esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
###### Abstract  
A specific application of this work is an automated design element segmentation tool for converting complex graphic designs, such as movie posters, into editable layered formats. By leveraging the improved DeepLabv3+ model, the tool can accurately identify and segment text, images, logos, and other layout elements within a raster image (e.g., JPEG/PNG). This enables designers to automatically generate structured, layered files (e.g., PSD) for efficient editing, adaptation to different formats (e.g., social media, print), or trend analysis. The model's reduced computational demands and enhanced accuracy make it suitable for integration into design software, streamlining workflows, and reducing manual effort in reverse-engineering layouts.  
The layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved DeepLabv3++ model. The method reduces the number of model parameters and training time by replacing the backbone network. To improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet, and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model.  
DeepLabv3+; image segmentation; poster layout analysis; deep learning 2020
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 5 Performance Assessment
### Fairness  
On the subject of fairness, the results of the K-S test shown in Table 4 confirm that in none of the priority settings does the parameter p reach the proposed alpha (0.05). This means that 3 GPP LBT is not a fair method in any of these cases, although by means of the parameter Dmax it can be stated that Priority 4 is the closer to fairness than the rest of options.  
Therefore, it can be concluded that the phenomenon that has the most impact, and therefore must be avoided, is the collisions between both technologies. In this manner, the more transmissions there are, the more likely they are to collide, thus it is advisable to make the number of such transmissions as efficient as possible, particularly in the dense scenario that is the subject of this research.  
Although in principle the use of a shorter backoff time is more likely to use the channel, as the standard and the literature suggest [16], it is also more likely in this context to interfere with other transmissions that are going unnoticed. Thus, these results raise the need to adopt specific mechanisms for especially dense environments that allow improving the coexistence between both technologies while maximizing 3 GPP LBT performance.
# Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data
## 3 Experiments and Results
### Comparison of Denoising Model  
In Figure 9, the left panel shows the spectral peaks near 1400 nm, 1900 nm and 2500 nm, caused by water absorption, while the right panel shows the spectra after removing these bands. Here, the results of these two spectra were compared.  
In the research of hyperspectral studies, bands selection can help improve prediction ability and reduce model complexity [25; 26]. For instance, Levenberg-Marquardt back propagation (LMBP) computes the inverse of the Jacobian matrix of the gradient [27] and the normal Bayes classifier needs to compute the inverse matrix of the covariance matrix. The computation of the inverse matrix occupies a large amount of the computation resource and the reduction on the input bands will largely lower the time consumption. The algorithms involving the computation of the inverse matrix or covariance matrix are sensitive to the input dimension and removing those noising bands is time efficient. Moreover, since the mechanism behinds those bands are clear, the accuracy is usually improved if they are abandoned.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Kernel Size Pair** & \(\mathbf{R^{2}}\) \\ \hline \((1\times 1,\,1\times 1)\) & 0.7812 \\ \((3\times 3,\,3\times 3)\) & 0.7477 \\ \((5\times 5,\,5\times 5)\) & 0.7032 \\ \((7\times 7,\,7\times 7)\) & 0.6806 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of kernel selection options in our model (coefficient of determination, \(R^{2}\)) Kernel Selection Comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel Pair** & \(\mathbf{R^{2}}\) & **Elapsed Time (400 Epochs)** \\ \hline \((3,\,3)\) & 0.7812 & 63.5 s \\ \((3,\,32)\) & 0.7812 & 185.5 s \\ \((32,\,32)\) & 0.7157 & 212.7 s \\ \((32,\,64)\) & 0.7171 & 507.3 s \\ \((256,\,32)\) & 0.6169 & 281.0 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of channel selection options in our model (coefficient of determination, \(R^{2}\)).  
In contrast, our results shown in Table 7 indicate that there is no improvement of accuracy when such noisy bands are removed. The only advantage was that it slightly reduced the computation time because the input dimension had been reduced. Professional researchers often select bands based on different mechanisms, which limits the applications of the data. In our capsule network method, this study demonstrated that it is not necessary to remove these bands. In fact, the network automatically lowers the weights if they do not contribute to the estimation. Our algorithm is easy to implement on any device and does not require any band selection procedure.
## 2 Materials and Methods
### Electronic Unit  
To obtain the raw signal associated with the pressures exerted by the subject's body and consequently be able to extract information relating to movement and respiratory activity, it is necessary to acquire all the sensing areas of the PML. Each single sensing element can be electrically represented as a variable resistance. To determine signal variation over time, we designed a specific front-end unit. In particular, to acquire all the 195 sensing areas of the PML, we developed a multi-channel electronic able to sequentially activate each individual detection area through appropriate management of the rows and columns of the pressure matrix.  
The front-end unit, as shown in Figure 3, provides a voltage divider scheme, where _Rsens_ is the variable resistance of the single sensitive element to be determined while R is a pull-down resistor of known value. The resulting \(V_{0}\) voltage is a function of the electrical resistance of the single sensing element. When a pressure is applied on the specific area, the _Rsens_ decreases while the \(V_{0}\) increases accordingly. Then, the \(V_{0}\) is low-pass filtered and connected to the analog input port of the multi-channel electronics based on the Arduino Mega 2560 platform (see Figure 4 ).  
The PML is in fact made up of a matrix of 15 columns, each of which is connected to a digital channel and 13 lines each of which connected to one of the 14 analog channels available in the Arduino Mega. The reading of each single sensor is managed by activating the digital channels sequentially (switching from _GND_ to _VDD_) and simultaneously acquiring all the 13 analog channels (see Figure 4). The acquired PML data that will be elaborated by the algorithms described in Sections 2.4 and 2.5 are expressed as the logical levels of the analog to digital converter of the Arduino board.  
Figure 3: Schematic representation of the electronic front-end developed in which the variable resistor, representing the single sensing elements, is connected to a voltage divider stage, a low-pass filter, and finally the analog port of the Arduino platform.
###### Abstract  
Spatial channel networks (SCNs) and related key technologies have been proposed to increase the capacity and flexibility of optical networks. We define the network resource allocation problem in a static SCN as the routing, modulation format (MF), spatial lane, and spectrum block assignment (RMSSA) problem and try to solve it. In this paper, we derive the relationship between the traffic bit rate, the transmission distance of optical channels, and MFs in SCNs, and obtain the adoption method of MFs. In addition, we introduce conversion nodes (CNs) into SCNs to perform a modulation format conversion (MFC) for more efficient use of network resources. Moreover, the RMSSA problem in static SCNs is modeled, and heuristic spatial lane and spectrum block minimization based on simulated annealing (LBMSA) algorithm is proposed to solve the RMSSA problem. Simulation results show that when the throughput of SCNs is small, the LBMSA algorithm can carry traffic requests with the least amount of network resources and maximize the network resource utilization. When the network throughput is high, the LBMSA algorithm is more inclined to carry all requests rather than efficient transmission. We also show that network resource utilization can be improved with the LBMSA algorithm by setting CNs to perform the MFC.  
spatial channel networks; network resource allocation; modulation format; conversion nodes; modulation format conversion Article  
Routing, Modulation Format, Spatial Lane, and Spectrum Block  
Assignment in Static Spatial Channel Networks. _Appl. Sci._**2023**, 13, 2105. [[https://doi.org/10.3390/app13042105](https://doi.org/10.3390/app13042105)]([https://doi.org/10.3390/app13042105](https://doi.org/10.3390/app13042105))
# A Surrogate Model Based Multi-Objective Optimization Method for Optical Imaging System
### Multi-Objective Optimization (MOO)  
In practical engineering, problems encountered by engineers with multiple objectives are known as multi-objective problems (MOPs), and MOPs with at least four objectives are casually known as many-objective problems (MaOPs) [123]. Multi-objective evolutionary algorithms (MOEAs) are typically applied to solve MOPs, which can be divided into decomposition-based [123, 124, 125, 126], indicator-based [127, 128], and Pareto-based [129, 130, 131] algorithms. However, it should be pointed out that the MOEAs confront three challenges when handling MaOPs, namely, dominance resistance (DR) phenomenon, dimensional curse, and visualization difficulty [132]. To solve the first challenge efficiently, three methods have been introduced, including modification of the Pareto dominance relation, an indicator-based approach, and enhanced diversity management [133].  
Even though these methods can deal with MOPs effectively, there are still high computational burdens. The third approach for MaOPs is to enhance diversity management. For example, the NSGA-II [134] algorithm managed the activation and deactivation of the crowding distance to maintain diversity. As one of the Pareto-based algorithms, NSGA-III [135, 136] achieved great success in practical application, which replaced the crowding distance operator in the NSGA-II with a clustering operator and used a set of well-distributed reference points to guarantee diversity. Although the NSGA-III algorithm can achieve good diversity, its performance needs to be improved by remedying deficiency or expanding application.  
In this paper, the multi-objective algorithm NSGA-III is adopted in the model proposed. NSGA-III has been widely applied to different areas, such as the economic dispatch problem [137] and the ship hull form optimization [138]. This algorithm does not need to convert multiple targets into a single one. It can directly optimize multiple targets at the same time and provide a non-dominated solution set as output. From this solution set, designers can search for the optimal solutions according to their optimization focus and strategy. The multi-objective optimization method proposed in this paper has great potential to be used in the design process of complex high-precision optical systems [135, 136, 15].
## 4 Result and Discussion
### Voltage Profile Results  
To present the impact of the EV charging on the power system voltage profile, the QSTS is carried out and the voltage of all phases at each bus is calculated for 24 h. Figure 16 shows the impact of the EV charging on phase # a at the worst-case bus 114. The voltage profile in case1 and case 2 is lower than the limits of 0.95 p.u due to the EV charging. However, using the DSM with wind power generation increases the voltage to be within the limits as presented in case 3.
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 2 System Components and Operating Principles  
A rapid and precise zoom system consists of a zoom optical system, a VCM drive structure, and a high-precision positioning control system, as shown in Figure 1, which was constructed using the structural design software Pro/ENGNER 5.0. The zoom optical system achieves optical zoom, focal plane stabilization, aberration correction, and focusing through the relative movement of multiple lens groups, ensuring high image quality across the entire zoom range. The rapid zoom function is implemented by changing the drive mechanism without altering the original optical design.  
The VCM drive structure, shown in Figure 2, is the core component in achieving rapid zoom. It primarily consists of a permanent magnet, an energized coil, and a magnetic yoke. The design of the VCM considers that it should provide sufficient peak driving force within the size constraints to meet the requirements for rapid zoom. The permanent magnet generates a stable magnetic field. The magnetic yoke concentrates and increases the magnetic flux and also provides the necessary structural support to ensure the stability of the magnets and coil. The zoom lens group is fixed on the energized coil, moving together as a unit. Based on the Lorentz force law, the system adjusts the current magnitude and direction in the coil within the magnetic circuit, driving the coil and the zoom lens group to perform variable-speed reciprocating linear motions along the optical axis. This design significantly enhances the zoom speed compared to traditional transmission mechanisms. The high-precision positioning control system must meet the requirements for the axial positioning accuracy of the lens group to ensure that positioning errors do not affect the image quality.  
The high-precision positioning control system ensures that the zoom lens group accurately moves to the designated position, achieving precise focal length changes during rapid zooming. As shown in Figure 3, this system mainly comprises a position-sensing element, a magnetic strip, and a control circuit. The position-sensing element is fixed to the zoom lens group and these move together, while the magnetic strip is fixed inside the lens and remains stationary. During zooming, the energized coil drives the zoom lens group and the position-sensing element to move relative to the fixed magnetic strip. The motion position-sensing element records the periodic changes in the magnetic field of the fixed magnetic strip, thereby providing real-time feedback on the zoom lens group's movement.  
Figure 1: Rapid and precise zoom lens components.  
Figure 2: Schematic of the VCM structure with a permanent magnet, magnetic yoke, and energized coil.  
Through the feedback control circuit, the system achieves real-time closed-loop control, ensuring the precise positioning of the zoom lens group. The high-precision positioning control system must meet the requirements for the axial positioning accuracy of the lens group to ensure that positioning errors do not affect the image quality.
## 2 Materials and Methods  
The redox activity of the synthesized mixed metal oxides was analyzed through simultaneous thermogravimetric analysis and differential scanning calorimetry (TGA/DSC, Netzsch, Selb, Germany). The materials, in the form of fine powders, were transferred to a ceramic crucible for thermal analysis. They were then heated (at 10 \({}^{\circ}\)C/min), under 20% O\({}_{2}\)/Ar (10 NmL/min for O\({}_{2}\) and 40 NmL/min for Ar) or Ar (40 NmL/min), to perform the reduction step (endothermal), also called the heat charging step. Afterwards, the cycle was completed by cooling down the temperature (at 10\({}^{\circ}\)C/min) and performing the heat discharging step through the material re-oxidation (exothermal) to its original stoichiometry (using the same 20% O\({}_{2}\)/Ar mixture), to confirm the reaction reversibility. The same procedure was then repeated in the next cycles (total of 3 cycles).  
The oxygen storage capacity (OSC) of the material was measured, corresponding to the mass of oxygen lost and recovered during these two steps (in wt% of the total mass of metal oxide). The onset temperature for both steps was measured using Netzsch Proteus software tool. A difference in temperature between the reduction and re-oxidation was observed due to kinetic limitations and was depicted by the presence of a hysteresis phenomenon. This gap in temperature gains from being reduced, as it will then require less sensible energy to perform charge/discharge cycles if the variation in temperature between the two steps is minimized. Thanks to the combined DSC, the heat flow absorbed and released by the sample was also recorded and quantified in kJ/kg (\(\Delta\)H) via the linear integration of endothermic (charge) and exothermic (discharge) reaction peaks. Experimental data concerning oxygen storage capacity of redox materials, transition temperatures during redox process, and reaction enthalpies were averaged over the course of three successive reduction/oxidation cycles for each material.  
The hereafter presented experimental data were obtained using metal oxides synthesized via a modified Pechini method [43] from the corresponding metal nitrates (>98% purity) mixed with citric acid and ethylene glycol (>99% purity) in aqueous solution. The solution was heated up until obtaining a viscous solution. The powders were then calcined in air at 200 \({}^{\circ}\)C for 2 h and at 750 \({}^{\circ}\)C for 4 h in order to eliminate the organics and residues of the synthesis, and to stabilize the structure. Crystalline phases were analyzed, before and after cycling, with X-ray diffraction (XRD) using a PANanalytical XPert Pro diffractometer, Malvern Panalytical, UK (CuK\(\alpha\) radiation, \(\lambda\) = 0.15418 nm). Detailed synthesis method and specific properties analysis can be found in previous works [35; 36]. Throughout the study, the amount of secondary metal (M') added to the main metal (M) is referred to as x(M') (content of M' in molar fraction), corresponding to the molar ratio on metal basis, n(M')/((n(M) + n(M')).
## 2 System Model and Problem Formulation
### Problem Formulation  
We define a binary variable \(b_{i,t}^{m,k}\in\{0,1\}\) that represents the transmitting decision of UE \(i\), i.e., \(b_{i,t}^{m,k}=1\) represents UE \(i\) transmitting signals with power \(P_{k}\) on the band \(m\) at time slot \(t\), while \(b_{i,t}^{m,k}=0\) represents UE \(i\) not transmitting signals with power \(P_{k}\) on the band \(m\) at time slot \(t\). UEs can only select one transmit power on the band \(m\) at each time slot, which can be expressed as  
\[\text{C}_{1}:\ \sum_{k\in\mathcal{K}}b_{i,t}^{m,k}=1 \tag{10}\]  
At time slot \(t\), the transmit power of UE \(i\) on the band \(m\) can be expressed as  
\[P_{i,t}^{m}=\sum_{k\in\mathcal{K}}b_{i,t}^{m,k}P_{k} \tag{11}\]  
We also define a binary variable \(c_{n,t}\in\{0,1\}\) that represents the beam-hopping scheduling decision of the LEO satellite, i.e., \(c_{n,t}=1\) represents the beam area \(n\) illuminated at time slot \(t\), while \(c_{n,t}=0\) represents the beam area \(n\) not illuminated at time slot \(t\). The satellite illuminates a certain number of beams at time slot \(t\) to receive UE data. Additionally, only \(N_{0}\) beam areas can be illuminated at each time slot due to the limited satellite payload resources, which can be expressed as  
\[\text{C}_{2}:\ \sum_{n\in\mathcal{N}}c_{n,t}=N_{0} \tag{12}\]  
The uplink communication rate of UE \(i\) can be expressed as \[R_{i}=\sum\limits_{t\in\mathcal{T}}\sum\limits_{m\in\mathcal{M}}\frac{B}{M}\text{ log}_{2}\left(1+\frac{\sum\limits_{n\in\mathcal{N}}h_{i,n}c_{n,t}\sum\limits_{k\in \mathcal{K}}b_{i,t}^{m,k}P_{k}}{\sum\limits_{t^{\prime}\in\mathcal{I},t^{\prime }\
eq i}\sum\limits_{n\in\mathcal{N}}h_{i,n}c_{n,t}\sum\limits_{k\in\mathcal{K} }b_{i^{\prime},t}^{m,k}P_{k}+\sigma^{2}}\right) \tag{13}\]  
where \(\sigma^{2}\) represents the noise power of the LEO satellite channel. In order to meet the communication requirements of NUEs, there is a rate constraint for all NUEs, which can be expressed as  
\[\text{C}_{3}:\ R_{i}\geq D,i\in\mathcal{I}\backslash\mathcal{J} \tag{14}\]  
where \(D\) denotes the rate threshold for NUEs. At the same time, we consider the worst case scenario, where the eavesdropping GEO satellite eavesdrops on all transmission bands. Based on the above discussion about covert communication, the KL-divergence in (9) can be calculated by  
\[\mathcal{D}(\mathbb{P}_{1,m}||\mathbb{P}_{0,m})=\left[\frac{\sum\limits_{j\in \mathcal{J}}\sum\limits_{k\in\mathcal{K}}h_{e,j}b_{i,t}^{m,k}P_{k}}{\sum \limits_{i\in\mathcal{I},t\
otin\mathcal{J}}\sum\limits_{k\in\mathcal{K}}h_{ e,j}b_{i,t}^{m,k}P_{k}+\sigma_{e}^{2}}-\ln\left(1+\frac{\sum\limits_{j\in \mathcal{J}}\sum\limits_{k\in\mathcal{K}}h_{e,j}b_{i,t}^{m,k}P_{k}}{\sum \limits_{i\in\mathcal{I},t\
otin\mathcal{J}}\sum\limits_{k\in\mathcal{K}}h_{ e,j}b_{i,t}^{m,k}P_{k}+\sigma_{e}^{2}}\right)\right] \tag{15}\]  
where \(\sigma_{e}^{2}\) represents the noise power of the eavesdropping GEO satellite channel. Furthermore, according to the [PERSON] expansion, when \(x\geq 0\), there is always \(x-\ln x\leq x^{2}/2\).
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
###### Abstract  
The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.  
wireless sensor networks; energy harvesting; mobile computing; supercapacitors +
Footnote †: journal: Sensors