esa-sceva/satcom-chunk-collection huggingface dataset:
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 4 Experiment Results and Discussion
### Implementation Details  
The hyperparameters and data augmentation used in the experiments are listed in Table 1. The backbone network (ResNet-50) was pretrained using the ILSVRC 2012 dataset [28]. Specifically, the optimizer used stochastic gradient descent (SGD), with a momentum and weight decay of 0.9 and 0.0001, respectively. PASCAL VOC and MS COCO datasets' batch size was 32 and 8. The input resolution of the proposed method for each dataset was \(512\ \times\ 512\) and \(800\ \times\ 1333\). The initialized learning rate (LR) was set to 0.02. We used a multistep learning rate (multistepLR) as the LR scheduler for each dataset. PASCAL VOC decreased LR by 0.1 per iteration, 2K and 2.1K. In addition, MS COCO reduced LR by 0.1 at every 60K and 90K. The data augmentation for the training to DLMFCOS included color jitter, random rotation, and random resized crop. Table 2 shows the hardware and software environments.  
As an evaluation matrix, we used average precision (AP) [29], a standard metric for quantitative comparison of object detection. AP calculates the mAP of the recall value from the precision-recall curve (PR curve). In addition, the computational cost is measured using FLOPs and params. Equations (15)-(18) show the _precision_, _recall_, _AP_, and _mAP_[30].  
\[Precision=\frac{TP}{TP+FP}, \tag{15}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{16}\]  
\[AP=\frac{1}{11}\sum_{r\in{0.0,\ \ldots,1.0}}{\rho_{interp}(r)}, \tag{17}\]  
\[mAP\ =\frac{1}{N}\sum_{i=1}^{N}{AP_{i}}. \tag{18}\]  
where \(TP\), \(FP\), \(FN\), \(r\), \(\rho_{interp}\), \(N\), and \(AP_{i}\) denote the true positive (TP), false positive (FP), false negative (FN), recall, the precision value of each recall, the total number of classes, and AP value of the \(i\)th class, respectively.
# Exploration of Swarm Dynamics Emerging from Asymmetry
## 4 Measures to Quantify the Swarm Dynamics
### Fitness Function Candidates
#### 4.3.3 Fitness Function C  
As one of the most important features of SC, different types of agents are allowed to exist in a swarm, implying the interactions among agents can be asymmetrical. Unlike other swarm system models, the perception ranges \(R\) are not always consistent between any pair of agents \((i,j)\), making such a situation possible to occur that the motion of one of them (agent \(i\)) is affected by the other (agent \(j\)), but not necessarily vice versa. The topological connectivity could be relevant to the emergence of a life-like behavior of agents. From terminology of graph theory, the network topology of interactions can be modeled as a directed graph, whose adjacency matrix is asymmetrical. In preparation, we need to define the in-degree and out-degree functions.  
* In-degree (\(K^{-}\)): The number of agents that agent \(i\) perceives. In other words, how many agents affect the motion of agent \(i\).
* Out-degree (\(K^{+}\)): The number of agents that perceive agent \(i\). In other words, how many agents are affected by agent \(i\).  
A numerical example is illustrated in Figure 6. Consider a swarm heterogeneous of 4 agents. In the case shown in (a), agent 1 is perceived from only agent 3, which means the in-degree of agent 1 is 1. By contrast, agent 1 perceives agent 2, 3 and 4. This means the out-degree of agent 1 is 3. The relationship of perception is visualized in (b). The degrees for the other agents can be computed in the same way. The result is shown in (c).  
In addition, as the degrees \(K^{+}\) and \(K^{-}\) are dependent on both time (\(t\)) and agent ID (\(i\)), they can be formally written as:  
\[K^{-}(t,\ i)\ \text{and}\ K^{+}(t,\ i), \tag{10}\]  
respectively. After every simulation, an adjacency matrix \(A=[a_{ij}]\) was computed for each timestep. Let \(a_{ij}=1\) if agent \(i\) \"perceives\" agent \(j\), otherwise \(a_{ij}=1\), and vice versa.  
Based on the above preparation, a fitness function could be defined as follows.  
\[\Delta K(t,i) = \left|K^{+}(t,i)-K^{-}(t,i)\right| \tag{11}\] \[\phi = \frac{1}{TN^{2}}\sum_{t=1}^{T}\sum_{i=1}^{N}\Delta K(t,i) \tag{12}\]
## 2 Two Models for Low-Speed Buoyant Flows
### Boussinesq Approximation  
Let us expand the density in a Taylor series with respect to temperature variations. Neglecting higher order terms (see for instance [4]):  
\[\rho(T) \approx \rho_{0}+\left.\frac{\partial\rho}{\partial T}\right|_{p}\Delta T\] \[= \rho_{0}-\rho_{0}\alpha_{p}(T-T_{0}),\]  
where \(\rho_{0}\) is the density at the reference temperature \(T_{0}\), and \(\alpha_{p}\) is the thermal expansion coefficient at constant pressure.  
In this model, the fluid is assumed incompressible and, therefore, of constant density \(\rho_{0}\), and the effect of density variations is accounted for in the source term. In particular, the gravity-forcing term is substituted by the buoyant forces with respect to equilibrium:  
\[\mathbf{f}_{m}=-g\alpha_{p}(T-T_{0}), \tag{5}\]where \(\mathbf{g}\) is the gravity acceleration.  
Note that, if \(T>T_{0}\), the fluid density decreases with respect to \(\rho_{0}\), so the buoyant force works against \(\mathbf{g}\), and the fluid particle lifts up, whereas if \(T<T_{0}\), the fluid density increases, so the buoyant force acts in favor of \(\mathbf{g}\), and the particle drops down. Also note that in this model \(p\) is the departure from the equilibrium hydrostatic pressure [4].  
Thus, in the system equations (2) we set  
\[\rho = \rho_{0} \tag{6}\] \[\rho f_{mi} = -\rho g_{i}\alpha_{p}(T-T_{0}) \tag{7}\]  
The entropy production \(\mathcal{P}\) (per unit time and volume) of the Boussinesq terms in the above system of equations can be calculated as [23]  
\[\mathcal{P} = \frac{\mathbf{u}}{T}\cdot\text{mom. eq. term}+\frac{-1}{T}\text{ energy eq. term} \tag{8}\] \[= \frac{\mathbf{u}}{T}\cdot\rho f_{m}-\frac{1}{T}\rho f_{m}\cdot\mathbf{u}. \tag{9}\]  
1. Note that the entropy production of the buoyant terms in the Boussinesq model should cancel, because gravity is a conservative field. Indeed, this is satisfied if the same buoyancy model is applied to the momentum and energy equations. This is the case of the model presented in this paper. However, there are implementations that do not respect entropy production [24; 25]. This is an important trait for physical and numerical reasons. From a numerical point of view, methods can inherit stability from the discrete second law of thermodynamics. From a physical point of view, if the system of equations does not respect the second law of thermodynamics, its solutions are physically wrong [23].
2. Note, with this set of equations, the velocity divergence does not appear explicitly in the total energy equation, and, therefore, this model does not suffer the entropy production pitfall of other methods, in which the expansion power appears in the temperature or internal energy equation [24; 25]. Indeed, if this is the case, on the one side, the velocity divergence of the Boussinesq model is zero, whereas the real divergence does not cancel due to the changes of density.
3. When using the internal energy equation or the temperature equation, the viscous dissipation and power expansion terms should be retained for consistency. As mentioned above, there are models that do not retain these terms.
# Design and Mechanical Properties of Negative Poisson's Ratio Structure-Based Topology Optimization
## 3 Structural Geometric Construction and Equivalent Elastic Properties  
The variable density method enabled topology optimization to design a negative Poisson's ratio cell structure. To investigate its mechanical properties accurately, a suitable geometric structure was extracted for modeling, as illustrated in Figure 8. In this figure, the transverse edge of the unit cell is represented by 2a, and the vertical edge of the unit cell is 2h. Meanwhile, the oblique edge of the unit cell is denoted with b, and the concave edge is represented with c. Additionally, the length of the connecting rod that joins the horizontal cells is called p. The acute angle between the oblique edge and the transverse edge is denoted with \(\alpha\), while the acute angle between the concave edge and the connecting rod is denoted with \(\gamma\).  
This paper sets the thickness at 0.65 mm and connecting rod thickness at 5 mm, taking into account the accuracy issues associated with finite element analysis and 3D printing. The focus of this paper is to investigate the influence of transverse edge a, oblique edge b, concave edge c, vertical edge h, and two angles \(\alpha\) and \(\gamma\) on the equivalent elastic modulus and Poisson's ratio. Furthermore, due to cellular connection problems, geometric parameters are subjected to constraints imposed using Equation (13) to ensure the rationality and coordination of the overall structure.  
\[c\cdot\cos\gamma<a+b\cdot\cos\alpha \tag{13}\]
## 3 Model Checking CTL Properties
### The Model Checking Problem  
We start with the LQMC \(\mathfrak{M}\) and the property \(\varphi\) we want to verify. The property is usually given as a QCTL state formula \(\varphi\), that is, a formula expressible according to the following grammar:  
\[\varphi ::=a\mid\varphi\land\varphi\mid\
eg\varphi\mid\mathbb{Q}_{\
eg \mathcal{E}}[\psi]\] \[\psi ::=\mathbf{X}\varphi\mid\varphi\mathbin{\mathbf{U}}^{\leq k} \varphi\mid\varphi\mathbin{\mathbf{U}}\varphi,\]  
where \(a\in AP\) is taken from the given finite set of atomic propositions, \(\sim\in\{\lesssim,\gtrsim,\simeq\}\), \(\mathcal{E}\in\mathcal{S}^{\mathcal{I}}(\mathcal{H})\), and \(k\in\mathbb{N}\) is a bound. The \(\psi\) formulas \(\mathbf{X}\varphi\), \(\varphi_{1}\mathbin{\mathbf{U}}^{\leq k}\varphi_{2}\), and \(\varphi_{1}\mathbin{\mathbf{U}}\varphi_{2}\) are usually referred to as path formulas. In addition to the usual Boolean operators \(\land\) and \(\
eg\), the QCTL logic makes use of the temporal operators Next \(\mathbf{X}\), Until \(\mathbf{U}\), and bounded Until \(\mathbf{U}^{\leq k}\). We freely use the usual syntactic sugar like the Boolean operators **false**, **true**, \(\lor\), \(\Rightarrow\), and \(\Leftrightarrow\) and the temporal operator Finally/Eventually \(\mathbf{F}\varphi=\textbf{true}\mathbin{\mathbf{U}}\varphi\). In literature, it is possible to find the temporal operators \(\bigcirc\) and \(\lozenge\), corresponding to \(\mathbf{X}\) and \(\mathbf{F}\), respectively.  
The semantics of QCTL is given by means of the satisfaction relation \(\models\), defined as follows. For state formulas, given a state \(s\in S\), we have  
\[s\models a \text{if }a\in L(s);\] \[s\models\varphi_{1}\land\varphi_{2} \text{if }s\models\varphi_{1}\text{ and }s\models\varphi_{2};\] \[s\models\
eg\varphi \text{if it is not the case that }s\models\varphi;\text{ and}\] \[s\models\mathbb{Q}_{\
eg\mathcal{E}}[\psi] \text{if }Q_{s}^{\mathfrak{M}}(\{\sigma\in\operatorname{Path}^{ \mathfrak{M}}\mid\sigma\models\psi\})\sim\mathcal{E}.\]  
For path formulas, given a path \(\sigma\in\operatorname{Path}^{\mathfrak{M}}\), we have  
\[\sigma\models\mathbf{X}\varphi \text{if }\sigma[1]\models\varphi;\] \[\sigma\models\varphi_{1}\mathbin{\mathbf{U}}^{\leq k}\varphi_{2} \text{if }\exists 0\leq n\leq k:\sigma[n]\models\varphi_{2}\text{ and }\forall 0\leq i<n:\sigma[i] \models\varphi_{1};\] \[\sigma\models\varphi_{1}\mathbin{\mathbf{U}}\varphi_{2} \text{if }\exists n\in\mathbb{N}:\sigma[n]\models\varphi_{2}\text{ and }\forall 0\leq i<n:\sigma[i] \models\varphi_{1}.\]  
The model checking problem for LQMCs against a QCTL state formula \(\varphi\) is formalized as follows.  
Definition 3: Let \(\mathfrak{M}\) be an LQMC, \(s\in S\) be a state, and \(\varphi\) be a QCTL state formula. The model checking problem for \(\mathfrak{M}\) against \(\varphi\) asks to verify whether \(s\models\varphi\) holds.
## 4 Numerical Results:
### Dielectric Waveguide Facet
Figure 4 exhibits a fair agreement with FDTD method and to a less degree with the Transfer Matrix Method (TMM) which fails to predict correctly the high reflectivity coating [24, 25]. [PERSON] et al. [25] pointed out that the reflectivity calculated by the FDTD method deteriorates at long wavelengths (beyond 1 \(\upmu\)m) due to the propagated field along the coating layers. This explains the disparity between the results of the FFT-BPM and the FDTD at long wavelengths. Figure 4: The power reflectivity as function of the wavelength of a multilayer coating made of 6 layers each having a thickness \(\lambda\)/4. Figure 3: Power reflectivity of single layer antireflection coating as function of coating thickness \(u\). The minimum at \(u=0.206\)\(\upmu\)m agrees with accurate previously published results.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion  
This section presents the results of the proposed metasurface as well as the parametric analysis of the design variables in the CST software. While the experimental testing in the setup, designed on the operating principle of placement of the metasurface sample perpendicular to the direction of radiation of the two antennas, to measure the transmission characteristics is also investigated here.
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 5 Predicted Performances  
### Quantity and Quality of Detectable Targets  
Before the radar design was implemented, extensive simulations of its performance compared to the requirements in the statement of work were carried out. As expected, the radar was capable of detecting and tracking objects that fly through the visibility field of the _Tx_ antenna. The quantity of these targets was predicted using the ESA's \"PROOF-2009\" software as a simple simulation environment. Spreadsheet processors were used to additionally post-process and plot the obtained data. The signal characteristics of the planned CW radar were allocated to the settings of the corresponding low duty factor pulsed radar in terms of detection capabilities because PROOF-2009 is intended for the latter type of radar [22]. The simulation mode was set to \"Statistical\". The precise parameters of the Cheia topographical coordinates and the mandatory radar characteristics were introduced [23; 24]. The simulations had a start time of 09:00 UTC, 30 March 2018, and the duration was fixed to 24 h.  
Figure 6 illustrates the lowest detectable target size relative to the orbital altitude in the CW operation. Figure 7 illustrates the outcomes of the PROOF-2009 modelling, presenting the number of detectable targets for 24 h at varying altitudes in the CW operation.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 2 Related Work
[42] applied FFDNet for VLC-MIMO estimation, effectively modeling high-dimensional nonlinearities, yet the convolution-heavy design limits low-latency performance. [PERSON] et al. [30] implemented adaptive predistortion using support vector regression for LED nonlinearity, which, while effective for VLC, remains inapplicable to RF domains. A comprehensive survey by [PERSON] et al. [43] reviewed DL approaches in VLC systems, highlighting both their strengths and the persistent gap in real-time, dual-domain estimation capabilities. [PERSON] et al. [44] explored CNN-based architectures that integrate spatial, frequency, and temporal correlations for mmWave massive MIMO estimation, approaching MMSE performance but with substantial memory and pilot overhead requirements. [PERSON] and [PERSON] [45] approached nonlinear channel estimation via Bayesian nonlinear filtering, introducing sequential gain map prediction for cooperative networks. Although offering accurate MMSE tracking, this method assumes ideal grid-based models. Both [PERSON] et al. [46] and [PERSON] et al. [47] investigated decentralized DL-enhanced estimation frameworks for mmWave and MIMO WSNs under imperfect CSI, demonstrating robust hybrid transceiver designs but still requiring processing power beyond that of embedded WSN nodes. In summary, while traditional ML, and DL approaches have provided valuable advances in nonlinear channel estimation, many fall short regarding hybrid adaptability, scalability, and computational efficiency, particularly within dynamic VLC-RF WSN environments. To address these multidimensional challenges, our proposed Hybrid VLC-RF channel estimation model introduces a lightweight, hierarchical, tree-based approach capable of real-time estimation, hybrid signal integration, and efficient computation, building directly upon the limitations identified across the current literature.
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 2 Materials and Methods
### ECG Signal Preprocessing
#### 2.2.3 Heart Beat Segmentation  
The aim of this subsection is to segment the heartbeat; after the detection of peaks, this study used the \(\mathbf{R^{nXm}}\) matrix for selecting the R-location as a reference point. After determining the position of R-peak, 200 samples or around 0.52 s points were separated in the window, which is considered a single heartbeat. The segmented area based on the QRS complex is described in Figure 4.The start point and end point of the highlighted beat represented as Q wave and S wave respectively, whereas the peak area is defined as R wave. The combination of Q, R and S wave represents ventricular depolarization of the heart.  
However, for the extraction of P and T waves, this study divided the 200 samples in two part (the division of points was based on 75 and 125 points which were taken from the left side and right side of the R-peak, respectively). To avoid the misleading of extraction, all samples were collected from 0.2 s to 0.32 s. In this way, using the temporal location and samples, the study extracted the Q, S, P, and T waves from each heartbeat.  
Figure 3: Representation of detected R-peaks in the ECG signal.
## 3 Current Status of the Circuit Component in the MMW Regime
### Low Noise Amplifier (\(\mathrm{LNA}\))
#### 3.1.2 LNA in SiGe Technology  
Figure 4 illustrates the measured minimum noise figure of the published MMW LNAs in SiGe technologies. In [58], the transformers are adopted in the amplifier to alleviate the loss of the passive components in the matching networks at 245 GHz. Therefore, the noise figure can be reduced. In [59], the improvement of the gain and NF are achieved by using the Q-enhanced cascode mechanism with a filter synthesis passband-forming technique. Also, the measured minimum noise figure of the amplifier is 6 dB at 92 GHz. In [60], with the source degeneration inductors and careful modeling of the passive components, the amplifier has a measured minimum NF of 4 dB at 110 GHz. In [61], a T-type matching topology is utilized to achieve the wide bandwidth and minimum noise figure performances.  
Figure 3: Chip photograph of the V-band magnetic coupled triple-cascode LNA with a size of 0.6 \(\times\) 0.48 mm\({}^{2}\) including all testing pads [50].
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 4 Discussion
The third-order bends are not discussed in our study because they are easily influenced by the high contour of tooth morphology, adhesive thickness, and position of bracket height. One limitation of the device was not considering the root information of the facial tooth axis of the clinical crown. Adding the root information in the AR-assisted bracket navigation system might increase the accuracy in the horizontal (mesiodistal) direction. Another limitation was the inadequate fluency and optimization of the system. The system might benefit from hardware and software upgrades, including higher graphics processing unit, high-resolution delicate micro lens, higher internet transfer speed, algorithm optimizing, and intact image extraction technology. Future studies should combine the AR-assisted bracket navigation system with artificial intelligence models for teeth image extraction [27; 28; 29] and use a 5G technology for data transfer speed [30], more efficient graphics processing unit, optimized algorithm, and high-resolution delicate micro lens, thereby increasing the accuracy and reliability. This AR system can have many applications in dentistry, including temporary anchorage devicesnavigation system, root parallel information display, and Bolton analysis [31]. The present study represents just the beginning of AR application in the orthodontic field [32]. Many clinical treatments can use the AR system, which will make treatments safer and more efficient.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 4 Hybrid Algorithm for UAV Path Planning
### Proposed Hybrid Path Planning  
The pseudocode of the proposed HPP algorithm is provided in Algorithms 1 and 2. Algorithm 1 uses the PRM to create a roadmap of the UAV's flight path. First, the starting and goal points of the UWSN are determined. From this \(P_{start}\) position, neighboring path coordinates are randomly selected and listed in \(R\). Subsequently, coordinates that do not collide with the starting point are listed in \(V\). Path coordinates in \(V\) are sorted by the distance on the road to the destination. Nodes that lead the UAVs closer to the destination and are free from obstacles are selected and connected to generate a simple path, which is also the shortest. The process is repeated based on the number of iterations to obtain the complete path from \(P_{start}\) to \(P_{end}\). ```
0:\(P_{start}=\) starting position coordinates (\(X_{s}\),\(Y_{s}\),\(Z_{s}\)) \(P_{end}=\) end-position coordinates. (\(X_{e}\), \(Y_{e}\), \(Z_{e}\)) \(Iter_{max}=\) number of nodes to complete the roadmap
0:Shortest and collision-free path /*Initialize network*/
1:begin
2:V\(\leftarrow\)\(\varnothing\)
3:E\(\leftarrow\)\(\varnothing\)
4:\(i=0\);
5:for(\(i\leq Iter_{max}\))do
6:if (\(R\)\(\leftarrow\) random configuration from \(P_{start}\) in free space) && (\(R\)\(\leftarrow\) collision-free), then
7:V\(\leftarrow\)\(V\)\(\cup\)\(R\)
8:endif
9:repeat;
10:endfor
11:for all \(R\in V\), do
12:\(N_{P_{start}}\)\(\leftarrow\) closest neighbors of \(R\) from \(V\) sorted by increasing distance toward \(P_{end}\) // finds the coordinates for the shortest path
13:for all \(R^{\prime}\in N_{P_{start}}\)do
14:if not connected (\(R\), \(R^{\prime}\)) && not obstacle (\(R-R^{\prime}\)) then // if the points are not connected, and the local planner finds a path between points
15:E\(\leftarrow\)E \(\cup\) (\(R\), \(R^{\prime}\))
16:endif
17:endfor
18:endfor
19:return the generated path
20:end
```  
**Algorithm 1** Roadmap construction based on PRM```
1:Input:
2:\(P_{start}=\) starting position coordinates (\(X_{s}\),\(Y_{s}\),\(Z_{s}\))
3:\(P_{end}=\) end-position coordinates.