esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Channel Model and Power Allocation Problem  
The turbulent cells of the atmosphere, known as eddies, work as a prism that will enhance or degrade a propagating optical signal [3]. If an eddy's diameter is almost the size of an incoming beam, it will result in received irradiance \(I_{r}\) fluctuations called scintillation, which is the primary factor of deterioration in the FSO downlink [3]. The scintillation index (SI) \(\sigma_{I}^{2}\) constitutes the normalized ratio of the standard deviation of received irradiance fluctuations to the mean received irradiance [3; 29]:  
\[\sigma_{I}^{2}=\frac{\left\langle I_{r}^{2}\right\rangle-\left\langle I_{r} \right\rangle^{2}}{\left\langle I_{r}\right\rangle^{2}} \tag{1}\]  
where \(\left\langle I_{r}\right\rangle\) represents the mean received optical irradiance in \(\mathrm{W}/\mathrm{m}^{2}\). In satellite downlinks with \(\sigma_{I}^{2}<1\) and elevation angle \(>20^{\circ}\), the atmospheric turbulence is considered weak [10; 11; 12]. In weak turbulence conditions, the optical channel follows the Lognormal (\(\mu,\ \sigma_{I}^{2}\)) with Probability Density Function (PDF) [5]:  
\[f_{I_{r}}(I_{r})=\frac{1}{I_{r}\sqrt{2\pi}\sigma_{I}}\exp\left\{-\frac{\left[ \ln\left(I_{r}/\left\langle I_{r}\right\rangle\right)+0.5\sigma_{I}^{2}\right] ^{2}}{2\sigma_{I}^{2}}\right\} \tag{2}\]  
where \(\mu=-0.5\sigma_{I}^{2}\).  
The optical satellite downlink system under investigation is represented by the real-valued channel matrix \(\mathrm{H}\in\mathbb{R}^{N\times M}\) where \(N\) is the number of ground receivers (Rx), and \(M\) is the number of on-board transmitters (Tx). It is assumed that the covariance matrix \(\Sigma=\mathrm{E}\left[(\mathrm{H}-\mathrm{E}[\mathrm{H}])(\mathrm{H}-\mathrm{ E}[\mathrm{H}])^{\mathrm{T}}\right]\) has zero non-diagonal elements, i.e., linearly uncorrelated channels. In practice, this is achieved by having a distance between the Rx elements larger than [PERSON]'s parameter [1; 29]. The CSI of the channels is denoted as \(\left(h_{ij}\right)_{1\leq i\leq N,\ 1\leq j\leq M}\)and the allocated power as \(P_{ij}(h_{ij})\). The channel capacity is \(C_{ij}\big{(}P_{ij}\big{(}h_{ij}\big{)},h_{ij}\big{)}\). The average (ergodic) channel capacity is \(\mathrm{E}\big{[}C_{ij}\big{(}P_{ij}\big{(}h_{ij}\big{)},h_{ij}\big{)}\big{]}\).  
For independent channels, and a proportionate number of Rx and Tx: \(N=M=L\) and \(i=j=l\), the total average capacity maximization problem is mathematically formalized as follows:  
\[(\mathrm{OP})\underset{\{P_{i}(h_{l}),\forall l\}}{\max}\sum_{l=1}^{L}\mathrm{ E}[C_{l}(P_{i}(h_{l}),h_{l})] \tag{3}\]  
\[\mathrm{s.t.}\quad 0\leq P_{i}(h_{l}) \leq P_{s},\quad\forall l\] \[\sum_{i=1}^{L}\mathrm{E}[P_{i}(h_{l})] \leq P_{av} \tag{4}\]  
where \(P_{s}\) is the maximum safety power allocated to a single channel, and \(P_{av}\) is the total available power constraint.
## 4 Results and Experimental Evaluation
### Experimental Setup
#### 4.1.2 Baseline Methods  
We compared our proposed StochasticTrajectory model against several established baseline methods, which can be categorized into three groups:  
* Deep Learning Models:
* LSTM: Long Short-Term Memory network as introduced by [PERSON] and [PERSON] [31], with the same number of layers and hidden units as our proposed model.  
Figure 4: Visualization of trajectory prediction with uncertainty quantification. The model outputs both a mean trajectory prediction and a distribution of possible trajectories represented through sampling. The uncertainty (shown as dashed ellipses) increases with prediction horizon.  
Attention: An LSTM model augmented with the attention mechanism proposed by [PERSON] et al. [32], but without the specialized weather attention or stochastic components of our approach.
* SimpleRNN: A basic recurrent neural network implementation following the architecture described by [PERSON] [33].
* GRU: Gated Recurrent Unit network as proposed by [PERSON] et al. [34], which uses simpler gating mechanisms than LSTM.
* MLP: Multi-Layer Perceptron as formalized by [PERSON] et al. [35], with equivalent parameter count to our recurrent models.
* Traditional Statistical Models:
* ARIMA: Auto-Regressive Integrated Moving Average model developed by [PERSON] and [PERSON] [36] for time series analysis.
* Physics-Based Models:
* Physics: A kinematic model based on aircraft equations of motion following the approach described by [PERSON] and [PERSON] [37].
* KalmanFilter: State estimation approach using the linear Kalman filtering method introduced by [PERSON] [38].  
All baseline models were optimized using the same training and validation datasets as our StochasticTrajectory model, with hyperparameters tuned through grid search to ensure fair comparison.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 2 Theoretical Formulation
### Geometry and Radiative Properties of SiC Nanowires
In Figure 2, the dielectric constants of SiC are plotted as a real part and imaginary part, respectively for \(B=0\) T and \(B=3\) T. From Figure 2, the results of the Lorentz-Drude model for both real and imaginary parts agree well with data reported by [PERSON] [27]. The real part increases as the angular frequency increases, which shows the usual dispersion characteristics, and decreases as the angular frequency increases, showing a unusual dispersion characteristics. The imaginary part showed a similar trend. Hence, the Lorentz-Drude model is suitable to calculate the di  
Figure 1: Schematic of radiative heat transfer between two-infinite SiC nanowires separated by a vacuum gap \(d\). As the magnetic field was applied, the real and imaginary part had obvious oscillation as shown in Figure 2b, as the magnetic field affected the optical constant of SiC. In general, the application of a magnetic field somewhat affects the optical constant of SiC, especially in the \(x\) and \(y\) directions.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.5 YouCook2 Dataset: Method Performance Overview
This variation in performance may be attributed to differences in the attention mechanisms used, the overall architecture design, or the ability to effectively capture and utilize contextual information in the video description process. Further research and development in attention-based methods are needed to improve their performance on the YouCook2 Dataset and other video description tasks.
# Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
## 2 Methodology
### Data Preprocessing  
The data from the monitoring system sensors are stored in the database in raw form. This means that a simple upload of a parameter of interest over a specific time interval cannot be used as an input data set for model training. Pre-processing and filtering are required. The data format to be used in the vast majority of methods can be represented in general terms as \(DD-MM-YYYY\ HH:MM:SS\ VALUE\), where \(YYYY\) is a four-digit year code, \(MM\) is a two-digit current month code, \(DD\) is a two-digit day code, \(HH\) is a two-digit hour code, \(MM\) is a two-digit minute code, \(SS\) is a two-digit seconds code, and \(VALUE\) is the real number corresponding to the parameter value obtained from the sensor.  
As was mentioned above, the parameters are recorded with a frequency of 1 time per second. Since the resources of the monitoring system are limited, data averaged over 1 min are recorded in the database for each observed parameter. This frequency is also redundant for building an input flow prediction model, so the raw data obtained from the database underwent preprocessing. First, the data containing obvious outliers and anomalous values were discarded. At the next processing stage, the data were averaged over 1 h (resampling). As a result, a sample of data was generated for 19 days of observations, in which each day was represented by a set of 24 values of the observed parameter. The data set corresponding to the first 17 days of observations was used as training data. The remaining data were used to test the quality of the models. All preprocessing was performed using Pandas (Python Data Analysis Library) [38].  
Table 1 shows a data slice of the entire dataset (total of 457 rows) obtained after applying the preprocessing procedure.  
Figure 2 shows the periodicity of the process, with a seasoning period of 24 h. The data in the green area (Test Data) have been used to test the model quality, and the data to the left of the green area (Train Data) have been used to train the model. The task is formulated as follows: to build a predictive model using the available training dataset and use the model to build a forecast of the input stream for the next 48 h. Further steps are reduced to constructing and validating the obtained models using machine learning methods, discussed at the article's beginning.  
The following section will outline the main theoretical provisions and peculiarities of implementing the above methods using Python and open-source libraries.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 4 Waypoint-Based Formation Obstacle Avoidance Algorithm
### Consensus-Based Design for UAV Swarm Formation Control Protocol  
Obstacle-avoidance principles include the A* algorithm and the smallest enclosing convex polygon of a set of points (SECP) decision principle.  
(1) A* algorithm  
The A* algorithm [29; 30] is the most effective direct search method for solving the shortest path in a static road network. We only needed to search for the formation channel between the obstacle circles, and there was no need to search for high-precision track points, so we could use the traditional A* algorithm:  
\[f(o)=g(o)+h(o) \tag{14}\]  
where \(f(n)\) is the cost estimate from the initial state to the target state via state \(n\); \(g(n)\) is the actual cost from the initial state to state \(n\) in the state space; and \(h(n)\) is the estimated cost of the best path from state to the target state. The shortest path could be determined by searching from the starting point according to the valuation function in Equation (14) to the ending point.  
2. SECP determination method Given a plane point set \(A=\{(x_{i},y_{i})|x_{i},y_{i}\in R\}\), we determined the smallest enclosing convex polygon of a set of points \(A_{i}\) (SECP): we connected the points two by two to form a line segment set \(S=\{(A_{i},A_{j})\big{|}A_{i},A_{j}\in A,i\
eq j\}\). If all the other line segments were on the side of the line where a line segment \(S_{convex}\) was located, then the line segment \(S_{convex}\) where this line (e.g., the dotted line in Figure 4) lay was a side of the required convex polygon SECP.
## 2 Generation of Mesostructures and Numerical Models
### Numerical Model  
#### 2.4.1 Finite Element Mesh Generation and CIE Insertion  
The pre-processing functionality in ANSYS is used to establish and mesh the concrete meso-geometric model obtained through the above process. The cement mortar phase is generated by applying the \"overlapping\" and \"subtraction\" Boolean operations. To make the cracking path more real and smooth, the aggregate phase and the mortar phase mesh with triangular solid finite elements. After referring to the relevant literature [2; 24; 43] and trial calculation, the basic size of the element is set to 1 mm. Zero-thickness CIEs are inserted into cement mortar and ITZ to explicitly simulate the initiation and expansion of cracks. The research object of this paper is ordinary concrete, where the strength of aggregate is greater than that of cement mortar, that is, when the concrete is damaged, the aggregates will not crack or damage, so no CIE is inserted into the aggregates.  
The zero-thickness CIEs insertion scheme was developed in tandem with ABAQUS and python code. In ABAQUS, the planar element CPE3 is used to simulate aggregate and cement mortar, and the two-dimensional CIE is the COH2D4 element provided by ABAQUS. The key process of CIEs insertion can be divided into three steps, as shown in Figure 3. Firstly, the node and element information of the initial mesh topology in ABAQUS must be read (Figure 3a). The second step is to copy and re-number the nodes between each pair of the solid elements, except aggregate elements, to obtain the nodes needed to generate CIEs in the identical positions of every two neighboring elements (Figure 3b). It should be noted that in Figure 3, to facilitate the display of the insertion position of the CIEs and the division of the model, the gap is set between the elements. There is no gap between solid elements in the real finite element model. The third step is to search for the common corner nodes of each two adjacent elements. For the 2D triangular elements, there are four common corner nodes between every two adjacent elements, and an appropriate CIE can be generated with these nodes. As shown in Figure 3c, all solid elements except aggregate elements are connected to adjacent elements by CIEs. CIEs are divided into two types. One type is located inside the cement mortar and is denoted as coh_mor (the red four-node elements in Figure 3c). The other is located on the ITZ and is denoted as coh_ITZ (the blue four-node elements in Figure 3c). Finally, a finite element mesh with zero-thickness CIEs is established in ABAQUS.  
Figure 3: The key process of the CIEs insertion. (**a**) Read the node and element information of the initial mesh topology; (**b**) Copy and re-number the nodes; (**c**) Generate the CIEs. Note: The numbers represent the node numbers.
## 3 Proposed Framework
### Animal Sensor Localization
#### 3.4.2 Computation Phase: To Calculate Estimated Distance between MES and WCU  
1. _To calculate the per-hop average distance_
2. _To calculate the per-hop average distance_
3. _The WCU uses the minimum hop count values to other WCUs to estimate the average size of one hop within the network using Equation (1)._ \[HopSize_{i}=\frac{\sum_{j\
eq i}\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y _{j}\right)^{2}}}{\sum_{j\
eq i}h_{ij}}\] (1) where \(x_{i}\), \(y_{i}\) and \(x_{j}\), \(y_{j}\) are the beacon coordinates of WCUs \(i\) and \(j\), \(h\) is the minimum number of hops between beacon WCUs \(i\) and \(j\).
4. _WCU broadcasts its average hop size to the network using controlled flooding to the entire network._
5. _Each MES receives the hop sizes from the nearest WCUs and saves the first received message and transmits it to the neighbors._
6. _To calculate the physical distance between MES and WCU_  
MES computes its estimated physical distance to the WCUs using the received hop size using Equation (2).  
\[d_{mw}=HopSize_{i}\times hop_{mw} \tag{2}\]  
where \(hop_{mw}\) is the minimum hop-count between the unknown \(\text{MES}_{\text{m}}\) and the \(\text{WCU}_{\text{w}}\), \(HopSize_{i}\) is the average hop size that the \(\text{MES}_{\text{m}}\) obtained from the nearest \(\text{WCU}_{\text{w}}\).
## 3 Case Study
### Dataset Construction  
For a specific tunnel, the surface settlement induced during shield tunneling is determined by shield tunneling parameters and the geological conditions of the strata traversed. Accordingly, we adopted the two aforementioned factors that determine surface settlement as features (Figure 7) and took the maximum surface settlement observed during shield tunneling as the target prediction value to construct the dataset.  
The features were selected through the following criteria: (i) Engineering Controllability: While traditional factors like the tail-lining gap and grout pressure are critical to volume loss, their real-time adjustment is often constrained by geological variability and equipment limitations (e.g., grout hardening time and gap measurement delays). In contrast, parameters such as thrust, torque, and advance rate are directly and dynamically controllable during tunneling, making them pragmatic targets for optimization. (ii) Literature-Driven Relevance: Features empirically linked to surface settlement (e.g., thrust and advance rate [4; 15; 17]) were prioritized.  
A total of 19 shield tunneling parameters were considered as features, including the grouting pressure, K-block location, horizontal displacement of the shield head and tail, vertical displacement of the shield head and tail, shield tail gap (top, bottom, left, and right), screw conveyor rotation speed, screw conveyor torque, muck pressure, tunneling speed, volume of excavated earth, cutterhead rotation speed, cutterhead torque, grouting volume, and thrust. The geological conditions included 14 features, including the water table, tunnel burial depth, and stratigraphic parameters (e.g., soil layer thickness, cohesion, internal friction angle, and compression modulus). The definitions of the aforementioned  
Figure 7: Schematic of features affecting surface settlement.  
parameters are provided in Appendix A. Thus, a total of 33 features and 875 ring samples were selected as the original dataset for this study, as shown in Table 2.  
The original dataset was divided into training (80%) and testing (20%) sets to facilitate algorithm training and final performance evaluation. Considering the periodic trends in time-series data, the dataset was segmented into 14 tunneling sections according to the shield start and stop times and further divided into training and testing sets. During cross-validation for algorithm training, a rolling time-series splitting method (i.e., walk-forward validation) was employed to generate the validation set, ensuring the temporal integrity of the time-series data. Table 3 lists all 14 segments with their ring number intervals, segment lengths, and assigned roles (training/testing).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
###### Abstract  
Phased arrays are extensively used in many modern beam-scanning applications such as radar and satellite communications. Electronic beam scanning makes phased arrays an important aspect of modern antenna array systems. This Tutorial aims to promote the basic understanding of the principle and operation of a phased array to general undergraduate students. This paper starts with a discussion on the theory of operation and some basic definitions of antenna parameters followed by derivations of two-element and N-element array patterns and, finally, a five-element array design. The essential hardware based on Phase-Locked Loops (PLLs) as phase controllable RF sources required to build an array and the basic tools required for software and measurement set-up to demonstrate the beam-scanning phased array operation are presented. This enables students to quickly understand and set-up an experiment to verify the phased array operation with commercial off-the-shelf components. In addition, the hardware and software necessary for autonomous control are discussed. By combining basic concepts of phase arrays with a series of simple coding and intuitive laboratory experiments, students can easily understand the Uniform Linear Array (ULA) scanning operation.  
linear antenna arrays; phased array; phase-locked loops; beam steering; tutorial; education +
Footnote †: journal: Electronics Electronics Electronics  
1
Footnote 1: email:
## 3 RPL Overview
### RPL Topology  
The root node starts building a new RPL instance by issuing a (DIO) message. A DIO message provides all information about the DODAG, which can be summarized as follows:  
* A DODAGID: Used to identify the root node and it's corresponding DODAG.
* Nodes use the rank value to calculate their positions in the DODAG with respect to the root node and other nodes.
* Objective Code Point (OCP): Used to identify the Objective Function needed to calculate the DODAG rank depending on the predefined constraints and metrics, which is used within the DODAG construction.
* After receiving the first DIO message, the receiving node adds the address of its sender to its parent list and computes the rank value of this sender according to the proposed objective function.
* Keep in mind that the node's rank must be greater than its parents rank as shown in Figure 2.
* Each node chooses the most preferred parent and forwards the DIO message in a multicast manner with the new rank information to the other nodes. When an RPL node receives a DIO message, it can do one of three things: 1. Discard this message with respect to additional criteria set by RPL mechanism. 2. Analyze the received DIO message to decide if it will maintain its existing location or change it to another lower depending on the path cost and the Objective Function proposed within the DODAG. 3. To avoid occurrence of routing loops, a node must drop all nodes in its parent's list with ranks lower than the new calculated node's rank. After the DODAG has been constructed, each node would have a defined route formed by the most preferred parents to the destination root node, Figure 3 summarizes all steps after receiving a DIO message.  
Figure 2: RPL DODAG Construction.
###### Abstract  
Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At the same time, the fatigue life of automobile parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with the NSGA-II algorithm coupled with entropy weighted grey relational analysis (GRA) for lightweight design of the lower control arm of automobile Macpherson suspension. The dynamic load histories of the control arm were extracted through dynamic simulations of a rigid-flexible coupling vehicle model on virtual proving ground. Then, the nominal stress method was used to predict its fatigue life. Six design variables were defined to describe the geometric dimension of the control arm, while mass and fatigue life were taken as optimization objectives. The multi-objective optimization design of the control arm was carried out based on the Kriging surrogate model and NSGA-II algorithm. Aiming at the uncertainty of design variables, the reliability constraint was added to the multi-objective optimization to improve the reliability of the fatigue life of the control arm. The optimal design of the control arm was determined from Pareto solutions by entropy weighted grey relational analysis (GRA). The optimization results show that the mass of the control arm was reduced by 4.1% and the fatigue life was increased by 215.8% while its reliability increased by 7.8%. The proposed multi-objective reliability optimization method proved to be feasible and effective for lightweight design of a suspension control arm.  
multi-objective reliability optimization; suspension control arm; fatigue life; kriging surrogate model; lightweight design Article
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Integrin \(\alpha_{\text{v}}\beta_{3}\)-Based Targeting  
Integrin \(\alpha_{\text{v}}\beta_{3}\) is a cell adhesion molecule that is overexpressed in most tumor cells for the regulation of angiogenesis and is involved in different stages of cancer development, such as malignant transformation, tumor growth, invasion, and metastasis. The cyclic arginine-glycine-aspartate acid (RGD) peptide was widely considered to target integrin \(\alpha_{\text{v}}\beta_{3}\), showing promising results, in term of higher affinity, selectivity, and stability than linear peptides [91; 138]. For these features, the cyclic RGD was also conjugated to AuNPs and then labeled with different radioisotopes as \({}^{64}\)Cu [41], \({}^{111}\)In [134], and \({}^{68}\)Ga [91; 133; 139] for theragnostic purposes.