esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Implementation of SMOTEENN  
SMOTEENN is a two-step technique used to better handle imbalanced datasets by combining the benefits of oversampling with data cleaning. In the first step, SMOTE generates new samples for the minority class. This approach helps counteract the issue of having too few training instances in the minority class and mitigates the risk of overfitting that can arise with the naive replication of rare samples. However, placing newly generated data in areas near class decision boundaries or within noisy regions can sometimes lower model performance because misaligned synthetic points may blur class distinctions [19].  
Once these synthetic samples have been added, the second step of SMOTEENN applies Edited Nearest Neighbors (ENN) to refine the data further. Here, every instance--regardless of whether it is an original minority sample, a synthetic point created by SMOTE, or a majority-class sample--undergoes a nearest-neighbor check by ENN. If an instance is misclassified when evaluated against its k-nearest neighbors, it is removed from the dataset by ENN. This helps eliminate not only incorrectly placed synthetic points but also outliers and borderline cases from the original data that could obscure decision boundaries. ENN thus serves as a cleaning mechanism, ensuring the final training set is more coherent and less susceptible to noise [21], while SMOTE addresses imbalanced class distributions by increasing the representation of the minority class. ENN safeguards data quality by removing problematic points, making SMOTEENN a powerful tool for improving classification performance on highly skewed datasets.  
Applying Edited Nearest Neighbors (ENN) Undersampling  
1. Define ENN Parameters: Determine the number of nearest neighbors to consider (usually denoted as \(k\)) when applying ENN.
2. Identify Noisy and Borderline Instances: For each sample in the dataset (after applying SMOTE), find its \(k\) nearest neighbors and examine the class labels.  
Remove Misclassified Samples: If a sample's class label differs from the majority of its neighbors' class labels, consider it misclassified or noisy and remove it from the dataset.
4. Clean the Dataset: ENN helps in cleaning overlapping or ambiguous areas in the feature space, reducing the potential for misclassification due to noisy data [7].
## 3 Architecture and System Evaluation
### The Need for Edge Server Offloading  
Our work looks into the concept of offloading the processing of the workload optimally on multiple end devices to achieve faster processing, or high-accuracy object detection of the images. In other words, we tried to utilize all the processing resources that are close to the server to achieve all the benefits provided by edge computing technology. We used the YOLOv5 version of the object detector, which can be easily implemented into Raspberry Pi along with different task-splitting strategies and compression decisions, which attempt to offload the workload to the end devices optimally. The offloading of the workload is done by the edge server using TCP Sockets. We used this API (Sockets) for network communication to ensure reliable data transfers.
# Pharmacokinetics of Marine-Derived Drugs
## 1 Introduction  
Marine organisms represent an excellent source of innovative compounds, having tremendous potential for the development of new drugs. Although marine species are used in traditional medicines in different countries [1; 2; 3; 4; 5; 6], a limited number of marine-derived drugs (such as fish oil, glucosamine, and protamine sulfate) have been used in official medicine for a long time [7; 8; 9]. Recent years have been marked by rapid growth in the discovery of new marine-derived molecules and in the study of their pharmacological activity. About 1200-1500 new compounds are annually reported in the literature [10; 11; 12; 13; 14]. In the last decades, unique compounds that show a broad spectrum of biological activities have been isolated from marine organisms: antiallergic, anti-atherosclerotic, antibacterial, anticancer, anticoagulant, antifungal, antihyperlipidemic, antihypertensive, anti-inflammatory, antioxidant, antiviral, cardioprotective, immunoassay adjuvant, hypocholesterolemomic, and so on [15; 16; 17; 18; 19; 20; 21; 22; 23]. However, a relatively low number of active molecules have been subject to extensive pre-clinical studies, and pharmacokinetic data have been provided for the limited number of candidates. In vivo pharmacokinetic studies are complicated; they require investment, a large number of analyses, and the development of highly sensitive and specific analytical methods. It is important to have reliable and validated methodologies that are able to assess the pharmacokinetics of molecule candidates before proceeding to drug formulation development.  
The pharmacokinetics of marine drugs has attracted increasing interest in recent decades due to its effective and potential contribution to the selection of rational dosage recommendations and theoptimal use of the therapeutic arsenal. In general, pharmacokinetics studies how a drug changes after administration via the processes of absorption, distribution, metabolism, and excretion (ADME) [24].  
Although ADME processes are roughly separated, they are highly integrated phenomena [25]. The key parameters that provide insights into how the drug concentration changes in an organism in course of ADME include the apparent half-life of elimination (T\({}_{1/2}\)), the area under the curve (AUC), clearance (CI), maximum concentration (C\({}_{\text{max}}\)) and time at which C\({}_{\text{max}}\) is observed (T\({}_{\text{max}}\)), median residual time (MRT), the high volume of distribution in the blood (V\({}_{\text{ss}}\)), and bioavailability (F). Distribution half-life (t\({}_{1/2~{}\alpha}\)) and elimination half-life (t\({}_{1/2~{}\beta}\)) are useful for two-compartment models [26].  
This review provides a concise summary of the pharmacokinetic studies of marine-derived active compounds, with a particular focus on their ADME.
## 3 RS Applications in Ocean
### Ocean Tide (OT)
#### 3.5.2 GNSS-R  
GNSS-R provides range measurements, which can be used to remove vertical land motion from tide gauge records [284]. However, many GNSS-R stations are not directly collocated to the tide gauge stations. GNNS time-series have also been applied in models to remove the influence of non-OTL from GPS coordinates [285]. In this regard, the Precise  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Passive} & \multirow{4}{*}{Optical} & Availability of open-access data, useful for all tidal applications, a wide range of spectral and spatial resolutions & Time and weather dependency, low accuracy in estimating water height changes \\  & & NRT data, continuous data, independent from weather, cost-efficient & Sensitive to sea surface reflections, dependency on complementary data, applicable only to tidal channels and OTL \\  & & Accurate estimation of ocean surface topographic changes, independent from weather conditions and time, useful for all tidal applications & Complex processing steps \\ \multirow{2}{*}{Active} & \multirow{2}{*}{Altimeter} & Multilook processing, accurate topographic measurements & Only global surface geostrophic, low track density, limited applications, applicable only to tidal channels and tidal flats \\  & & Relatively higher spatial resolution, accurate estimation of ocean surface topographic changes & Comparatively costly, useful for the data acquisition at optimal tidal and weather conditions, insufficient coverage, applicable only to tidal channels, tidal flats, and tidal wetlands \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different RS systems for OT studies along with their advantages and disadvantages.  
Point Positioning (PPP) technique is used to process the GNSS-R data [286]. OT can cause horizontal and vertical motions of more than a centimeter during a day, which is large enough to be measured with space geodetic techniques. In this regard, [PERSON] et al. [286] investigated OTL displacement parameters with kinematic PPP using the harmonic analysis method from GNSS coordinate series considering the effect of mass loading.  
Moreover, GNSS-R data, along with airborne RS techniques, have been utilized in a semiautomatic method to propose 3D distribution tidal channel maps including their shapes and locations [265]. The tidal channels' volumes and locations were verified by comparing GNNS positioning solutions and the coordinates extracted from UAV images. Moreover, [PERSON] et al. [265] generated 3D safety mapping of tidal channels located in the western coastal area in Korea, where tidal channels were considered the main cause of the summer drowning fatalities. Finally, GNNS technology has been extensively utilized to determine real-time tidal information using tidal level extraction algorithms, which can provide additional values to different aspects of OT studies.
## 2 Acquisition and Characterization of Pore Structure
### Sem  
The SEM method uses an electron beam to induce secondary excitation on the surface of solid samples, obtaining scanning images that reflect the surface structure of the specimen. It can be used for studies related to the surface morphology of rocks, mineral composition, and other aspects. When using scanning electron microscopy, the most commonly used techniques are acquiring secondary electron images (SEI) [38] and backscattered electron images (BSE) [39]. The former provides details of the sample's microscopic morphology, while the latter, combined with energy-dispersive X-ray spectroscopy (EDS), can provide compositional information of the sample.  
This method offers ultra-high imaging resolution and measurement accuracy. Compared to conventional microscopes, it has a greater depth of field, a wider field of view, and produces more three-dimensional images, providing more comprehensive surface information of the sample. However, SEM measurements require sample preparation suitable for observation under the scanning electron microscope, ensuring the surface is flat and free of contaminants, which increases the complexity and cost of the experiment. Additionally, samples need to be observed in a vacuum environment, which limits the types of samples and their applicability.
## 3 Results
### Electromagnetic Properties
Moreover, based on imaginary permittivity analysis of S2-30 wt % sample (Figure 4b), the high conductivity results from the Si(2) silane coupling grafted on the surface of rGO. Next, the dielectric loss of S2 sample is also attributed to the complex interfacial polarization (Si(2)-rGO&PVDF-_co_-HFP, Si(2)-rGO&Fe\({}_{3}\)O\({}_{4}\), and Fe\({}_{3}\)O\({}_{4}\)&PVDF-_co_-HFP) and the lattice defects in graphene structure (Figure 3e), which is beneficial for the EM energy dissipation [16; 40]. Meanwhile, the abundant of wrinkles in Si(2)-rGO would provide a channel for improving the multiple-reflection of EMW (Figure S1b,\(c\), Supplementary Materials). Third, permeability parameters (\(\mu^{{}^{\prime}}\) and \(\mu^{{}^{\prime\prime}}\)) and \(C_{o}\) curve indicated that magnetic loss effect of Fe\({}_{3}\)O\({}_{4}\) NPs in S2 sample mainly resulted from the natural resonance in the range of 2-18 GHz (Figure 5d & Figure S9c, Supplementary Materials). Finally, the synergistic effect of Si(2)-rGO@Fe\({}_{3}\)O\({}_{4}\) and PVDF-_co_-HFP was beneficial to further improving the microwave absorption properties of S2 sample (Figure 4 & Figure S9, Supplementary Materials), and the absorption interference dominates an important position in the high-efficiency microwave absorption performance of S2 sample (Figure S8, Supplementary Materials). Moreover, Table 1 also gives the comparison of microwave absorption performance between those in the literature and the obtained Si-modified-rGO@Fe\({}_{3}\)O\({}_{4}\)/PVDF-_co_-HFP, it is clear from the Table 1 that the prepared Si-modified-rGO@Fe\({}_{3}\)O\({}_{4}\)/PVDF-_co_-HFP in this work exhibits primary advantages by fillers loading, effective bandwidth frequency, and effective thickness.
## 5 The Hybrid Predictor
### Prediction Validation  
**Dataset**: To validate the hybrid model, we use a synthetic dataset (presented in Figure 4) that mimics the Azure 2019 [30] traces over 7 days, with a peak invocation rate of 1200 per second and an average of 300 per second, including some Gaussian noise. The invocations are generated with timestamps at 5-min intervals over 7 days, resulting in 2016 data points. The base workload is modeled using a Fourier series to capture cyclical patterns with additive seasonality and an upward trend. Spikes are modeled with Pareto-distributed magnitudes to simulate 10-100\(\times\) surges: 90% of the spikes are 2-5\(\times\) the base workload, and 10% are 10-15\(\times\) the baseline (resembling extreme surges), with a 5% probability per hour (aligned with Azure's burst frequency) of occurrence. The shape of the spikes is modeled as asymmetric Gaussian pulses to simulate rapid rise and decay. To simulate background noise, zero-mean Gaussian noise (\(\sigma\) = 15 invocations/second) is added to represent small fluctuations, along with impulse noise applied to 1% of the data points, multiplied by 10\(\times\) to simulate outliers.  
**Metrics and Baselines**: We used five metrics to evaluate prediction accuracy: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), R-squared (R\({}^{2}\)), and Peak Error. Our hybrid prediction model was evaluated against several baseline strategies, including ARIMA, LSTM, Prophet, and Moving Average.  
**Results**: The results presented in detail in Figure 5, show that our hybrid model outperformed traditional prediction methods. It achieved a high level of accuracy, with up to a 35% reduction in RMSE compared to standalone ARIMA, and a 22% reduction compared to LSTM. While LSTM is effective at capturing long-term dependencies and complex sequential patterns, it requires a large amount of data to train properly. With limited or noisy data, LSTM is prone to overfitting, which can lead to higher RMSE values. In contrast, the RF component of our hybrid predictor leverages exogenous features to model sudden spikes, reducing dependence on dense temporal data. This leads to lower RMSE values, particularly during irregular events, reflecting better overall accuracy. On the other hand, Prophet uses an additive model (trend + seasonality + holidays) and assumes linearity. It struggles to handle workload surges that do not align with predefined seasonal or holiday patterns. As a result, Prophet tends to smooth out spikes, leading to significant errors during sudden bursts. The Random Forest component's ability to explicitly model such bursts and capture lagged residuals from previous spikes is reflected in the improved Peak Error scores when compared to both LSTM and Prophet.  
Figure 4: Prediction accuracy of the Hybrid Predictor model on the actual synthetic dataset.  
Figure 5: Model performance comparison (a lower result indicates better accuracy, except R\({}^{2}\)).
## 3 LOADng Protocol Overview
### SmartREQ Enhancement for LOADng  
To reduce the number of control messages exchanged during the route discovery process, the SmartREQ enhancement was proposed for LOADng [26]. With the use of SmartREQ, the node should start the route discovery process with an RREQ containing a new smart-rreq flag set as true. Every node that receives a SmartREQ (RREQ message with smart-rreq true) should perform additional processing in the RREQ message handling. After executing all the initial processing, and after verifying whether the message is valid for forwarding, the node should perform the specific processing of SmartREQ. Thus, the node checks whether it owns a route entry on its Routing Set to the message destination with R_next_hop that is different from the previous hop of the received SmartREQ. If this condition is satisfied, the node should transmit the SmartREQ message in unicast to the next address found. The next hop that receives the SmartREQ message should perform the same processing until the message reaches the final destination. If a node does not find a route entry to the SmartREQ destination, the message should be forwarded using broadcast. The destination of a SmartREQ should answer the request by generating a normal RREP. Hence, the SmartREQ enhancement can reduce the number of broadcast transmissions, thereby contributing to  
Figure 2: Flowcharts of LOADng RREQ and RREP control messages processing.  
reducing the control message overhead required to discover a new route and decreasing the network energy consumption.
# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 2 Experiments
### Computer Simulation  
A computer simulation can be effectively used to track the fusion boundary based on the temperature distribution. By calculating the temperature distribution, the weld quality can be estimated indirectly. The heat transfer module of the COMSOL Multiphysics software (ver. 5.3a; COMSOL Inc.) was used and the results were validated based on prior research, where the simulated temperature distribution was compared to the actual temperatures measured by thermocouples and infrared (IR) cameras.  
For the simulation, a blocked domain of dimensions 20 \(\times\) 10 \(\times\) 2 mm was used, and a linear heating source with a Gaussian-shaped laser beam was assumed to be as described in Equation (1).  
\[Q(x,y,z)=Q_{0}(1-R_{c}).\frac{A_{c}}{\pi\sigma_{x}\sigma_{y}}e^{-[\frac{(x-y_ {0})^{2}}{2\sigma_{2}\sigma_{2}}+\frac{(y-y_{0})^{2}}{2\sigma_{2}}]}.e^{-A_{c }z}, \tag{1}\]  
where \(Q_{0}\) is the peak power, \(R_{c}\) is the reflectance, \(\sigma_{x}\) and \(\sigma_{y}\) are the beam waists in the \(x\) and \(y\) directions, and \(A_{c}\) is the skin depth/absorption layer thickness.  
As described in Equation (1), the irradiating laser beam was assumed as a 2D ellipsoidal distribution, with \(A_{c}\) of skin depth with respect to the z-direction. The skin depth can be analytically calculated for the metallic material, but it was found from computer simulation in this research. The reflectance \(R_{c}\) value is also an important value, and it was found by the experimental data analysis by using an integrating sphere.  
To trace the temperature distribution, multiple probe points were assigned to various locations. The simulation conditions and schedules were identical to the experimental ones  
Figure 2: Welding jig configuration.  
which are described in Table 1. As the governing equation for the simulation domain, a general 3D heat transfer equation was used, as described as Equation (2).  
\[\rho C_{p}\bigg{(}\frac{\partial T}{\partial t}+\mathbf{u}_{trans}\cdot\
abla T \bigg{)}+\
abla\cdot(q+q_{r})=-\alpha\mathbf{T}:\frac{dS}{dt}+Q, \tag{2}\]  
\[q=-k\
abla T\;or\;h(T_{ext}-T), \tag{3}\]  
where Equation (3) is a conductive (\(\kappa\)) or convective (\(h\)) heat transfer through the surrounding bodies. In Equation (2), \(\rho\) is the density of the polymers; \(C_{p}\) is the specific heat capacity at constant stress; \(T\) is the absolute temperature; \(u_{trans}\) is the velocity vector of translational motion; \(q\) and \(q_{r}\) are the heat flux caused by conduction and radiation, respectively. \(\alpha\) is the coefficient of thermal expansion, \(S\) is the second Piola-Kirchhoff stress tensor, and \(Q\) contains additional laser heat sources which can be described using Equation (1).
## 4 Simulation and Experimental Results
### DDoS Simulation Attacks
Moreover, we divided the architecture topology shown in Figure 3 hypothetically into two networks: the first half went from host 1 up to host 6, and the second half went from host 7 up to host 10. This allowed us to have a subnetwork completely agnostic from our service. Finally, a further division was possible in the first half of the network; we could have either attackers or targets that were in the same switches as the client-server hosts. In this way, the TCP flow of the DoS attack affected the TCP flow of our service directly. We started with a one-host attack, starting from host 7 and targeting host 10 to see the behavior of the network with an attack outside of the client-server path (i.e., attacking the second half of the network that did not affect our service). The case scenario was very simple and observed the infected host 7, which started producing malicious traffic within the network with the aim of slowing down its performance. H7 sent TCP requests every second, using H10 as a target and increasing the flow rate gradually. The second attack scenario was based on the same topology as the previous case, but in this case, the targeting host was in the first half of the network, which contained our service. H9 was the infected host, and H3 was the target host. Again, in this case, the nodes directly connected to switches S1 and S3 were not involved in the attack. However, H3 was in the middle between H1 and H6. Then, the third attack scenario referred to the same scenario proposed in the second attack but targeted two hosts from the same switch: H9 and H10 as attackers and H3 and H4 as targets. The fourth attack involved both nodes that were directly connected to the same switches as the client and server hosts. In particular, attacks started from H8 and point to nodes H2 and H5, allowing us to watch what happened at the service when an attack involved switches that were directly connected to the service. Finally, the last attack scenario involved up to four hosts, starting from H7 and attacking H10, H5, H3, and H2, involving a host for each switch except the one where the attack started from. All attacks had a rather \"slow\" approach (i.e., they started producing a fairly modest amount of malicious flow and then went on to increase it as they proceeded). All the results of these simulations are shown and discussed in the following section.
# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
## 3 Approach
### Data Set  
We use a dataset that includes trajectories of 489,391 hours from 111 southern elephant seals (_Mirounga leonina_, 32 females and 79 males), and their positions obtained from Argos platform transmitter terminals. All procedures to obtain the data were approved by the respective ethics committees and licensing bodies including, the Australian Antarctic Animal Ethics Committee (ASAC 2265, AAS 2794, AAS 4329), the Tasmanian Parks and Wildlife Service, the University of California, Santa Cruz and the Programa Antartico Brasileiro. The procedures were carried out in accordance with current guidelines and regulations.  
For each trajectory, we use a \(T\)-hour sliding window to extract length-\(T\) trajectory segments. We use tr-\(T\) to represent the set of segments. The majority of the female seals in our dataset were adult seals (two were juveniles) and all of the male seals were juveniles or subadults. We therefore considered representative patterns from gender groups. We randomly selected 80% of the individualsin each group and used their segments for training. We selected the remaining 20% of the seals and extracted their segments for testing. We set \(T=6\) in this experiment.