esa-sceva/satcom-chunk-collection huggingface dataset:
# Full Life Cycle Evaluation of Stability Pile in High Slope with Multi-Layer Weak Interlayers
## 4 Calculation Model  
The three-dimensional calculation models were based on the two vertical sections, written as Model 1 and Model 2. The constraint of the bottom boundary was a fixed constraint, and those of the left, right, front, and back boundaries were normal constraints.  
The mesh of the two models was divided into very fine element distribution, with a global scale factor of 1.2 and a minimum element size factor of \(5\times 10^{-3}\) by PLAXIS. As a result, Figure 2 shows the larger mesh in the deeper layer and finer mesh among multi-layer weak interlayers. The constitutive relation of the rock and soil mass adopted the ideal elastoplastic model. The Mohr-Coulomb yield criteria can yield similar results compared to other models [18], despite isotropic and elastoplasticity assumptions and the linear behavior of principal stress leading to limitations in this model. In addition, the material parameters (Table 1) were determined using the Mohr-Coulomb failure criterion. The interface strength reduction value was set as 1 in PLAXIS. The pore water pressure and the angle of dilatancy were both ignored.  
The pile is made of C30 concrete, and its material model adopted a linear elastic model. Its mechanical parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Geological Materials**} & **Unit Volume** & **Elastic Modulus** & \multirow{2}{*}{**Poisson’s Ratio**} & **Cohesion (MPa)** & **Internal Friction** \\  & **Weight (g.cm\({}^{-3}\))** & **(MPa)** & & & **Angle (\({}^{\circ}\))** \\ \hline Laterite & 1.75 & 300 & 0.35 & 0.0578 & 13.8 \\ Sand & 1.71 & 80 & 0.33 & 0.03 & 15 \\ Gravel soil 1 & 1.90 & 200 & 0.31 & 0.035 & 20 \\ Gravel soil 2 & 2.50 & 350 & 0.28 & 0.045 & 25 \\ Sandy dolomite & 1.71 & 150 & 0.30 & 0.04 & 22 \\ Fragmented dolomite 1 & 1.90 & 350 & 0.28 & 0.07 & 28 \\ Fragmented dolomite 2 & 2.50 & 500 & 0.25 & 0.10 & 35 \\ Weak interlayer & 2.73 & 20 & 0.35 & 0.03 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of geological materials [42].  
Figure 2: The lateral and longitudinal distribution of the piles in Model 1. Piles are set in the toe, middle, or top of different grades of the slope.  
According to the technical code for building slope engineering [32], the \(F\) of the slope should be >1.35 after pile support. To conduct a full life cycle evaluation, three states of slopes, i.e., the initial state, the state when \(F\) = 1.35 (1.35 state hereafter), and the limit equilibrium state, were analyzed. The results of the initial state are represented by \(F\), those of the 1.35 state by the maximum principal stress and the maximum shear stress when \(F\) = 1.35, and those of the limit equilibrium state by the distribution of the plastic zone.
## 3 Proposed Approach
### Data and Pre-Processing  
A 100,000-sample dataset was collected from publicly accessible GitHub repositories that are dedicated to malware research and education. These include repositories like the JavaScript malware collection master, malware collection master, malware database  
Figure 1: Smart contract life-cycle over Ethereum blockchain.  
Figure 2: Proposed approach design.  
main/master, my malware collection, the malware repo, and trashers malware repo. These sets consist of various types of malware, such as PDF, RTF, XLS, JS, ELF, and JAR files, which are representative of different real-world attack patterns and malicious activities. Benign samples, employed for dataset balance, consist of general forms like EXE, XLS, DOC, RTF, ZIP, 7Z, RAR, JAR, PDF, and ELF. The dataset allows for not just the examination of malware signatures but also knowledge of propagation patterns and frequently attacked vectors, such as those which can attack blockchain-related environments and smart contracts. Yet, it must be acknowledged that the dataset is not specific to blockchain malware; rather, it is meant to facilitate the creation of generalized malware detection models. Compared to other widely used network intrusion datasets such as UNSW-NB15 or CIC-IDS-2017, which focus on traffic level features, our dataset is built upon static file-based malware instances. This approach supports complementary detection views suitable for smart contract environments interacting with external data inputs. Algorithm 1 defines the pre-processing pipeline as follows: data cleaning, removal of duplicates, missing values handling, normalization, and standardization of features, class balance, and feature engineering to improve the correctness. Each file is first converted into an appropriate form of representation like hexadecimal or binary, depending on the type, and then statistical and content-based feature extraction. The resulting dataset is formatted and checked to prepare for further model training and performance assessment.  
```
1:procedurePreprocessDataset(D)
2: Initialize Processed_Dataset \(\leftarrow\emptyset\)
3:for each row \(r\in D\)do
4: Clean the row \(r\)
5: Remove duplicates in \(D\)
6: Normalize values
7: Transform data if necessary
8: Validate the row \(r\)
9: Processed_Dataset.add(\(r\))
10:endfor
11:return Dataset
12:endprocedure
```  
**Algorithm 1** Algorithm for Pre-Processing.  
The following equations describe the main pre-processing operations applied to the malware and benign file dataset:
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 4 Logo-Based Advertisement Detector  
The dataset described in Section 3 encompasses channel logos with heterogeneous characteristics (e.g., form, aspect ratio, size, opacity, color, and animation) which is aligned with the state-of-the-art [14]. Note that there may be logos on screen that are not from the channel, as advertisements, and even certain programs, can have different/their own logo, as depicted in Figure 5. However, there are certain common characteristics that can be exploited. For example, channel logos are typically placed on a corner of the image and are temporally consistent. It may occur that the logo switches sides even during a program, but even in these cases, there is a constant stability of the position before and after the change in position.  
Based on all previous observations and insights, a video-based advertisement detection methodology is defined focusing on the automatic identification and detection of the channel logo for detecting advertisement segments without using prior data about the channel, logos, or streams to be processed. A high-level overview of the proposed approach is presented in Figure 6.  
In the proposed architecture, each input video is first processed by the Logo Locator module, which is responsible for automatically identifying and locating possible locations of the channel logo using an unsupervised approach. The goal is to identify logo candidates as quickly as possible to minimize the delay introduced before conveying decisions on the frames and consequently on program/advertisement segments. A feature extraction methodology is then applied to each of the located regions to form a balanced training dataset of positive and negative samples of each logo candidate. Then, an automatic training procedure is applied to this data in order to obtain object detectors tailored to each of the identified logo candidates. The extraction of features and training of the logo detectors is done by the next module in the processing chain, the Detector Trainer. Finally, the Logo Detection module is responsible for applying the learned detectors in each frame of the input stream. Through this process, a given frame is assigned as belonging to a program if the detector(s) can find a logo on the image, or as advertisement otherwise.  
Figure 5: Advertisement with the brand’s logo in top right corner.  
Figure 6: High-level conceptual view of the advertisement detection algorithm.  
The proposed architecture includes automatic feature extraction, labeling, and an online training procedure; thus, no human interaction is involved. Furthermore, no trained models or data initially required are stored, as each input video is considered independent and the complete training process is performed automatically, from the data extraction to the actual model training.
## 2 Materials and Methods
### Method
#### 2.3.4 VHI Algorithm Optimization  
The steps to calculate the contribution of VCI and TCI to VHI are as follows (Figure 1): Firstly, according to the calculation method of VHI, the parameter \(\alpha\) starts from 0.01, increases gradually to 0.99 by 0.01 steps, and then calculates VHI with different \(\alpha\) values.  
\[VHI_{\alpha}=\alpha\times VCI+(1-\alpha)\times TCI \tag{11}\]  
Secondly, Pearson correlation analysis is carried out between 99 VHI values calculated in step 1 and SPEI values in different time scales. The time scale corresponding to the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**VHI** & **0–0.1** & **0.1–0.2** & **0.2–0.3** & **0.3–0.4** & **0.4–1** \\ \hline Drought & Extreme & Severe & Moderate & & \\ severity & drought & drought & drought & Mild drought & No drought \\ \hline \hline \end{tabular}
\end{table}
Table 2: VHI drought classification [6; 12].  
maximum correlation coefficient is taken as the optimal time scale. The Pearson correlation coefficient (R) is calculated as  
\[R=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum_{i=1 }^{n}{(x_{i}-\overline{x})^{2}(y_{i}-\overline{y})^{2}}}} \tag{12}\]  
Here, \(R\) varies between \(-1\) and \(1\). Suppose \(x\) and \(y\) represent two different drought indicators. Then, \(x_{i}\) and \(y_{i}\) are the values of \(x\) and \(y\) in the \(i\)-th year, \(n\) is the length of the dataset, \(\overline{x}\) and \(\overline{y}\) are the averages of \(x\) and \(y\).  
Thirdly, the Pearson correlation analysis is carried out on 99 VHI values calculated in step (1) and SPEI of the optimal time scale, and the \(\alpha\) value corresponding to the maximum correlation coefficient is taken as the optimal contribution value.  
\[\alpha_{opt}=maxR(VHI_{\alpha},SPEI03) \tag{13}\]  
Finally, VHIopt is calculated as  
\[VHI_{opt}=\alpha_{opt}\times VCI\times 0.01+\left(1-\alpha_{opt}\right) \times TCI\times 0.01 \tag{14}\]
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 3 Motivation and Methodology for Empirical Models of MAC Protocol Performance
### Relevant Metrics and Parameters and Simulation Setup
The speed of all the platoons is set to 100 km/h and the length of each car is 4 m long. The 16 platoons are re-arranged spatially according to the number of lanes under analysis (e.g., single line if a single lane is used; three or four platoons/lane if all five lanes are considered). All protocols operate a beacon interval \(T_{\text{round}}\) of 100 ms, an adequate value for a platooning application [26]. The MAC service data unit (MSDU) is always 200 bytes. We used the physical layer (PHY) and MAC models of IEEE 802.11p proposed in [27], using a bitrate of 6 Mbit/s that is suited for demanding safety related applications [28]. Furthermore, we disabled the switching between control channel (CCH) and service channel (SCH), using only the CCH, and all beacons use the same access category \((AC_{\_}V1)\). We use a free-space path loss model with an \(\alpha\) value of 2.0. We do not consider more complex path loss models, obstructions, or fading effects to avoid introducing artefacts caused by complex propagation phenomena in the network-level metrics (we expect to address this in future work). Leaders transmit with power set to 100 mW (20 dBm). With the referred propagation model and equal omni-directional antennas, this power grants a communication range of approximately 1300 m. Each simulation experiment reproduces 30 s of real-world activity. Platoons start their periodic beaconing rounds with an independent and random delay between 10 ms and 1 s, drawn from a uniform distribution. We vary the number of lanes between 1 and 5, using four lanes as default. The size of the platoons is varied from 2 to 10 cars in either heterogeneous or homogeneous way, with the default value of ten cars with heterogeneous platoon sizes. The followers transmit with power ranging from 0.1 mW (\(-10\) dBm) to 5 mW (7 dBm) granting approximately 30 m and 254 m of communication range, respectively, under the referred propagation model and equal omni-directional antennas. The default transmit power value is 1 mW (0 dBm). Table 1 lists scenario parameters and Table 2 summarizes all communication-related parameters. In both tables, default values are underlined.
We use an extended cavity laser composed by an AlGaInAs-based multiple quantum well (MQW) gain-chip evanescently coupled to a passive Si ring-based Vernier filter. Bragg mirrors with a spectrally broad transmission are used to provide the optical feedback in the laser cavity. A second III-V gain-chip is evanescently coupled to the Si waveguide at the laser output to work as a compact SOA booster. The total footprint area of the co-integrated III-V/Si laser-SOA system is 0.5 mm\({}^{2}\). The widely tunable laser presents a threshold current as low as 10 mA, has a tuning range of 50 nm centered at 1549 nm, and shows a laser linewidth of 259 kHz under high current injection. The compact SOA booster generates an on-chip output power exceeding 13 dBm for an on-chip optical input power of 3 dBm, thus offering a 10 dB net gain. The co-integrated widely tunable laser-SOA booster device provides 10 dBm of power in a single mode fiber in butt-coupling configuration. This work puts forward the co-integration of low-threshold widely tunable lasers and high-power optical amplifiers in a heterogeneous III-V-on-SOI platform to develop highly compact and performant on-chip light sources.
## 2 Theoretical Maximum Efficiency  
High rotor efficiency is desirable for increased wind energy extraction and should be maximised within the limits of affordable production. Energy (\(P\)) carried by moving air is expressed as a sum of its kinetic energy [Equation (1)]:  
\[P=\frac{1}{2}\rho AV^{3} \tag{1}\]  
A physical limit exists to the quantity of energy that can be extracted, which is independent of design. The energy extraction is maintained in a flow process through the reduction of kinetic energy and subsequent velocity of the wind. The magnitude of energy harnessed is a function of the reduction in air speed over the turbine. 100% extraction would imply zero final velocity and therefore zero flow. The zero flow scenario cannot be achieved hence all the winds kinetic energy may not be utilised. This principle is widely accepted [4; 5] and indicates that wind turbine efficiency cannot exceed 59.3%. This  
Figure 1: Alternative configurations for shaft and rotor orientation.  
parameter is commonly known as the power coefficient \(Cp\), where max \(Cp=0.593\) referred to as the Betz limit [6]. The Betz theory assumes constant linear velocity. Therefore, any rotational forces such as wake rotation, turbulence caused by drag or vortex shedding (tip losses) will further reduce the maximum efficiency. Efficiency losses are generally reduced by:  
* Avoiding low tip speed ratios which increase wake rotation
* Selecting aerofoils which have a high lift to drag ratio
* Specialised tip geometries  
In depth explanation and analysis can be found in the literature [4,6].
# Selection Methodology of an Electric Actuator for
## 4 Conclusions  
A comprehensive study for solving the kinematics of landing gear have been presented in this work and various parameters for the actuator have been calculated theoretically as well as using MSC ADAMS(r). The results for both of the methods were found to be in good agreement with each other. The landing gear was retracted, as per the design constraints that are given in Table 1. Various parameters for the actuator, like stroke, translational velocity, and force were calculated. The maximum force and power requirement came out at different time during retraction cycle. It is concluded that maximum velocity, force and power do not occur at same time during landing gear retraction cycle, since there are other factors that are also contributing to the power required for the retraction. When landing gear reaches its maximum retracted position the force required is maximum, because maximum gravitational force is acting on the landing gear at that time due to its horizontal position, while the required power is not maximum, because the velocity of landing gear actuation is not maximum, rather it is close to zero as the landing gear is about to complete its actuation cycle. The detailed design phase of actuator must consider all of the parameters calculated in this work. Moreover, a methodology for the drop test on MSC ADAMS(r) has been proposed in the paper, after modelling the oleo-pneumatic, which resulted in the calculation of locking loads on actuator. The actuator design must take into consideration the component of impact force along the axis of actuator. After thorough analysis, as presented in this paper, it is concluded that the developed retraction kinematics meet all of the requirements of retraction cycle and safety for the present case study of Nose Landing Gear and the actuation can be carried out with an electrical actuator. The design has been successfully tested on a scale down model. The proposed landing gear actuation system will increase the aircraft performance with a substantial reduction in drag through retraction. Moreover multibody dynamic software are not only helpful in solving the kinematics of landing gear but they are also useful in modeling the behaviour of landing gear strut during impact. The analytical equations that are derived in this work are helpful in for solving the kinematics of any similar mechanisms, and both the computational and analytical method utilized in the paper verify the authenticity of each other, since the error recorded between them was almost negligible.  
Figure 14: Actuator locking load evaluation during impact simulation. This figure shows variation of axial force with time acting on actuator during impact simulation.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 4 Conclusions  
This paper introduces a VoI-based trajectory planning approach for data collection in UASNs, where a single AUV collaborates with a USV to collect data from multiple CHs distributed over an uneven seafloor. The approach formulates an optimization problem aimed at maximizing the total residual VoI at mission completion, based on a VoI model that captures both data importance and timeliness. This problem is solved using a forward DP algorithm, which jointly optimizes the AUV's trajectory and the USV's start and end positions while satisfying the AUV's kinematic constraints. Simulation results demonstrate promising performance in terms of total residual VoI and overall system efficiency compared to standard baselines. However, performance declined in larger and deeper environments with increased communication delays. These findings emphasize the need for enhanced system adaptability and motivate future extensions involving multi-AUV collaboration. Furthermore, accounting for sensing and transmission uncertainties is critical to accurately modeling real-world underwater conditions. Future work should also consider benchmarking the proposed method against state-of-the-art techniques to further validate its effectiveness.  
Conceptualization, T.S.A. and A.V.S.; methodology, T.S.A. and A.V.S.; software, T.S.A.; validation, T.S.A.; formal analysis, T.S.A.; investigation, T.S.A.; resources, T.S.A.; data curation, T.S.A.; writing--original draft preparation, T.S.A.; writing--review and editing, A.V.S.; visualization, T.S.A.; supervision, A.V.S.; project administration, A.V.S.; funding acquisition, A.V.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Australian Research Council by the grant DP190102501.  
The data presented in this study are available upon request from the corresponding author, as they are not publicly accessible due to privacy restrictions.  
The authors declare no conflicts of interest.  
Figure 8: Comparison of the proposed forward DP (green solid line) and the TSP-based (red dash-dot line) methods: (**a**) 3D trajectories and USV positions; (**b**) 2D x–y projection.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 2 Methodology  
### Gravity-Gradient Data  
Consider a right-handed Cartesian coordinate system with the \(x\) and \(y\) oriented horizontally, pointing north and east, respectively, and the \(z\)-axis pointing vertically downward. Gravity-gradient data are commonly defined in terms of second derivatives \(g_{i}^{\alpha\beta}\), \(\alpha,\beta\in\{x,y,z\}\), of the gravitational potential at the \(i\)th observation point \((x_{i},y_{i},z_{i})\), \(i\in\{1,\ldots,N\}\). In the source-free region, these second derivatives form a symmetric \(3\times 3\) gravity-gradient tensor given by  
\[\mathbf{G}_{i}=\begin{bmatrix}g_{i}^{xx}&g_{i}^{xy}&g_{i}^{xz}\\ g_{i}^{xy}&g_{i}^{yy}&g_{i}^{yz}\\ g_{i}^{zz}&g_{i}^{yz}&g_{i}^{zz}\end{bmatrix}\,, \tag{1}\]  
with trace equal to zero, i.e., \(g_{i}^{xx}+g_{i}^{yy}+g_{i}^{zz}=0\), due to Laplace's equation. This means that there are five linearly independent components \(g_{i}^{xx},g_{i}^{xy},g_{i}^{xz},g_{i}^{yy}\), and \(g_{i}^{yz}\)[35].  
The two most common airborne systems that can provide gravity-gradient data are the Full Tensor Gradiometry (FTG) system and the single axial Falcon airborne gravity gradiometer (AGG) system. The FTG system measures five linearly independent components of the gravity-gradient tensor (i.e., \(g_{i}^{xx},g_{i}^{xy},g_{i}^{zz},g_{i}^{yy}\) and \(g_{i}^{yz}\)). The Falcon AGG system measures the two horizontal differential curvature gradients \(g_{i}^{xy}\) and \(g_{i}^{zw}=(g_{i}^{yy}-g_{i}^{xx})/2\)[36].
## 5 Experimental Evaluation
### Impact of Hyperparameters on MAE and RMSE
#### 5.6.2 Experimental Setup  
To systematically investigate the optimal model architecture and hyperparameter configuration, this study analyzes the influence of hyperparameters through a dual-perspective approach:  
1. Number of GCN Layers: We systematically evaluate the effectiveness of graph feature extraction at varying depths by adjusting the number of graph convolutional layers \(L\). The experimental investigation encompasses a range of \(L\in\{1,2,3,4,5\}\).
2. Hidden Layer Dimensions: We analyze the influence of feature space capacity on model performance by modifying the dimensionality \(d\) of GCN hidden layers. The experimental investigation encompasses a range of \(d\in\{8,16,32,64,128\}\).  
In this experimental investigation, we maintained consistent values for key hyperparameters, including learning rate, batch size, and dropout rate, across all experimental sets. The evaluation was conducted on two distinct datasets: the garlic and scallion datasets. To effectively visualize the influence of hyperparameters on model performance metrics (MAE and RMSE), we generated three-dimensional visualization plots. In these plots, the X-axis represents the number of graph convolutional network (GCN) layers, the Y-axis denotes the dimensionality of hidden layers, and the Z-axis displays the corresponding MAE or RMSE values.