (**b**) thresholds and outliers of \(S_{2}\); (**c**) thresholds and outliers of \(S_{10}\); and (**d**) the frequency of outliers during every step of sliding.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 3 Proposed DCAN-Net
### Proposed Model Selection
Mathematically, we can respectively express the feature maps for channel attention, spatial attention, and refined attention as follows:  
\[\text{Att}_{\text{C}}^{\text{W}\text{M}\text{C}}=\text{A}_{\text{C}}\Big{(} \text{F}_{\text{M}}^{\text{W}\text{M}\text{C}}\Big{)}\otimes\text{F}_{\text{M }}^{\text{W}\text{M}\text{C}}, \tag{3}\]  
\[\text{Att}_{\text{S}}^{\text{W}\text{M}\text{H}\text{C}}=\text{A}_{\text{S}} \Big{(}\text{Att}_{\text{C}}^{\text{W}\text{M}\text{H}\text{C}}\Big{)}\otimes \text{Att}_{\text{C}}^{\text{W}\text{M}\text{H}\text{C}}, \tag{4}\]  
\[\text{F}_{\text{C}}^{\text{W}\text{M}\text{H}\text{C}}=\text{Att}_{\text{S}}^{ \text{W}\text{M}\text{H}\text{C}}\otimes\text{F}_{\text{M}}^{\text{W}\text{M} \text{H}\text{C}}, \tag{5}\]  
Figure 2: Generic overflow of the proposed DCAN-Net for effective skin cancer classification. In the equations above (Equations (3)-(5)), H, W, and C denote the dimensions of the feature maps, representing the height, width, and number of channels, respectively. A_C and A_CS refer to the intermediate channel and spatial attention module, respectively. We acquired the refined feature maps, labeled F_(M^\"), by integrating the spatial attention feature maps (A_S) with the input feature maps (F_M), as shown in Figure 3. This strategy enabled us to increase the feature representation capability and improve the overall performance of the skin cancer model. ConvNeXt was selected for skin cancer detection due to its outstanding performance, efficiency, scalability, and proven reliability, making it an ideal choice for this medical application. It consistently achieved state-of-the-art results in complex image classification tasks, demonstrating its ability to extract intricate features and discern patterns in dermatoscopic images, which is crucial for identifying subtle visual cues indicative of skin cancer. ConvNeXt's efficiency ensures a low computational overhead while maintaining high accuracy, essential for real-time diagnosis and processing large datasets in healthcare environments. Its extensive validation within the research community underscores its robustness and reliability, while its generalizability across diverse datasets and tasks is advantageous for handling the inherent variability in dermatoscopic images. Despite the suitability of ConvNeXt, a novel model was developed to address the unique demands of skin cancer detection by incorporating a dual-attention mechanism to improve the feature localization and precision. This modification enhances the model's focus on relevant regions within images, further optimizing its performance for skin cancer diagnosis and ensuring that it meets both the accuracy and interpretability requirements for clinical applications.
## 3 The Proposed DoA Estimation Algorithm and Beamforming Scheme  
The proposed algorithm derives real-time DoA information of the UAV from its FSI, and then the calculated DoA values are supplied to the beamformer and used to obtain the optimum beamforming weight vector. Considering the size and power consumption limitation of most commercial UAVs, we assume the target UAV carries a single omni-directional antenna.
## 4 Introducing Samza
### Samza and Kafka  
The close connection between Samza and Kafka may seem restrictive; however, it gives the system some unique characteristics, not commonly found in others systems for stream processing, somehow mirroring the way MapReduce relies on HDFS. For example, Kafka already provides replicated data storage and can be accessed with low latency. Furthermore, it is also possible to store intermediate results thanks to Kafka and then process them in downstream states. The strong relationship between Samza and Kafka allows the processing steps to be loosely coupled with each other. An arbitrary number of subscribers can be added to the output for each step without affecting the processing of the other subscribers. This can be very useful for organizations when multiple teams need to access similar data: Teams can become subscribers to topics created by other teams without causing stress on the system infrastructure. Writing directly to Kafka also eliminates back-pressure problems, i.e., when the load peaks of a data flow are greater than the components' ability to process in real-time, thus potentially introducing stalls and data losses. Being designed to keep data for certain periods of time, thanks to Kafka the components can process it according to their abilities and start over without causing loss of results. As a result, Samza is a good choice for stream processing and, furthermore, it greatly simplifies stream processing by offering low latency performance.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Experimental Setup
#### 5.2.2 Error Metrics  
We evaluate forecasting accuracy using two widely accepted metrics: mean absolute error (MAE) and mean squared error (MSE).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Controlled Parameters**} \\ \hline Optimizer & Adam \\ Number of Training Epochs & 15 (runtime tests), 50 (accuracy tests with early stopping) \\ Optimizer Settings & Default settings (PyTorch) \\ Loss Function & MSELoss (PyTorch) \\ Hardware Platform & 4 \(\times\) NVIDIA A100 GPUs (40 GB each) \\ Distributed Setup & PyTorch DDP with NCCL backend \\ Dataloader Threads & 4 \\ GPU Memory Tracking & PyTorch CUDA memory API \\ Accuracy Metrics & MSE, MAE (see Section 5.2.2) \\ \hline \multicolumn{2}{c}{**Varied Parameters**} \\ \hline Feature Dimension & \{10, 50, 100, 500, 1000, 3000, 5000\} \\ Batch Size & \{100, 300, 500, 700, 1000\} \\ Sequence Length & \{100, 500, 1000, 3000, 5000\} \\ Model Architectures & Transformer, Informer, Autoformer, PatchTST, DLinear \\ Batching Mode & PyTorch Default, Optimized, Optimized In Memory \\ Datasets & Synthetic, ETTh1, ETTh2, ETTm1, ETTm2 \\ \hline \multicolumn{2}{c}{**Model Size (Number of Parameters)**} \\ \hline Transformer & 10 Millions \\ Informer & 11 Millions \\ Autoformer & 11 Millions \\ PatchTST & 8 Millions \\ DLinear & 36,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental parameters and model configurations.  
**Mean Absolute error (MAE)**: measures average absolute deviations:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\]
**Mean squared error (MSE)**: measures average squared deviations, penalizing larger errors more significantly:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\]
**Both metrics evaluate how closely predictions (\(\hat{y}_{i}\)) match the actual values (\(y_{i}\))**.
**Cluster setup**: All experiments were conducted on a computing cluster with A100 40 GB GPUs and a network connection speed of 200 Gbps. We ran each experiment 15 times and took the average of the last 10 runs. The results were carefully averaged across multiple runs with different random seeds to mitigate randomness and provide robust conclusions.
## 4 Simulation Results
### Probability Distributions of Wind Power and Load  
In consideration of the probabilistic variability of both wind speeds and loads, an MCS is conducted to select both the wind power and the system load as input variables. To estimate the Weibull parameters of wind speeds, two years of historical wind speed data were collected from a wind turbine in Seongsan-eup on Jeju Island, South Korea [29]. These data were used as the input for a 2-MW PMSG wind turbine. Our study assumed that wind power generation consumes no fuel. Figure 6 illustrates the cumulative distribution curve of the power output based on a 2-MW wind turbine using an MCS with 1000 samples. The mean value of the wind power output is approximately 1 MW. The normally distributed system load uncertainty is shown in Figure 7. It is considered using  
Figure 5: Modified IEEE-24 bus system with a wind farm.  
the mean value of the period in which congestion occurs more frequently. For statistical purposes, the normal distribution representing the load uncertainty can be obtained with a standard deviation of 5%.
# Maritime Cyber Security: A Global Challenge Tackled through Distinct Regional Approaches
## 2 Literature Review
### Maritime Cyber Crime  
Having looked at the structure of the maritime transport sector and briefly examined its response to the IMO cyber regulation, it is important to understand how the sector's transition to the digital era can, at the same time, introduce cyber related risks. To illustrate, one cyber threat campaign, referred to by researchers as the Daily Show, began as a phishing attack against the shipmaster of a tanker operator [22]. Since the initial infection, which involved key logger malware, the campaign spread around the world, affecting more than 50% of the IMO member nations, and infected not only unknown numbers of vessels and port facilities but also oil/gas, manufacturing, customs agencies, logistics companies, and banks.  
As stated previously, maritime transport companies are constantly adopting new systems, platforms, and technologies to achieve greater levels of capability and efficiency, which introduces new risks to their operations. These risks range from email and IT-office environments to OT systems. The latter is the most important in shipping operations as it affects ships' seaworthiness [10]. It can be argued that, in the maritime transport sector specifically, cyber threats are becoming more frequent and sophisticated, as individual hackers, well-funded and organized criminal networks, nation states, and others target ports, shipping companies, vessels, and shore-side facilities. Predominantly, during the COVID-19 pandemic, according to MTS-ISAC [23], an increase in cyber attacks in shipping took place, with some additional reporting from the Nautical Institute, demonstrating that this increase may have reached up to 900% [24].  
Due to its nature, the cyber domain knows no boundaries. Cyber threat actors are less constrained by geography than connectivity. As shipping is increasingly reliant on connected technologies, every vessel, shore-based facility, and office represents a potential target. Vessels are particularly vulnerable to cyber threats, since ship operators (including management and third-party vendors) regularly access the vessel's networks and related operational systems, both physically and remotely. Unpatched operating systems on vessels with poorly configured networks, open serial ports, and legacy applications represent a considerable asymmetrical risk to shipping and land-based operations (ports, offices, and supply chains) [10].  
The maritime transport industry has been slow to respond to the growing cyber threat [2; 25]. However, this belated response is not granular throughout. The reasons for this anomaly are identified and analyzed in this paper.
## 2 Materials and Methods
### Experimental Design, Data Collection, and Initial Analysis  
In this study, speed ratio (\(u/v\)), depth of operation (DO), and CI were selected as independent parameters, and the specific energy (SPE) requirement as the dependent parameter. The SPE of the system was calculated from the measured draft and torque (Equation (6)) which is the ratio of equivalent power to the actual field productivity of the implement [24; 30; 37; 38].  
\[SPE=\left(\frac{P_{r}}{\mu_{r}}+\frac{P_{d}}{\mu_{d}}\right)\times\frac{10}{v \times w}, \tag{6}\]  
where \(SPE\) is specific energy (kWh ha\({}^{-1}\)); \(P_{r}\) is rotational power (kW); \(\mu_{r}\) is transmission efficiency of the rotational motor (assumed to be 0.80); \(P_{d}\) is drawbar power (kW); \(\mu_{d}\) is transmission efficiency of the drawbar motor (assumed to be 0.80); \(v\) is actual forward speed (km h\({}^{-1}\)); \(w\) is tilted width (m). The weeding tool was tested at three levels of speed ratios (SR: 1.7, 2.2, 3), three depths of operation (DO: 20, 40, 60 mm), three levels of cone Index (CI: 300, 400, 500 \(\pm\) 25 kPa) and in three replicates. For the type of soil-tool interaction study presented here, a full factorial design (FFD) experiment was formulated as it is regarded as a highly robust and widely adopted approach for studying main and interaction effects of independent factors on the response as well as using combinations of those input factors for predictive purposes [24; 25]. Given three typical conditions of CI, \(u/v\) ratio, and DO in the field, a total of 27 combinations were possible. Nonetheless, trials at those 27 input combinations were replicated thrice to obtain 81 datasets that were averaged to 27 datasets for further analysis in the study. The soil bin laboratory experiment is shown in Figure 4. After data collection, initial analysis of variance was conducted to determine the significance of the main and interaction effects of the input parameters on draft, torque and specific energy using the Statistical Package in the Social Sciences (SPSS Inc., Chicago, IL, USA, 1999) at a 5% significance.